Extension session 6/18/2026 02:11 AM
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- 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Gener1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files Welcome to Introduction to In-context Learning.difficulty 3/5 — confidence 2/5
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- After watching this video, you'll be able to describe in-context learnAfter watching this video, you'll be able to describe in-context learning.difficulty 3/5 — confidence 2/5
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- You will also be able to explain the fundamentals of prompt engineerinYou will also be able to explain the fundamentals of prompt engineering.difficulty 3/5 — confidence 2/5
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- In-context learning is a specific method of prompt engineering where dIn-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language.difficulty 3/5 — confidence 2/5
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- However, in-context learning doesn’t require additional training.However, in-context learning doesn’t require additional training.difficulty 3/5 — confidence 2/5
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- A new task is learned from a small set of examples presented within thA new task is learned from a small set of examples presented within the context or prompt at inference time.difficulty 3/5 — confidence 2/5
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- Let's understand some advantages and disadvantages of in-context learnLet's understand some advantages and disadvantages of in-context learning.difficulty 3/5 — confidence 2/5
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- 46 It eliminates the need for continual fine-tuning, allowing the mo:46 It eliminates the need for continual fine-tuning, allowing the model to adapt and learn within its context.difficulty 3/5 — confidence 2/5
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- Here is an example of a prompt given to GPT 3.5.Here is an example of a prompt given to GPT 3.5.difficulty 3/5 — confidence 2/5
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- The wind is This simple prompt leads to a poetic response.The wind is This simple prompt leads to a poetic response.difficulty 3/5 — confidence 2/5
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- Benefits include modularity, extensibility, decomposition capabilitiesBenefits include modularity, extensibility, decomposition capabilities, and easy integration with vector databases.difficulty 3/5 — confidence 2/5
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- Several practical applications include deciphering complex legal documSeveral practical applications include deciphering complex legal documents, extracting key statistics from reports, customer support, and automating routine writing tasks.difficulty 3/5 — confidence 2/5
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- LangChain can be used with other data types by using external librariLangChain can be used with other data types by using external libraries and models.difficulty 3/5 — confidence 2/5
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- Press [CTRL + S] to save as a note And graded assessments prove whatPress [CTRL + S] to save as a note And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers.difficulty 3/5 — confidence 2/5
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- We’re here to support your success, and we’re excited that you’re hereWe’re here to support your success, and we’re excited that you’re here.difficulty 3/5 — confidence 2/5
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- And graded assessments prove what you’ve learned, leading to a shareab: And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers.difficulty 3/5 — confidence 2/5
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- A comprehensive security program must contain confidentiality, integriA comprehensive security program must contain confidentiality, integrity, and availability.difficulty 3/5 — confidence 2/5
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- Confidentiality means that data is protected from unauthorized access.Confidentiality means that data is protected from unauthorized access.difficulty 3/5 — confidence 2/5
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- Integrity means that data is protected from unauthorized changes.Integrity means that data is protected from unauthorized changes.difficulty 3/5 — confidence 2/5
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- And, availability means you have access to your data whenever you needAnd, availability means you have access to your data whenever you need it.difficulty 3/5 — confidence 2/5
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- When confidential data is exposed beyond the intended audience, it cauWhen confidential data is exposed beyond the intended audience, it causes risk.difficulty 3/5 — confidence 2/5
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- 54 Confidential information is kept secret to prevent identity theft:54 Confidential information is kept secret to prevent identity theft, compromised accounts and systems, legal concerns, damage to reputation, and other severe consequences.difficulty 3/5 — confidence 2/5
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- To determine if data should be confidential, ask: Who is authorized?To determine if data should be confidential, ask: Who is authorized?difficulty 3/5 — confidence 2/5
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- Are there conditions for when data can be accessed?Are there conditions for when data can be accessed?difficulty 3/5 — confidence 2/5
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- What would the impact of disclosure be?What would the impact of disclosure be?difficulty 3/5 — confidence 2/5
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- Cybercriminals are always after sensitive information or personal dataCybercriminals are always after sensitive information or personal data.difficulty 3/5 — confidence 2/5
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- Course Overview Welcome to the Cybersecurity Essentials for Everyone cCourse Overview Welcome to the Cybersecurity Essentials for Everyone course.difficulty 3/5 — confidence 2/5
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- This course has been thoughtfully designed to introduce you to the corThis course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world.difficulty 3/5 — confidence 2/5
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- Throughout the course, you'll gain practical knowledge of key cybersecThroughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats.difficulty 3/5 — confidence 2/5
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- Additionally, you’ll develop the ability to troubleshoot common securiAdditionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment.difficulty 3/5 — confidence 2/5
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- To support your learning, each module includes hands-on labs that simuTo support your learning, each module includes hands-on labs that simulate real-world scenarios.difficulty 3/5 — confidence 2/5
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- These culminate in a final practical project that allows you to demonsThese culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way.difficulty 3/5 — confidence 2/5
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- No prior experience in cybersecurity is needed.No prior experience in cybersecurity is needed.difficulty 3/5 — confidence 2/5
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- This course is designed for beginners and serves as an excellent startThis course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field.difficulty 3/5 — confidence 2/5
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- After completing this course, you will be able to: Articulate the fundAfter completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape.difficulty 3/5 — confidence 2/5
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- Identify and describe common cyber threats such as malware, phishingIdentify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them.difficulty 3/5 — confidence 2/5
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- They may be set by us or by third party providers whose services we haThey may be set by us or by third party providers whose services we have added to our pages.difficulty 3/5 — confidence 2/5
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- If you do not allow these cookies then some or all of these services mIf you do not allow these cookies then some or all of these services may not function properly.Marketing CookiesAlways ActiveThese cookies may be set through our site by our advertising partners.difficulty 3/5 — confidence 2/5
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- They may be used by those companies to build a profile of your interesThey may be used by those companies to build a profile of your interests and show you relevant adverts on other sites.difficulty 3/5 — confidence 2/5
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- They are based on uniquely identifying your browser and internet devicThey are based on uniquely identifying your browser and internet device.difficulty 3/5 — confidence 2/5
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- If you do not allow these cookies, you will experience less targeted aIf you do not allow these cookies, you will experience less targeted advertising.difficulty 3/5 — confidence 2/5
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- Information is a summary of the raw data.Information is a summary of the raw data.difficulty 3/5 — confidence 2/5
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- For example, positive or negative results that happen after some speciFor example, positive or negative results that happen after some specific change.difficulty 3/5 — confidence 2/5
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- And, insights are conclusions based on the results of information analAnd, insights are conclusions based on the results of information analysis.difficulty 3/5 — confidence 2/5
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- Meaningful business decisions are based on insights.Meaningful business decisions are based on insights.difficulty 3/5 — confidence 2/5
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- For example, If a positive trend occurs after store hours are changedFor example, If a positive trend occurs after store hours are changed, the right business decision would be to maintain those new hours.difficulty 3/5 — confidence 2/5
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- Intellectual property (or IP) refers to creations of the mind and geneIntellectual property (or IP) refers to creations of the mind and generally are not tangible.difficulty 3/5 — confidence 2/5
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- It's often protected by copyright, trademark, and patent law.It's often protected by copyright, trademark, and patent law.difficulty 3/5 — confidence 2/5
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- Industrial designs, trade secrets, and research discoveries are allIndustrial designs, trade secrets, and research discoveries are all examples of IP.difficulty 3/5 — confidence 2/5
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- Even some employee knowledge is considered intellectual property.Even some employee knowledge is considered intellectual property.difficulty 3/5 — confidence 2/5
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- Companies use a legally binding document called a Non-Disclosure AgreCompanies use a legally binding document called a Non-Disclosure Agreement (or an NDA) to prevent the sharing of sensitive information.difficulty 3/5 — confidence 2/5
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- Willkommen bei „Sicherheit und Datenschutz“.Willkommen bei „Sicherheit und Datenschutz“.difficulty 3/5 — confidence 2/5
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- Ein Informationsgut sind Informationen oder Daten, die von Wert sind.Ein Informationsgut sind Informationen oder Daten, die von Wert sind.difficulty 3/5 — confidence 2/5
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- Beispiele hierfür sind Patientenakten, Kundeninformationen und geistigBeispiele hierfür sind Patientenakten, Kundeninformationen und geistiges Eigentum.difficulty 3/5 — confidence 2/5
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- Informationsbestände können physisch, auf Papier , Festplatten oder anInformationsbestände können physisch, auf Papier , Festplatten oder anderen Medien oder elektronisch in Datenbanken und Dateien vorhanden sein.difficulty 3/5 — confidence 2/5
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- Bei der Datenanalyse werden Rohdaten wie Werte oder Fakten verwendetBei der Datenanalyse werden Rohdaten wie Werte oder Fakten verwendet, um aussagekräftige Informationen zu erstellen.difficulty 3/5 — confidence 2/5
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- Daten sind Rohwerte und Fakten, die normalerweise von automatisiertenDaten sind Rohwerte und Fakten, die normalerweise von automatisierten Systemen gesammelt werden.difficulty 3/5 — confidence 2/5
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- Zum Beispiel Seitenbesuche, Linkklicks, monatliche Verkäufe.Zum Beispiel Seitenbesuche, Linkklicks, monatliche Verkäufe.difficulty 3/5 — confidence 2/5
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- Informationen sind eine Zusammenfassung der Rohdaten.Informationen sind eine Zusammenfassung der Rohdaten.difficulty 3/5 — confidence 2/5
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- Zum Beispiel positive oder negative Ergebnisse, die nach einer bestimmZum Beispiel positive oder negative Ergebnisse, die nach einer bestimmten Änderung auftreten.difficulty 3/5 — confidence 2/5
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- Erkenntnisse sind Schlussfolgerungen, die auf den Ergebnissen der InfoErkenntnisse sind Schlussfolgerungen, die auf den Ergebnissen der Informationsanalyse basieren.difficulty 3/5 — confidence 2/5
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- Module 1 Common Security Threats and Risks Module 2 Security Best PracModule 1 Common Security Threats and Risks Module 2 Security Best Practices Module 3 Safe Browsing Practices Module 4 Final Exam and Project Hands-on Lab: Windows Update Course Introduction Video .difficulty 3/5 — confidence 2/5
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- Duration: 3 minutes 3 min Course Overview Reading .Duration: 3 minutes 3 min Course Overview Reading .difficulty 3/5 — confidence 2/5
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- Duration: 10 minutes 10 min Confidentiality, Integrity, and AvailabiliDuration: 10 minutes 10 min Confidentiality, Integrity, and Availability Video .difficulty 3/5 — confidence 2/5
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- Duration: 5 minutes 5 min Security and Information Privacy Video .Duration: 5 minutes 5 min Security and Information Privacy Video .difficulty 3/5 — confidence 2/5
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- Duration: 8 minutes 8 min Activity: Exploring Information Privacy UngrDuration: 8 minutes 8 min Activity: Exploring Information Privacy Ungraded Plugin .difficulty 3/5 — confidence 2/5
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- Duration: 5 minutes 5 min Intellectual Property and Types of ConfidentDuration: 5 minutes 5 min Intellectual Property and Types of Confidential Information Reading .difficulty 3/5 — confidence 2/5
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- Duration: 5 minutes 5 min Microsoft Windows Server Lab Environment VidDuration: 5 minutes 5 min Microsoft Windows Server Lab Environment Video .difficulty 3/5 — confidence 2/5
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- Duration: 2 minutes 2 min Threats and Breaches Video .Duration: 2 minutes 2 min Threats and Breaches Video .difficulty 3/5 — confidence 2/5
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- Duration: 7 minutes 7 min Hands-on Lab: Windows Update Ungraded App ItDuration: 7 minutes 7 min Hands-on Lab: Windows Update Ungraded App Item .difficulty 3/5 — confidence 2/5
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- Duration: 45 minutes 45 min Threat Types Video .Duration: 45 minutes 45 min Threat Types Video .difficulty 3/5 — confidence 2/5
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- This next capability I'm going to talk about is really one that you caThis next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or computer scientist.difficulty 3/5 — confidence 2/5
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- But you can use it in conjunction with somebody else who is really gooBut you can use it in conjunction with somebody else who is really good at those things if you aren't a programmer or computer scientist.difficulty 3/5 — confidence 2/5
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- If you're an organization that has a bunch of programmers or you haveIf you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that.difficulty 3/5 — confidence 2/5
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- It's a very exciting one from the perspective of software development.It's a very exciting one from the perspective of software development.difficulty 3/5 — confidence 2/5
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- One of the challenges that we have is we have all these little tools tOne of the challenges that we have is we have all these little tools that we would like to have to help us out.difficulty 3/5 — confidence 2/5
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- All day long when I'm working and thinking it'd be really nice to haveAll day long when I'm working and thinking it'd be really nice to have a piece of software that did this and simplified this process for me.difficulty 3/5 — confidence 2/5
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- Sometimes as a software engineer, I'll go and take the time to actuallSometimes as a software engineer, I'll go and take the time to actually write the software to do that.difficulty 3/5 — confidence 2/5
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- The Code Interpreter creates an intriguing new possibility where we caThe Code Interpreter creates an intriguing new possibility where we can actually turn a conversation with Code Interpreter into software.difficulty 3/5 — confidence 2/5
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- It could delete all the files for all you know, if you can't read theIt could delete all the files for all you know, if you can't read the code.difficulty 3/5 — confidence 2/5
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- We 100% know that large language models can make mistakes, and you neeWe 100% know that large language models can make mistakes, and you need to pay attention to the code that comes out of what I'm going to show you, and if you can't read it and understand it, you should not proceed.difficulty 3/5 — confidence 2/5
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- I want to help you gauge the difficulty of a task that you're about toI want to help you gauge the difficulty of a task that you're about to start with Code Interpreter.difficulty 3/5 — confidence 2/5
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- Now, the reason for this is when you get started, you're going to haveNow, the reason for this is when you get started, you're going to have all ideas of things that you can go and try.difficulty 3/5 — confidence 2/5
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- But I want you to have a way of understanding how difficult each of yoBut I want you to have a way of understanding how difficult each of your ideas are going to be to accomplish.difficulty 3/5 — confidence 2/5
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- If you understand two basic things that we're going to talk about, youIf you understand two basic things that we're going to talk about, you'll be much better off in terms of gauging the difficulty of accomplishing a particular task.difficulty 3/5 — confidence 2/5
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- Those two things are, if you want to know how difficult a particular tThose two things are, if you want to know how difficult a particular task is going to be, we want to go and look at the document or data that we're going to work with.difficulty 3/5 — confidence 2/5
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- The first question we want to ask is how structured or unstructured isThe first question we want to ask is how structured or unstructured is that data?difficulty 3/5 — confidence 2/5
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- Structured data is something like a table.Structured data is something like a table.difficulty 3/5 — confidence 2/5
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- If you've got a bunch of tables that you're trying to read through andIf you've got a bunch of tables that you're trying to read through and it's all in Excel format, that's a structured format.difficulty 3/5 — confidence 2/5
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- This is an example here where I'm uploading a CSV file, that is a struThis is an example here where I'm uploading a CSV file, that is a structured format.difficulty 3/5 — confidence 2/5
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- It's some very clearly marked format where you know what all the partsIt's some very clearly marked format where you know what all the parts are.difficulty 3/5 — confidence 2/5
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- We can always go and upload individual files and download individual fWe can always go and upload individual files and download individual files to code interpreter, and we can certainly get by that way.difficulty 3/5 — confidence 2/5
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- It also allows us to do interesting things, like when you have a zip fIt also allows us to do interesting things, like when you have a zip file, it can actually have a folder hierarchy inside of it.difficulty 3/5 — confidence 2/5
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- Or we can give additional things or tools or other possibilities, bitsOr we can give additional things or tools or other possibilities, bits of python code, anything we want to do to code interpreter.difficulty 3/5 — confidence 2/5
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- But I'm going to give you a simple example of why an archive is so helBut I'm going to give you a simple example of why an archive is so helpful.difficulty 3/5 — confidence 2/5
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- Let's imagine that you have a series of images, and you want to go andLet's imagine that you have a series of images, and you want to go and apply a transformation to them.difficulty 3/5 — confidence 2/5
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- You want to make them much more stylized, apply some filter to them.You want to make them much more stylized, apply some filter to them.difficulty 3/5 — confidence 2/5
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- So, I'm going to upload an archive full of images to code interpreter.So, I'm going to upload an archive full of images to code interpreter.difficulty 3/5 — confidence 2/5
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- But this could be an archive of any of your files.But this could be an archive of any of your files.difficulty 3/5 — confidence 2/5
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- It could be an archive full of Excel files that you want to combine.It could be an archive full of Excel files that you want to combine.difficulty 3/5 — confidence 2/5
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- It might be an archive full of Excel files, and you want a specific viIt might be an archive full of Excel files, and you want a specific visualization built for every single individual Excel file.difficulty 3/5 — confidence 2/5
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- Code Interpreter is really useful for working with media.Code Interpreter is really useful for working with media.difficulty 3/5 — confidence 2/5
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- If you have videos, if you have audio files, if you have collections oIf you have videos, if you have audio files, if you have collections of images, working with all of them, you can do a lot of things really quickly.difficulty 3/5 — confidence 2/5
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- So I'm going to give you an example of this.So I'm going to give you an example of this.difficulty 3/5 — confidence 2/5
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- He loves to go and do BMX racing and do dirt jumps and all kinds of inHe loves to go and do BMX racing and do dirt jumps and all kinds of interesting things.difficulty 3/5 — confidence 2/5
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- I've uploaded this video and I'm going to do a simple extraction.I've uploaded this video and I'm going to do a simple extraction.difficulty 3/5 — confidence 2/5
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- I'm going to take 10 frames out of this video, and then I'm going to dI'm going to take 10 frames out of this video, and then I'm going to do some things with them.difficulty 3/5 — confidence 2/5
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- You can go and play around with media and do all kinds of interestingYou can go and play around with media and do all kinds of interesting things.difficulty 3/5 — confidence 2/5
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- I encourage you, after you do this, take an image, upload it, and tryI encourage you, after you do this, take an image, upload it, and try experimenting with doing different operations on the image.difficulty 3/5 — confidence 2/5
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- I'm just going to go and say extract 10 frames from this video evenlyI'm just going to go and say extract 10 frames from this video evenly spaced apart.difficulty 3/5 — confidence 2/5
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- This is something I would probably have to go and look up some commandThis is something I would probably have to go and look up some command line tool.difficulty 3/5 — confidence 2/5
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- When your agent answers the wrong question or repeats a task, it's temWhen your agent answers the wrong question or repeats a task, it's tempting to fix the output.difficulty 3/5 — confidence 2/5
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- But to build intelligent systems, you have to debug the behavior, notBut to build intelligent systems, you have to debug the behavior, not just the result.difficulty 3/5 — confidence 2/5
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- Let's look at how to break down what your agent did, why it did it, anLet's look at how to break down what your agent did, why it did it, and how to fix it.difficulty 3/5 — confidence 2/5
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- why debugging agents means analyzing behavior, not just fixing brokenwhy debugging agents means analyzing behavior, not just fixing broken outputs.difficulty 3/5 — confidence 2/5
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- What kinds of bugs emerge from decision loops, memory use and tool logWhat kinds of bugs emerge from decision loops, memory use and tool logic?difficulty 3/5 — confidence 2/5
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- Key questions to ask when tracing agent reasoning and response failureKey questions to ask when tracing agent reasoning and response failures.difficulty 3/5 — confidence 2/5
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- How to design tests that evaluate reasoning chains, fallback handlingHow to design tests that evaluate reasoning chains, fallback handling and memory updates.difficulty 3/5 — confidence 2/5
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- How to use logs and traces to identify where and why agent behavior brHow to use logs and traces to identify where and why agent behavior breaks down.difficulty 3/5 — confidence 2/5
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- You need to inspect not just what the agent said, but what it believedYou need to inspect not just what the agent said, but what it believed was true at the time.difficulty 3/5 — confidence 2/5
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- When debugging an agent, ask yourself, did it perceive the input correWhen debugging an agent, ask yourself, did it perceive the input correctly?difficulty 3/5 — confidence 2/5
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- Congratulations, you've completed Building AI Agents for Complex TasksCongratulations, you've completed Building AI Agents for Complex Tasks.difficulty 3/5 — confidence 2/5
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- This course wasn't just about crafting clever bots.This course wasn't just about crafting clever bots.difficulty 3/5 — confidence 2/5
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- It was about understanding how to build intelligent systems, agents thIt was about understanding how to build intelligent systems, agents that perceive, plan, and act in complex real-world environments.difficulty 3/5 — confidence 2/5
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- Let's take a moment to reflect on what you've accomplished.Let's take a moment to reflect on what you've accomplished.difficulty 3/5 — confidence 2/5
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- Agent Architecture Fundamentals You explored the core types of agent dAgent Architecture Fundamentals You explored the core types of agent design – reactive, deliberative, and hybrid – and learned how they behave under dynamic conditions.difficulty 3/5 — confidence 2/5
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- Perception and Decision Logic You saw why successful agents need strucPerception and Decision Logic You saw why successful agents need structured decision-making and planning – not just responses, but reasoning chains that adapt in real-time.difficulty 3/5 — confidence 2/5
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- Hands-on Frameworks and Builds You built and tested real agents usingHands-on Frameworks and Builds You built and tested real agents using tools like LangChain and Rasa, integrating memory, tools, and multi-step logic into workflows that execute autonomously.difficulty 3/5 — confidence 2/5
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- Failure Evaluation and Debugging You practiced analyzing agent logs, sFailure Evaluation and Debugging You practiced analyzing agent logs, spotting failure points, and applying fixes that improved reliability, context retention, and goal completion.difficulty 3/5 — confidence 2/5
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- Designing for Adaptability You learned that building intelligent agentDesigning for Adaptability You learned that building intelligent agents isn't just about getting the right answer.difficulty 3/5 — confidence 2/5
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- It's about designing systems that improve, recover, and adapt under prIt's about designing systems that improve, recover, and adapt under pressure.difficulty 3/5 — confidence 2/5
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- Project: Design and Deploy a Real-World AI Agent What to expect This iProject: Design and Deploy a Real-World AI Agent What to expect This is a practice assignment to help you check your understanding.difficulty 3/5 — confidence 2/5
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- It doesn’t count toward your course grade.It doesn’t count toward your course grade.difficulty 3/5 — confidence 2/5
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- Imagine an agent that doesn't just act, but learns from each action toImagine an agent that doesn't just act, but learns from each action to refine its future decisions.difficulty 3/5 — confidence 2/5
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- That's what systems like Baby AGI and AlphaCode demonstrate.That's what systems like Baby AGI and AlphaCode demonstrate.difficulty 3/5 — confidence 2/5
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- Feedback loops that drive intelligent behavior.Feedback loops that drive intelligent behavior.difficulty 3/5 — confidence 2/5
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- Baby AGI is an open-source agent that simulates autonomous goal pursuiBaby AGI is an open-source agent that simulates autonomous goal pursuit.difficulty 3/5 — confidence 2/5
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- You give it a high-level objective, like grow a Twitter following, andYou give it a high-level objective, like grow a Twitter following, and it breaks it into tasks, ranks them, and executes them one at a time.difficulty 3/5 — confidence 2/5
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- After each task, it evaluates the result, reprioritizes, and adjusts tAfter each task, it evaluates the result, reprioritizes, and adjusts the task list.difficulty 3/5 — confidence 2/5
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- By the end of this video, you will be able to describe how Baby AGI usBy the end of this video, you will be able to describe how Baby AGI uses task loops and prioritization to simulate autonomous goal pursuit.difficulty 3/5 — confidence 2/5
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- Explain how AlphaCode leverages trial and selection to solve complex cExplain how AlphaCode leverages trial and selection to solve complex coding problems.difficulty 3/5 — confidence 2/5
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- Identify the role of feedback and iteration in improving agent behavioIdentify the role of feedback and iteration in improving agent behavior.difficulty 3/5 — confidence 2/5
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- Differentiate between static automation and adaptive intelligence in aDifferentiate between static automation and adaptive intelligence in agent design.difficulty 3/5 — confidence 2/5
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- And suddenly, what worked in a controlled test breaks in production.And suddenly, what worked in a controlled test breaks in production.difficulty 3/5 — confidence 2/5
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- This reading explores the common edge cases and failure modes that affThis reading explores the common edge cases and failure modes that affect intelligent agents.difficulty 3/5 — confidence 2/5
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- You'll look at how agents can fall into infinite loops, forget past coYou'll look at how agents can fall into infinite loops, forget past context, over-trigger fallback behavior, or misfire on tool selection.difficulty 3/5 — confidence 2/5
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- You'll also see how poorly managed memory and misaligned decision logiYou'll also see how poorly managed memory and misaligned decision logic can lead to behaviors that technically work—but feel unintelligent or broken to the user.difficulty 3/5 — confidence 2/5
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- By walking through real-world examples and code snippets, you'll beginBy walking through real-world examples and code snippets, you'll begin to recognize not just how agents fail—but why they fail, and how you can design against these pitfalls from the start.difficulty 3/5 — confidence 2/5
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- Duration: 6 minutes 6 min Introduction and Welcome Video .Duration: 6 minutes 6 min Introduction and Welcome Video .difficulty 3/5 — confidence 2/5
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- Duration: 2 minutes 2 min How AI Agents Perceive the World Around ThemDuration: 2 minutes 2 min How AI Agents Perceive the World Around Them Video .difficulty 3/5 — confidence 2/5
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- Deliberative Agents in the Real World Video .Deliberative Agents in the Real World Video .difficulty 3/5 — confidence 2/5
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- AutoGPT – Choosing the Right Agent Model Video .AutoGPT – Choosing the Right Agent Model Video .difficulty 3/5 — confidence 2/5
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- Duration: 4 minutes 4 min HOL: Practice Classifying Agent Types in ReaDuration: 4 minutes 4 min HOL: Practice Classifying Agent Types in Real-World Use Cases Practice Assignment .difficulty 3/5 — confidence 2/5
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- Duration: 10 minutes 10 min Justify Your Agent Classifications DialoguDuration: 10 minutes 10 min Justify Your Agent Classifications Dialogue .difficulty 3/5 — confidence 2/5
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- These cookies are automatically enabled and cannot be turned off becauThese cookies are automatically enabled and cannot be turned off because they are required for the Site to function properly.difficulty 3/5 — confidence 2/5
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- These cookies allow us to understand how visitors use the Site to enhaThese cookies allow us to understand how visitors use the Site to enhance the content, quality, and features of the Site and the services.difficulty 3/5 — confidence 2/5
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- For example, these cookies allow us to recognize and count the numberFor example, these cookies allow us to recognize and count the number of visitors and understand how visitors move around the Site when using it.difficulty 3/5 — confidence 2/5
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- These cookies enable the website to provide enhanced functionality andThese cookies enable the website to provide enhanced functionality and personalization.difficulty 3/5 — confidence 2/5
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- Picture this, you're building an AI customer service agent.Picture this, you're building an AI customer service agent.difficulty 3/5 — confidence 2/5
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- It needs to access your CRM, check inventory levels, process paymentsIt needs to access your CRM, check inventory levels, process payments, and update support tickets.difficulty 3/5 — confidence 2/5
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- With traditional approaches, you'd need to build four different connecWith traditional approaches, you'd need to build four different connectors, each with its own authentication, data format, and error handling.difficulty 3/5 — confidence 2/5
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- Luis, a full-stack and AI engineer, lived this nightmare while consultLuis, a full-stack and AI engineer, lived this nightmare while consulting for a retail company.difficulty 3/5 — confidence 2/5
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- Their AI recommendation engine needed data from 12 different systemsTheir AI recommendation engine needed data from 12 different systems, customer data, inventory, pricing, reviews, shipping, and more.difficulty 3/5 — confidence 2/5
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- The engineering team spent 60% of their time maintaining connectors inThe engineering team spent 60% of their time maintaining connectors instead of improving the AI.difficulty 3/5 — confidence 2/5
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- Then their biggest competitor launched a similar system in half the tiThen their biggest competitor launched a similar system in half the time.difficulty 3/5 — confidence 2/5
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- And that's exactly what we'll explore in this video, how the model conAnd that's exactly what we'll explore in this video, how the model context protocol is changing the way AI systems connect to data.difficulty 3/5 — confidence 2/5
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- By the end of this video, you will be able to identify challenges in tBy the end of this video, you will be able to identify challenges in traditional AI integrations, explain how MCP standardizes and secures data connections, and summarize how MCP enables smarter, faster AI solutions.difficulty 3/5 — confidence 2/5
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- AI systems are only as good as the data they can access.AI systems are only as good as the data they can access.difficulty 3/5 — confidence 2/5
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- Module 1Custom GPTs Fundamentals Module 2THINK: Create Great GPTs (ParModule 1Custom GPTs Fundamentals Module 2THINK: Create Great GPTs (Part I) Module 3THINK: Create Great GPTs (Part II) Benchmark Design ConsiderationsReading.difficulty 3/5 — confidence 2/5
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- Duration: 20 minutes20 min Build a Custom GPT for Generating Test CaseDuration: 20 minutes20 min Build a Custom GPT for Generating Test CasesVideo.difficulty 3/5 — confidence 2/5
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- Duration: 8 minutes8 min Build Your Own Custom GPT Test Case GeneratorDuration: 8 minutes8 min Build Your Own Custom GPT Test Case GeneratorGraded Assignment.difficulty 3/5 — confidence 2/5
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- Duration: 30 minutes30 min The Goal is to Help the Human Solve the ProDuration: 30 minutes30 min The Goal is to Help the Human Solve the Problem, Not Provide the AnswerVideo.difficulty 3/5 — confidence 2/5
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- Duration: 1 minute1 min Practical ScenarioReal-world application.Duration: 1 minute1 min Practical ScenarioReal-world application.difficulty 3/5 — confidence 2/5
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- Duration: 5 minutes5 min Template Pattern & MarkdownReading.Duration: 5 minutes5 min Template Pattern & MarkdownReading.difficulty 3/5 — confidence 2/5
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- Duration: 10 minutes10 min Format of the Menu Actions PatternReading.Duration: 10 minutes10 min Format of the Menu Actions PatternReading.difficulty 3/5 — confidence 2/5
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- Duration: 10 minutes10 min Where to Get Additional HelpVideo.Duration: 10 minutes10 min Where to Get Additional HelpVideo.difficulty 3/5 — confidence 2/5
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- Duration: 2 minutes2 min Building a GPT with a MenuGraded Assignment.Duration: 2 minutes2 min Building a GPT with a MenuGraded Assignment.difficulty 3/5 — confidence 2/5
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- Duration: 30 minutes30 min Information Before Decision MakingVideo.Duration: 30 minutes30 min Information Before Decision MakingVideo.difficulty 3/5 — confidence 2/5
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- The testing should include variability in the test cases to mimic theThe testing should include variability in the test cases to mimic the real-world unpredictability of user interactions.difficulty 3/5 — confidence 2/5
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- To achieve this, we classify our test cases into diverse categories suTo achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges.difficulty 3/5 — confidence 2/5
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- Moreover, we consider the user's characteristics like literacy levelsMoreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users.difficulty 3/5 — confidence 2/5
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- We also test it with different levels of input complexity from shortWe also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up.difficulty 3/5 — confidence 2/5
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- This rigorous testing ensures that the GPT can deliver high-quality, rThis rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios.difficulty 3/5 — confidence 2/5
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- 1.What if a customer is expressing frustration in a non-direct way?1.What if a customer is expressing frustration in a non-direct way?difficulty 3/5 — confidence 2/5
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- Testing how the GPT detects passive language indicative of frustration–Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques.difficulty 3/5 — confidence 2/5
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- 2.What if a customer uses technical jargon incorrectly?2.What if a customer uses technical jargon incorrectly?difficulty 3/5 — confidence 2/5
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- Testing whether the GPT can gently correct the customer and provide th–Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense.difficulty 3/5 — confidence 2/5
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- 3.What if the customer asks for a service or product that doesn’t exis3.What if the customer asks for a service or product that doesn’t exist?difficulty 3/5 — confidence 2/5
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- R Status: PLUS PLUS OpenAI GPTs: Creating Your Own Custom AI AssistantR Status: PLUS PLUS OpenAI GPTs: Creating Your Own Custom AI Assistants Today's Skill Points 18 XP See skill progress Module 1 Custom GPTs Fundamentals Module 2 THINK: Create Great GPTs (Part I) Test Test Video .difficulty 3/5 — confidence 2/5
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- Duration: 1 minute 1 min Build a Benchmark Video .Duration: 1 minute 1 min Build a Benchmark Video .difficulty 3/5 — confidence 2/5
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- Duration: 5 minutes 5 min Benchmark Design Considerations Reading .Duration: 5 minutes 5 min Benchmark Design Considerations Reading .difficulty 3/5 — confidence 2/5
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- Duration: 20 minutes 20 min Build a Custom GPT for Generating Test CasDuration: 20 minutes 20 min Build a Custom GPT for Generating Test Cases Video .difficulty 3/5 — confidence 2/5
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- Duration: 8 minutes 8 min Build Your Own Custom GPT Test Case GeneratoDuration: 8 minutes 8 min Build Your Own Custom GPT Test Case Generator Graded Assignment .difficulty 3/5 — confidence 2/5
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- Duration: 30 minutes 30 min Help the User Solve the Problem, Not ProviDuration: 30 minutes 30 min Help the User Solve the Problem, Not Provide Answers The Goal is to Help the Human Solve the Problem, Not Provide the Answer Video .difficulty 3/5 — confidence 2/5
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- Duration: 1 minute 1 min How to Cite Knowledge Video .Duration: 1 minute 1 min How to Cite Knowledge Video .difficulty 3/5 — confidence 2/5
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- Duration: 4 minutes 4 min Output Formatting Video .Duration: 4 minutes 4 min Output Formatting Video .difficulty 3/5 — confidence 2/5
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- Duration: 6 minutes 6 min Practical Scenario Real-world application .Duration: 6 minutes 6 min Practical Scenario Real-world application .difficulty 3/5 — confidence 2/5
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- Duration: 5 minutes 5 min Template Pattern & Markdown Reading .Duration: 5 minutes 5 min Template Pattern & Markdown Reading .difficulty 3/5 — confidence 2/5
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- Thinking up great test cases obviously is going to be a challenge.>> Thinking up great test cases obviously is going to be a challenge.difficulty 3/5 — confidence 2/5
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- You have to really think carefully about all the different dimensions.You have to really think carefully about all the different dimensions.difficulty 3/5 — confidence 2/5
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- And sometimes as human beings, we don't do the best job of really thinAnd sometimes as human beings, we don't do the best job of really thinking through all the different ways that somebody could interact with our system and all the different issues that could arise.difficulty 3/5 — confidence 2/5
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- And I've given you a number of different dimensions to think about wheAnd I've given you a number of different dimensions to think about when building test cases.difficulty 3/5 — confidence 2/5
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- Now, how are we going to overcome this problem?Now, how are we going to overcome this problem?difficulty 3/5 — confidence 2/5
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- Well, one way that I can help you overcome this problem is I can showWell, one way that I can help you overcome this problem is I can show you how to build your own custom GPT to generate your test cases.difficulty 3/5 — confidence 2/5
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- In fact, this is a great way to get started with building a custom GPTIn fact, this is a great way to get started with building a custom GPT because the risk is extremely low.difficulty 3/5 — confidence 2/5
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- We are trying to have it generate ideas for test cases that we can theWe are trying to have it generate ideas for test cases that we can then use to test other GPTs.difficulty 3/5 — confidence 2/5
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- Now, if it doesn't do a great job, we'll look at it and say, hey, noneNow, if it doesn't do a great job, we'll look at it and say, hey, none of those are useful test cases to me.difficulty 3/5 — confidence 2/5
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- We can go and tweak it and try to improve it, but often what we'll seeWe can go and tweak it and try to improve it, but often what we'll see is it can generate really good and sort of thoughtful test cases for our domain.difficulty 3/5 — confidence 2/5
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- Anytime we change the instructions to our GPT or we change the knowledAnytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects.difficulty 3/5 — confidence 2/5
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- One of the important things that we want to do when we start buildingOne of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT?difficulty 3/5 — confidence 2/5
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- A very important thing to do is to build yourself a simple benchmark.A very important thing to do is to build yourself a simple benchmark.difficulty 3/5 — confidence 2/5
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- Well, you do this so that as you make changes, you can make sure thatWell, you do this so that as you make changes, you can make sure that it's still reasoning effectively.difficulty 3/5 — confidence 2/5
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- That it's not regressing in some area and starting to give bad answersThat it's not regressing in some area and starting to give bad answers to something that used to do well on.difficulty 3/5 — confidence 2/5
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- But the other reason you want to do it is because you want to make surBut the other reason you want to do it is because you want to make sure that it really is as good as you think it is.difficulty 3/5 — confidence 2/5
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- Often when we go and sort of ad hoc to this, we may miss areas where wOften when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it.difficulty 3/5 — confidence 2/5
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- Then it turns out it doesn't do a very good job of it.Then it turns out it doesn't do a very good job of it.difficulty 3/5 — confidence 2/5
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- Building yourself a benchmark is a really simple thing that you can doBuilding yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well.difficulty 3/5 — confidence 2/5
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- But two, that as you go and modify it and improve it over time, that yBut two, that as you go and modify it and improve it over time, that you don't have some type of regression.difficulty 3/5 — confidence 2/5
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- Last month, Louise worked with a healthcare company facing a criticalLast month, Louise worked with a healthcare company facing a critical decision.difficulty 3/5 — confidence 2/5
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- Their AI diagnostic system needed to connect with 15 different data soTheir AI diagnostic system needed to connect with 15 different data sources, patient records, lab results, imaging systems, drug databases.difficulty 3/5 — confidence 2/5
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- Their current approach was failing, costs were escalating, and their gTheir current approach was failing, costs were escalating, and their go-to-market timeline was at risk.difficulty 3/5 — confidence 2/5
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- Switching their entire integration strategy to MCP required a systematSwitching their entire integration strategy to MCP required a systematic evaluation framework.difficulty 3/5 — confidence 2/5
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- The company had to evaluate how many different data sources does the AThe company had to evaluate how many different data sources does the AI system need to connect with.difficulty 3/5 — confidence 2/5
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- If it was more than three, MCP's standardization benefit would becomeIf it was more than three, MCP's standardization benefit would become compelling.difficulty 3/5 — confidence 2/5
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- If the company was building point-to-point connections, traditional apIf the company was building point-to-point connections, traditional approaches might suffice.difficulty 3/5 — confidence 2/5
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- But if the company was planning to build an AI ecosystem, MCP would beBut if the company was planning to build an AI ecosystem, MCP would be essential.difficulty 3/5 — confidence 2/5
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- MCP provides built-in security models, but you need to evaluate if theMCP provides built-in security models, but you need to evaluate if they meet your specific requirements.difficulty 3/5 — confidence 2/5
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- For healthcare, finance, or government applications, MCP's standardizeFor healthcare, finance, or government applications, MCP's standardized security approach often exceeds what custom solutions provide.difficulty 3/5 — confidence 2/5
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Explanation Study Kits
Focus van deze les: Imagine an agent that doesn't just act, but learns from each action to refine its future decisions Wat je echt moet begrijpen: That's what systems like Baby AGI and AlphaCode demonstrate Hoe je dit stap voor stap toepast: 1. De uitleg opent met Imagine an agent that doesn't just act, but learns fr
- Imagine an agent that doesn't just act, but learns from each action to refine its future decisions
- Wat je echt moet begrijpen:
- That's what systems like Baby AGI and AlphaCode demonstrate
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Imagine an agent that doesn't just act, but learns from each action to refine its future decisions.. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via That's what systems like Baby AGI and AlphaCode demonstrate., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera Summary: Focus van deze les: Imagine an agent that doesn't just act, but learns from each action to refine its future decisions Wat je echt moet begrijpen: That's what systems like Baby AGI and AlphaCode demonstrate Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Imagine an agent that doesn't just act, but learns fr Key points: - Imagine an agent that doesn't just act, but learns from each action to refine its future decisions - Wat je echt moet begrijpen: - That's what systems like Baby AGI and AlphaCode demonstrate - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Imagine an agent that doesn't just act, but learns from each action to refine its future decisions..
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Dat zet het vertrekpunt van de redenering neer.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: When your agent answers the wrong question or repeats a task, it's tempting to fix Wat je echt moet begrijpen: the output. But to build intelligent systems, you have to debug the behavior, not just the Hoe je dit stap voor stap toepast: 1.
- When your agent answers the wrong question or repeats a task, it's tempting to fix
- Wat je echt moet begrijpen:
- the output. But to build intelligent systems, you have to debug the behavior, not just the
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Did it select the right tool or intent?. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via traditional software, AI agents rely on decision loops, memory and tool use, which means bugs, zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Agent Behavior Breakdown: Debugging and Testing | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Agent Behavior Breakdown: Debugging and Testing | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Agent Behavior Breakdown: Debugging and Testing | Coursera Summary: Focus van deze les: When your agent answers the wrong question or repeats a task, it's tempting to fix Wat je echt moet begrijpen: the output.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- But to build intelligent systems, you have to debug the behavior, not just the Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - When your agent answers the wrong question or repeats a task, it's tempting to fix - Wat je echt moet begrijpen: - the output.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- But to build intelligent systems, you have to debug the behavior, not just the - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Imagine an agent that doesn't just act, but learns from each action to refine its future decisions Wat je echt moet begrijpen: That's what systems like Baby AGI and AlphaCode demonstrate Hoe je dit stap voor stap toepast: 1. De uitleg opent met Imagine an agent that doesn't just act, but learns fr
- Imagine an agent that doesn't just act, but learns from each action to refine its future decisions
- Wat je echt moet begrijpen:
- That's what systems like Baby AGI and AlphaCode demonstrate
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Imagine an agent that doesn't just act, but learns from each action to refine its future decisions.. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via That's what systems like Baby AGI and AlphaCode demonstrate., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera Summary: Focus van deze les: Imagine an agent that doesn't just act, but learns from each action to refine its future decisions Wat je echt moet begrijpen: That's what systems like Baby AGI and AlphaCode demonstrate Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Imagine an agent that doesn't just act, but learns fr Key points: - Imagine an agent that doesn't just act, but learns from each action to refine its future decisions - Wat je echt moet begrijpen: - That's what systems like Baby AGI and AlphaCode demonstrate - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Imagine an agent that doesn't just act, but learns from each action to refine its future decisions..
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Dat zet het vertrekpunt van de redenering neer.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: We can always go and upload individual files and download individual files to Wat je echt moet begrijpen: code interpreter, and we can certainly get by that way Hoe je dit stap voor stap toepast: 1. De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- We can always go and upload individual files and download individual files to
- Wat je echt moet begrijpen:
- code interpreter, and we can certainly get by that way
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via And this is just a fantastic tool for file organization., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Zip Files for Automation | Coursera Summary: Focus van deze les: We can always go and upload individual files and download individual files to Wat je echt moet begrijpen: code interpreter, and we can certainly get by that way Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - We can always go and upload individual files and download individual files to - Wat je echt moet begrijpen: - code interpreter, and we can certainly get by that way - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/4] Code Interpreter is really useful for working with media. If you have videos, if you have audio files, if you have collections of images, working with all of them, you...
- Part 1/4] Code Interpreter is really useful for working with media. If you have videos, if you have audio files, if you have collections of images, working with all of them, you...
- Wat je echt moet begrijpen:
- Part 2/4] I'm giving it a pattern to follow on each individual one. I'm saying resize each, and then I'm giving it to constraints what I want. This is going to be a pattern that...
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met [Part 1/4] Code Interpreter is really useful for working with media.. Dat zet het vertrekpunt van de redenering neer.
- 3. Vervolgens bouwt de les verder via So I'm going to give you an example of this., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Working with Media | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Working with Media | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Working with Media | Coursera Summary: Focus van deze les: Part 1/4] Code Interpreter is really useful for working with media.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- If you have videos, if you have audio files, if you have collections of images, working with all of them, you...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Part 1/4] Code Interpreter is really useful for working with media.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- If you have videos, if you have audio files, if you have collections of images, working with all of them, you...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Wenn Sie hören, dass Code-Interpreter die Art Wat je echt moet begrijpen: und Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten Hoe je dit stap voor stap toepast: 1. De uitleg opent met Wenn Sie hören, dass Code-Interpreter die Art.
- Wenn Sie hören, dass Code-Interpreter die Art
- Wat je echt moet begrijpen:
- und Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Wenn Sie hören, dass Code-Interpreter die Art. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via und Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten,, zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Working with Structured Data | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Working with Structured Data | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Working with Structured Data | Coursera Summary: Focus van deze les: Wenn Sie hören, dass Code-Interpreter die Art Wat je echt moet begrijpen: und Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Wenn Sie hören, dass Code-Interpreter die Art.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Wenn Sie hören, dass Code-Interpreter die Art - Wat je echt moet begrijpen: - und Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Wenn Sie hören, dass Code-Interpreter die Art.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/5] Let's have some fun with code interpreter by learning to work with small documents and what document could be more fun every year to work with than the IRS 1040, that... Wat je echt moet begrijpen: Part 2/5] That's going to trigger code interpreter basically going and looking at what's ins
- Part 1/5] Let's have some fun with code interpreter by learning to work with small documents and what document could be more fun every year to work with than the IRS 1040, that...
- Wat je echt moet begrijpen:
- Part 2/5] That's going to trigger code interpreter basically going and looking at what's inside the document. It's gone and looked through and inspected the documents. That's fi...
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met It's possibly the most fun document that I ever worked with in my life.. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via It's the document that I absolutely dread dealing with., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Asking Questions in a Small Document | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Asking Questions in a Small Document | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Asking Questions in a Small Document | Coursera Summary: Focus van deze les: Part 1/5] Let's have some fun with code interpreter by learning to work with small documents and what document could be more fun every year to work with than the IRS 1040, that...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Wat je echt moet begrijpen: Part 2/5] That's going to trigger code interpreter basically going and looking at what's ins Key points: - Part 1/5] Let's have some fun with code interpreter by learning to work with small documents and what document could be more fun every year to work with than the IRS 1040, that...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Wat je echt moet begrijpen: - Part 2/5] That's going to trigger code interpreter basically going and looking at what's inside the document.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- It's gone and looked through and inspected the documents.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Code interpreter can help us provide a safety net so Wat je echt moet begrijpen: that we can catch mistakes by humans before they go out and cause a problem Hoe je dit stap voor stap toepast: 1. De uitleg opent met Code interpreter can help us provide a safety net so.
- Code interpreter can help us provide a safety net so
- Wat je echt moet begrijpen:
- that we can catch mistakes by humans before they go out and cause a problem
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Code interpreter can help us provide a safety net so. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via that we can catch mistakes by humans before they go out and cause a problem., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "(H) Help Provide a Safety Net | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "(H) Help Provide a Safety Net | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: (H) Help Provide a Safety Net | Coursera Summary: Focus van deze les: Code interpreter can help us provide a safety net so Wat je echt moet begrijpen: that we can catch mistakes by humans before they go out and cause a problem Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Code interpreter can help us provide a safety net so.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Code interpreter can help us provide a safety net so - Wat je echt moet begrijpen: - that we can catch mistakes by humans before they go out and cause a problem - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Code interpreter can help us provide a safety net so.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/4] Code Interpreter is really useful for working with media. If you have videos, if you have audio files, if you have collections of images, working with all of them, you...
- Part 1/4] Code Interpreter is really useful for working with media. If you have videos, if you have audio files, if you have collections of images, working with all of them, you...
- Wat je echt moet begrijpen:
- Part 2/4] I'm giving it a pattern to follow on each individual one. I'm saying resize each, and then I'm giving it to constraints what I want. This is going to be a pattern that...
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met [Part 1/4] Code Interpreter is really useful for working with media.. Dat zet het vertrekpunt van de redenering neer.
- 3. Vervolgens bouwt de les verder via So I'm going to give you an example of this., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Working with Media | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Working with Media | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Working with Media | Coursera Summary: Focus van deze les: Part 1/4] Code Interpreter is really useful for working with media.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- If you have videos, if you have audio files, if you have collections of images, working with all of them, you...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Part 1/4] Code Interpreter is really useful for working with media.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- If you have videos, if you have audio files, if you have collections of images, working with all of them, you...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Wenn Sie hören, dass Code-Interpreter die Art Wat je echt moet begrijpen: und Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten Hoe je dit stap voor stap toepast: 1. De uitleg opent met Wenn Sie hören, dass Code-Interpreter die Art.
- Wenn Sie hören, dass Code-Interpreter die Art
- Wat je echt moet begrijpen:
- und Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Wenn Sie hören, dass Code-Interpreter die Art. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via und Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten,, zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Working with Structured Data | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Working with Structured Data | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Working with Structured Data | Coursera Summary: Focus van deze les: Wenn Sie hören, dass Code-Interpreter die Art Wat je echt moet begrijpen: und Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Wenn Sie hören, dass Code-Interpreter die Art.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Wenn Sie hören, dass Code-Interpreter die Art - Wat je echt moet begrijpen: - und Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Wenn Sie hören, dass Code-Interpreter die Art.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/5] Let's have some fun with code interpreter by learning to work with small documents and what document could be more fun every year to work with than the IRS 1040, that... Wat je echt moet begrijpen: Part 2/5] That's going to trigger code interpreter basically going and looking at what's ins
- Part 1/5] Let's have some fun with code interpreter by learning to work with small documents and what document could be more fun every year to work with than the IRS 1040, that...
- Wat je echt moet begrijpen:
- Part 2/5] That's going to trigger code interpreter basically going and looking at what's inside the document. It's gone and looked through and inspected the documents. That's fi...
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met It's possibly the most fun document that I ever worked with in my life.. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via It's the document that I absolutely dread dealing with., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Asking Questions in a Small Document | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Asking Questions in a Small Document | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Asking Questions in a Small Document | Coursera Summary: Focus van deze les: Part 1/5] Let's have some fun with code interpreter by learning to work with small documents and what document could be more fun every year to work with than the IRS 1040, that...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Wat je echt moet begrijpen: Part 2/5] That's going to trigger code interpreter basically going and looking at what's ins Key points: - Part 1/5] Let's have some fun with code interpreter by learning to work with small documents and what document could be more fun every year to work with than the IRS 1040, that...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Wat je echt moet begrijpen: - Part 2/5] That's going to trigger code interpreter basically going and looking at what's inside the document.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- It's gone and looked through and inspected the documents.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well. Or to wr...
- Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well. Or to wr...
- Wat je echt moet begrijpen:
- Part 2/5] ead, it did not go and run Python again. Now, there's two things that that could mean, and it's helpful to know this. One is the information that it needs to do this t...
- Hoe je dit stap voor stap toepast:
- 4. Daarna laat de les zien hoe Now, what does it mean for it to be easy to access? het resultaat of de toepassing afrondt.
- Waar je op moet letten in de bron:
- 1) Welk probleem helpt "Getting Information Into the Conversation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Getting Information Into the Conversation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Getting Information Into the Conversation | Coursera Summary: Focus van deze les: Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Or to wr...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Or to wr...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: This next capability I'm going to talk about Wat je echt moet begrijpen: is really one that you can only Hoe je dit stap voor stap toepast: 1. De uitleg opent met Add places where all these paths.
- This next capability I'm going to talk about
- Wat je echt moet begrijpen:
- is really one that you can only
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Add places where all these paths. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via the paths to the documents as command line argument,, zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Zip Files for Automation | Coursera Summary: Focus van deze les: This next capability I'm going to talk about Wat je echt moet begrijpen: is really one that you can only Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Add places where all these paths.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - This next capability I'm going to talk about - Wat je echt moet begrijpen: - is really one that you can only - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Add places where all these paths.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: This next capability I'm going to talk about Wat je echt moet begrijpen: is really one that you can only Hoe je dit stap voor stap toepast: 1. De uitleg opent met Add places where all these paths.
- This next capability I'm going to talk about
- Wat je echt moet begrijpen:
- is really one that you can only
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Add places where all these paths. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via the paths to the documents as command line argument,, zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Zip Files for Automation | Coursera Summary: Focus van deze les: This next capability I'm going to talk about Wat je echt moet begrijpen: is really one that you can only Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Add places where all these paths.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - This next capability I'm going to talk about - Wat je echt moet begrijpen: - is really one that you can only - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met Add places where all these paths.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1. De uitleg opent met I want to help you gauge the difficulty of.
- I want to help you gauge the difficulty of
- Wat je echt moet begrijpen:
- a task that you're about to start with Code Interpreter
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met I want to help you gauge the difficulty of. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via a task that you're about to start with Code Interpreter., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Turning Conversations into Software Utilities | Coursera Summary: Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - I want to help you gauge the difficulty of - Wat je echt moet begrijpen: - a task that you're about to start with Code Interpreter - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: We can always go and upload individual files and download individual files to Wat je echt moet begrijpen: code interpreter, and we can certainly get by that way Hoe je dit stap voor stap toepast: 1. De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- We can always go and upload individual files and download individual files to
- Wat je echt moet begrijpen:
- code interpreter, and we can certainly get by that way
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via And this is just a fantastic tool for file organization., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Zip Files for Automation | Coursera Summary: Focus van deze les: We can always go and upload individual files and download individual files to Wat je echt moet begrijpen: code interpreter, and we can certainly get by that way Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - We can always go and upload individual files and download individual files to - Wat je echt moet begrijpen: - code interpreter, and we can certainly get by that way - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: We can always go and upload individual files and download individual files to Wat je echt moet begrijpen: code interpreter, and we can certainly get by that way Hoe je dit stap voor stap toepast: 1. De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- We can always go and upload individual files and download individual files to
- Wat je echt moet begrijpen:
- code interpreter, and we can certainly get by that way
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via And this is just a fantastic tool for file organization., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Zip Files for Automation | Coursera Summary: Focus van deze les: We can always go and upload individual files and download individual files to Wat je echt moet begrijpen: code interpreter, and we can certainly get by that way Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - We can always go and upload individual files and download individual files to - Wat je echt moet begrijpen: - code interpreter, and we can certainly get by that way - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1. De uitleg opent met I want to help you gauge the difficulty of.
- I want to help you gauge the difficulty of
- Wat je echt moet begrijpen:
- a task that you're about to start with Code Interpreter
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met I want to help you gauge the difficulty of. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via a task that you're about to start with Code Interpreter., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Working with Small Documents | Coursera Summary: Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - I want to help you gauge the difficulty of - Wat je echt moet begrijpen: - a task that you're about to start with Code Interpreter - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp... Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c
- Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Wat je echt moet begrijpen:
- Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Hoe je dit stap voor stap toepast:
- 4. Daarna laat de les zien hoe If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. het resultaat of de toepassing afrondt.
- Waar je op moet letten in de bron:
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Turning Conversations into Software Utilities | Coursera Summary: Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c Key points: - Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Wat je echt moet begrijpen: - Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Hoe je dit stap voor stap toepast: - 4.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp... Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c
- Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Wat je echt moet begrijpen:
- Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Hoe je dit stap voor stap toepast:
- 4. Daarna laat de les zien hoe If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. het resultaat of de toepassing afrondt.
- Waar je op moet letten in de bron:
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Turning Conversations into Software Utilities | Coursera Summary: Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c Key points: - Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Wat je echt moet begrijpen: - Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Hoe je dit stap voor stap toepast: - 4.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well. Or to wr...
- Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well. Or to wr...
- Wat je echt moet begrijpen:
- Part 2/5] ead, it did not go and run Python again. Now, there's two things that that could mean, and it's helpful to know this. One is the information that it needs to do this t...
- Hoe je dit stap voor stap toepast:
- 4. Daarna laat de les zien hoe Now, what does it mean for it to be easy to access? het resultaat of de toepassing afrondt.
- Waar je op moet letten in de bron:
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Working with Small Documents | Coursera Summary: Focus van deze les: Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Or to wr...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Or to wr...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1. De uitleg opent met I want to help you gauge the difficulty of.
- I want to help you gauge the difficulty of
- Wat je echt moet begrijpen:
- a task that you're about to start with Code Interpreter
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met I want to help you gauge the difficulty of. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via a task that you're about to start with Code Interpreter., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Working with Small Documents" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Working with Small Documents" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Working with Small Documents Summary: Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - I want to help you gauge the difficulty of - Wat je echt moet begrijpen: - a task that you're about to start with Code Interpreter - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp... Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c
- Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Wat je echt moet begrijpen:
- Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Hoe je dit stap voor stap toepast:
- 4. Daarna laat de les zien hoe If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. het resultaat of de toepassing afrondt.
- Waar je op moet letten in de bron:
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Turning Conversations into Software Utilities | Coursera Summary: Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c Key points: - Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Wat je echt moet begrijpen: - Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Hoe je dit stap voor stap toepast: - 4.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1. De uitleg opent met I want to help you gauge the difficulty of.
- I want to help you gauge the difficulty of
- Wat je echt moet begrijpen:
- a task that you're about to start with Code Interpreter
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met I want to help you gauge the difficulty of. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via a task that you're about to start with Code Interpreter., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Working with Small Documents | Coursera Summary: Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - I want to help you gauge the difficulty of - Wat je echt moet begrijpen: - a task that you're about to start with Code Interpreter - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1. De uitleg opent met I want to help you gauge the difficulty of.
- I want to help you gauge the difficulty of
- Wat je echt moet begrijpen:
- a task that you're about to start with Code Interpreter
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met I want to help you gauge the difficulty of. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via a task that you're about to start with Code Interpreter., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Working with Small Documents | Coursera Summary: Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - I want to help you gauge the difficulty of - Wat je echt moet begrijpen: - a task that you're about to start with Code Interpreter - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp... Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c
- Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Wat je echt moet begrijpen:
- Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Hoe je dit stap voor stap toepast:
- 4. Daarna laat de les zien hoe If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. het resultaat of de toepassing afrondt.
- Waar je op moet letten in de bron:
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Turning Conversations into Software Utilities | Coursera Summary: Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c Key points: - Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Wat je echt moet begrijpen: - Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Hoe je dit stap voor stap toepast: - 4.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well. Or to wr...
- Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well. Or to wr...
- Wat je echt moet begrijpen:
- Part 2/5] ead, it did not go and run Python again. Now, there's two things that that could mean, and it's helpful to know this. One is the information that it needs to do this t...
- Hoe je dit stap voor stap toepast:
- 4. Daarna laat de les zien hoe Now, what does it mean for it to be easy to access? het resultaat of de toepassing afrondt.
- Waar je op moet letten in de bron:
- 1) Welk probleem helpt "Getting Information Into the Conversation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Getting Information Into the Conversation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Getting Information Into the Conversation | Coursera Summary: Focus van deze les: Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Or to wr...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Part 1/5] One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Or to wr...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1. De uitleg opent met I want to help you gauge the difficulty of.
- I want to help you gauge the difficulty of
- Wat je echt moet begrijpen:
- a task that you're about to start with Code Interpreter
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met I want to help you gauge the difficulty of. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via a task that you're about to start with Code Interpreter., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Working with Small Documents | Coursera Summary: Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - I want to help you gauge the difficulty of - Wat je echt moet begrijpen: - a task that you're about to start with Code Interpreter - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1. De uitleg opent met I want to help you gauge the difficulty of.
- I want to help you gauge the difficulty of
- Wat je echt moet begrijpen:
- a task that you're about to start with Code Interpreter
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met I want to help you gauge the difficulty of. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via a task that you're about to start with Code Interpreter., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Working with Small Documents | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Working with Small Documents | Coursera Summary: Focus van deze les: I want to help you gauge the difficulty of Wat je echt moet begrijpen: a task that you're about to start with Code Interpreter Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - I want to help you gauge the difficulty of - Wat je echt moet begrijpen: - a task that you're about to start with Code Interpreter - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met I want to help you gauge the difficulty of.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp... Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c
- Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Wat je echt moet begrijpen:
- Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Hoe je dit stap voor stap toepast:
- 4. Daarna laat de les zien hoe If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. het resultaat of de toepassing afrondt.
- Waar je op moet letten in de bron:
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Turning Conversations into Software Utilities | Coursera Summary: Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c Key points: - Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Wat je echt moet begrijpen: - Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Hoe je dit stap voor stap toepast: - 4.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp... Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c
- Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Wat je echt moet begrijpen:
- Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Hoe je dit stap voor stap toepast:
- 4. Daarna laat de les zien hoe If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. het resultaat of de toepassing afrondt.
- Waar je op moet letten in de bron:
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Turning Conversations into Software Utilities | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Turning Conversations into Software Utilities | Coursera Summary: Focus van deze les: Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Wat je echt moet begrijpen: Part 2/4] cases that you can then run completely separately from Code Interpreter and you c Key points: - Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Wat je echt moet begrijpen: - Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Hoe je dit stap voor stap toepast: - 4.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: We can always go and upload individual files and download individual files to Wat je echt moet begrijpen: code interpreter, and we can certainly get by that way Hoe je dit stap voor stap toepast: 1. De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- We can always go and upload individual files and download individual files to
- Wat je echt moet begrijpen:
- code interpreter, and we can certainly get by that way
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via And this is just a fantastic tool for file organization., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Zip Files for Automation | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Zip Files for Automation | Coursera Summary: Focus van deze les: We can always go and upload individual files and download individual files to Wat je echt moet begrijpen: code interpreter, and we can certainly get by that way Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - We can always go and upload individual files and download individual files to - Wat je echt moet begrijpen: - code interpreter, and we can certainly get by that way - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met And so it's a useful tool for organizing the input that you're giving it and.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Picture this. You walk into a room and the light turns on automatically.
- Picture this. You walk into a room and the light turns on automatically. Now picture
- Wat je echt moet begrijpen:
- asking your smart assistant to plan a movie night. Both systems are reacting, but very
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met It's about choosing the right tool for the right task.. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via select the right agent behavior model for specific use cases., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Reactive vs. Deliberative Agents in the Real World | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Reactive vs. Deliberative Agents in the Real World | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Reactive vs.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Deliberative Agents in the Real World | Coursera Summary: Focus van deze les: Picture this.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- You walk into a room and the light turns on automatically.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Picture this.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Have you ever wondered how a self-driving car knows when to slow down or how a voice Wat je echt moet begrijpen: assistant knows when you're asking a question versus just thinking out loud? Hoe je dit stap voor stap toepast: 1.
- Have you ever wondered how a self-driving car knows when to slow down or how a voice
- Wat je echt moet begrijpen:
- assistant knows when you're asking a question versus just thinking out loud?
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Have you ever wondered how a self-driving car knows when to slow down or how a voice. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via assistant knows when you're asking a question versus just thinking out loud?, zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- assistant knows when you're asking a question versus just thinking out loud?
- 1) Welk probleem helpt "How AI Agents Perceive the World Around Them | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- assistant knows when you're asking a question versus just thinking out loud?
- 1) Welk probleem helpt "How AI Agents Perceive the World Around Them | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: How AI Agents Perceive the World Around Them | Coursera Summary: Focus van deze les: Have you ever wondered how a self-driving car knows when to slow down or how a voice Wat je echt moet begrijpen: assistant knows when you're asking a question versus just thinking out loud?
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Have you ever wondered how a self-driving car knows when to slow down or how a voice - Wat je echt moet begrijpen: - assistant knows when you're asking a question versus just thinking out loud?
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Willkommen bei „Bedrohungen und Sicherheitslücken“. Nachdem Sie sich dieses Video angesehen haben, können Sie die verschiedenen Arten von Sicherheitsbedrohungen identifizieren,...
- Willkommen bei „Bedrohungen und Sicherheitslücken“. Nachdem Sie sich dieses Video angesehen haben, können Sie die verschiedenen Arten von Sicherheitsbedrohungen identifizieren,...
- Wat je echt moet begrijpen:
- Willkommen bei „Bedrohungen und Sicherheitslücken“
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Willkommen bei „Bedrohungen und Sicherheitslücken“.. Dat zet het vertrekpunt van de redenering neer.
- 4. Daarna laat de les zien hoe Es ist viel einfacher, Daten direkt von einem Laptop oder Server zu stehlen, als sich remote in ein komplexes Netzwerk zu hacken. het resultaat of de toepassing afrondt.
- 1) Welk probleem helpt "Threats and Breaches | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Threats and Breaches | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Threats and Breaches | Coursera Summary: Focus van deze les: Willkommen bei „Bedrohungen und Sicherheitslücken“.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Nachdem Sie sich dieses Video angesehen haben, können Sie die verschiedenen Arten von Sicherheitsbedrohungen identifizieren,...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Willkommen bei „Bedrohungen und Sicherheitslücken“.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Nachdem Sie sich dieses Video angesehen haben, können Sie die verschiedenen Arten von Sicherheitsbedrohungen identifizieren,...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Willkommen bei „Sicherheit und Datenschutz“. Nachdem Sie sich dieses Video angesehen haben, werden Sie in der Lage sein, geistiges Eigentum zu definieren, zu erklären, wie Daten...
- Willkommen bei „Sicherheit und Datenschutz“. Nachdem Sie sich dieses Video angesehen haben, werden Sie in der Lage sein, geistiges Eigentum zu definieren, zu erklären, wie Daten...
- Wat je echt moet begrijpen:
- Willkommen bei „Sicherheit und Datenschutz“
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Willkommen bei „Sicherheit und Datenschutz“.. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via Ein Informationsgut sind Informationen oder Daten, die von Wert sind., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Security and Information Privacy | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
- 1) Welk probleem helpt "Security and Information Privacy | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 4) Hoe leg je dit in één minuut aan iemand anders uit?
Flashcards (10)
Quiz later (4)
- Concept: Security and Information Privacy | Coursera Summary: Focus van deze les: Willkommen bei „Sicherheit und Datenschutz“.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Nachdem Sie sich dieses Video angesehen haben, werden Sie in der Lage sein, geistiges Eigentum zu definieren, zu erklären, wie Daten...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Willkommen bei „Sicherheit und Datenschutz“.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Nachdem Sie sich dieses Video angesehen haben, werden Sie in der Lage sein, geistiges Eigentum zu definieren, zu erklären, wie Daten...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Manage Consent PreferencesEssential CookiesAlways ActiveThese cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use... Wat je echt moet begrijpen: These cookies are automatically enabled and cannot be turned off because they are required f
- Manage Consent PreferencesEssential CookiesAlways ActiveThese cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use...
- Wat je echt moet begrijpen:
- These cookies are automatically enabled and cannot be turned off because they are required for the Site to function properly.Analytics CookiesAlways ActiveThese cookies allow us...
- Hoe je dit stap voor stap toepast:
- Waar je op moet letten in de bron:
- Onthoud vooral: They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites
- 1) Welk probleem helpt "Introduction to Cybersecurity Essentials - Home - Week week | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Focus van deze les: Information is a summary of the raw data. For example, positive or negative results that happen after some specific change.
- Information is a summary of the raw data. For example, positive or negative results that happen after some specific change. And, insights are conclusions based on the results...
- Wat je echt moet begrijpen:
- Industrial designs, trade secrets, and research discoveries are all examples of IP. Even some employee knowledge is considered intellectual property. Companies use a legally...
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Information is a summary of the raw data.. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via For example, positive or negative results that happen after some specific change., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Security and Information Privacy | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Security and Information Privacy | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Security and Information Privacy | Coursera Summary: Focus van deze les: Information is a summary of the raw data.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- For example, positive or negative results that happen after some specific change.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Information is a summary of the raw data.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- For example, positive or negative results that happen after some specific change.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Information is a summary of the raw data. For example, positive or negative results that happen after some specific change.
- Information is a summary of the raw data. For example, positive or negative results that happen after some specific change. And, insights are conclusions based on the results...
- Wat je echt moet begrijpen:
- Industrial designs, trade secrets, and research discoveries are all examples of IP. Even some employee knowledge is considered intellectual property. Companies use a legally...
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Information is a summary of the raw data.. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via For example, positive or negative results that happen after some specific change., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Security and Information Privacy | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Security and Information Privacy | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Security and Information Privacy | Coursera Summary: Focus van deze les: Information is a summary of the raw data.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- For example, positive or negative results that happen after some specific change.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Information is a summary of the raw data.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- For example, positive or negative results that happen after some specific change.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Manage Consent PreferencesEssential CookiesAlways ActiveThese cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use... Wat je echt moet begrijpen: These cookies are automatically enabled and cannot be turned off because they are required f
- Manage Consent PreferencesEssential CookiesAlways ActiveThese cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use...
- Wat je echt moet begrijpen:
- These cookies are automatically enabled and cannot be turned off because they are required for the Site to function properly.Analytics CookiesAlways ActiveThese cookies allow us...
- Hoe je dit stap voor stap toepast:
- Waar je op moet letten in de bron:
- Onthoud vooral: They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites
- 1) Welk probleem helpt "Introduction to Cybersecurity Essentials - Home - Week week | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Focus van deze les: And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and...
- And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and...
- Wat je echt moet begrijpen:
- And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers.. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via We’re here to support your success, and we’re excited that you’re here., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Course Introduction | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Course Introduction | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Course Introduction | Coursera Summary: Focus van deze les: And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- We’re here to support your success, and...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- We’re here to support your success, and...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Manage Consent PreferencesEssential CookiesAlways ActiveThese cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use... Wat je echt moet begrijpen: These cookies are automatically enabled and cannot be turned off because they are required f
- Manage Consent PreferencesEssential CookiesAlways ActiveThese cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use...
- Wat je echt moet begrijpen:
- These cookies are automatically enabled and cannot be turned off because they are required for the Site to function properly.Analytics CookiesAlways ActiveThese cookies allow us...
- Hoe je dit stap voor stap toepast:
- Waar je op moet letten in de bron:
- Onthoud vooral: They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites
- 1) Welk probleem helpt "Introduction to Cybersecurity Essentials - Home - Week week | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Introduction to Cybersecurity Essentials - Home - Week week | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Introduction to Cybersecurity Essentials - Home - Week week | Coursera Summary: Focus van deze les: Manage Consent PreferencesEssential CookiesAlways ActiveThese cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Wat je echt moet begrijpen: These cookies are automatically enabled and cannot be turned off because they are required f Key points: - Manage Consent PreferencesEssential CookiesAlways ActiveThese cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Wat je echt moet begrijpen: - These cookies are automatically enabled and cannot be turned off because they are required for the Site to function properly.Analytics CookiesAlways ActiveThese cookies allow us...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- - Hoe je dit stap voor stap toepast: - Waar je op moet letten in de bron: - Onthoud vooral: They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites Explanation: Focus van deze les: Manage Consent PreferencesEssential CookiesAlways ActiveThese cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: Benefits include modularity, extensibility, decomposition capabilities, and easy integration with vector databases. Several practical applications include deciphering complex...
- Benefits include modularity, extensibility, decomposition capabilities, and easy integration with vector databases. Several practical applications include deciphering complex...
- Wat je echt moet begrijpen:
- Benefits include modularity, extensibility, decomposition capabilities, and easy integration with vector databases
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met Benefits include modularity, extensibility, decomposition capabilities, and easy integration with vector databases.. Dat zet het vertrekpunt van de redenering neer.
- 3. Vervolgens bouwt de les verder via LangChain can be used with other data types by using external libraries and models., zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Introduction to LangChain | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Introduction to LangChain | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Introduction to LangChain | Coursera Summary: Focus van deze les: Benefits include modularity, extensibility, decomposition capabilities, and easy integration with vector databases.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Several practical applications include deciphering complex...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Key points: - Benefits include modularity, extensibility, decomposition capabilities, and easy integration with vector databases.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Several practical applications include deciphering complex...
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
Focus van deze les: 1.25 Introduction to In-Context Learning Warning: 3 XP Wat je echt moet begrijpen: Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files Hoe je dit stap voor stap toepast: 1. De uitleg opent met 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Gen
- 1.25 Introduction to In-Context Learning Warning: 3 XP
- Wat je echt moet begrijpen:
- Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files
- Hoe je dit stap voor stap toepast:
- 1. De uitleg opent met 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files. Dat zet het vertrekpunt van de redenering neer.
- 2. Vervolgens bouwt de les verder via 1.25 Introduction to In-Context Learning Warning: 3 XP, zodat duidelijk wordt hoe deze stap de volgende beslissing aanstuurt.
- 1) Welk probleem helpt "Introduction to In-Context Learning | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
- 1) Welk probleem helpt "Introduction to In-Context Learning | Coursera" oplossen?
- 2) Welke stap of eigenschap is cruciaal voor een correcte toepassing?
- 3) Welke fout maak je hier het snelst?
Flashcards (10)
Quiz later (4)
- Concept: Introduction to In-Context Learning | Coursera Summary: Focus van deze les: 1.25 Introduction to In-Context Learning Warning: 3 XP Wat je echt moet begrijpen: Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files Hoe je dit stap voor stap toepast: 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Gen Key points: - 1.25 Introduction to In-Context Learning Warning: 3 XP - Wat je echt moet begrijpen: - Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files - Hoe je dit stap voor stap toepast: - 1.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- De uitleg opent met 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
- Dat zet het vertrekpunt van de redenering neer.
- Een detail dat minder belangrijk is dan het hoofdidee.
- Een oppervlakkige formulering die niet uitlegt waarom het werkt.
- Een keuze die het concept verwart met iets anders.
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}flashcards — 2026-07-21
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"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
}
]
}quiz — 2026-07-21
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
"choices": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 2?",
"choices": [
"After watching this video, you'll be able to describe in-context learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 3?",
"choices": [
"You will also be able to explain the fundamentals of prompt engineering.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 4?",
"choices": [
"In-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
}
]
}cheat_sheet — 2026-07-21
{
"bullets": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
]
}summary — 2026-07-21
{
"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-21
{
"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
}
]
}quiz — 2026-07-21
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
"choices": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 2?",
"choices": [
"After watching this video, you'll be able to describe in-context learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 3?",
"choices": [
"You will also be able to explain the fundamentals of prompt engineering.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 4?",
"choices": [
"In-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
}
]
}cheat_sheet — 2026-07-21
{
"bullets": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
]
}summary — 2026-07-21
{
"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-21
{
"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
}
]
}quiz — 2026-07-21
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
"choices": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 2?",
"choices": [
"After watching this video, you'll be able to describe in-context learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 3?",
"choices": [
"You will also be able to explain the fundamentals of prompt engineering.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 4?",
"choices": [
"In-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
}
]
}cheat_sheet — 2026-07-21
{
"bullets": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
]
}summary — 2026-07-21
{
"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-20
{
"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
}
]
}quiz — 2026-07-20
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
"choices": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 2?",
"choices": [
"After watching this video, you'll be able to describe in-context learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 3?",
"choices": [
"You will also be able to explain the fundamentals of prompt engineering.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 4?",
"choices": [
"In-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
}
]
}cheat_sheet — 2026-07-20
{
"bullets": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
]
}summary — 2026-07-20
{
"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-20
{
"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
}
]
}quiz — 2026-07-20
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
"choices": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 2?",
"choices": [
"After watching this video, you'll be able to describe in-context learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 3?",
"choices": [
"You will also be able to explain the fundamentals of prompt engineering.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 4?",
"choices": [
"In-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
}
]
}cheat_sheet — 2026-07-20
{
"bullets": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
]
}summary — 2026-07-20
{
"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-20
{
"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
}
]
}quiz — 2026-07-20
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
"choices": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 2?",
"choices": [
"After watching this video, you'll be able to describe in-context learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 3?",
"choices": [
"You will also be able to explain the fundamentals of prompt engineering.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 4?",
"choices": [
"In-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
}
]
}cheat_sheet — 2026-07-20
{
"bullets": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
]
}summary — 2026-07-20
{
"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-20
{
"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
}
]
}quiz — 2026-07-20
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
"choices": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 2?",
"choices": [
"After watching this video, you'll be able to describe in-context learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 3?",
"choices": [
"You will also be able to explain the fundamentals of prompt engineering.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 4?",
"choices": [
"In-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
}
]
}cheat_sheet — 2026-07-20
{
"bullets": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
]
}summary — 2026-07-20
{
"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-15
{
"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
}
]
}quiz — 2026-07-15
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
"choices": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 2?",
"choices": [
"After watching this video, you'll be able to describe in-context learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 3?",
"choices": [
"You will also be able to explain the fundamentals of prompt engineering.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 4?",
"choices": [
"In-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
}
]
}cheat_sheet — 2026-07-15
{
"bullets": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
]
}summary — 2026-07-15
{
"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-12
{
"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
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}quiz — 2026-07-12
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
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"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
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"prompt": "Welke kernles hoort het best bij punt 2?",
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"Een detail dat minder belangrijk is dan het hoofdidee.",
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"Een keuze die het concept verwart met iets anders."
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{
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"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
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{
"prompt": "Welke kernles hoort het best bij punt 4?",
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{
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"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-12
{
"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
}
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}quiz — 2026-07-12
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
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"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
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},
{
"prompt": "Welke kernles hoort het best bij punt 3?",
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"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
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},
{
"prompt": "Welke kernles hoort het best bij punt 4?",
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}cheat_sheet — 2026-07-12
{
"bullets": [
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"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-11
{
"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
}
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}quiz — 2026-07-11
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
"choices": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
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"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
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{
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"answerIndex": 0,
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"Een keuze die het concept verwart met iets anders."
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"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
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"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
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}quiz — 2026-07-11
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
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"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
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"answerIndex": 0,
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{
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"answerIndex": 0,
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},
{
"prompt": "Welke kernles hoort het best bij punt 4?",
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{
"bullets": [
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"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-11
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"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
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}quiz — 2026-07-11
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
"choices": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
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"answerIndex": 0,
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{
"prompt": "Welke kernles hoort het best bij punt 3?",
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{
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"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}flashcards — 2026-07-11
{
"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
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}quiz — 2026-07-11
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
"choices": [
"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
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"Een keuze die het concept verwart met iets anders."
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"answerIndex": 0,
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{
"prompt": "Welke kernles hoort het best bij punt 3?",
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},
{
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{
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{
"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
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"cards": [
{
"front": "Herinnering 1",
"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
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}quiz — 2026-07-11
{
"questions": [
{
"prompt": "Welke kernles hoort het best bij punt 1?",
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"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
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"answerIndex": 0,
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{
"prompt": "Welke kernles hoort het best bij punt 3?",
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"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
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"text": "Concept: BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera:\n\nKernidee: Concept: BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera\n\nSummary:\nFocus van deze les:\nImagine an agent that doesn't just act, but learns from each action to refine its future decisions\n\nWat je echt moet begrijpen:\nThat's what systems like Baby AGI and AlphaCode demonstrate\n\nHoe je dit stap voor stap toepast:\n1.\n\nWaarom dit belangrijk is: De uitleg opent met Imagine an agent that doesn't just act, but learns fr\n\nKey points:\n- Imagine an agent that doesn't just act, but learns from each action to refine its future decisions\n- Wat je echt moet begrijpen:\n- That's what systems like Baby AGI and AlphaCode demonstrate\n- Hoe je dit stap voor stap toepast:\n- 1.\n\nWat je vooral moet onthouden: De uitleg opent met Imagine an agent that doesn't just act, but learns from each action to refine its future decisions.."
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{
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"sourceText": "When your agent answers the wrong question or repeats a task, it's tempting to fix\nthe output. But to build intelligent systems, you have to debug the behavior, not just the\nresult. Let's look at how to break down what your agent did, why it did it, and how\nto fix it. why debugging agents means analyzing behavior,\nnot just fixing broken outputs. What kinds of bugs emerge from decision loops, memory\nuse and tool logic? Key questions to ask when tracing agent reasoning and response failures.\nHow to design tests that evaluate reasoning chains, fallback handling and memory updates.\nHow to use logs and traces to identify where and why agent behavior breaks down. Unlike\ntraditional software, AI agents rely on decision loops, memory and tool use, which means bugs\ndon't just live in the code, they live in the logic behind tool choice, the breakdown\nof memory updates, the way an agent handles failed responses. You need to inspect not\njust what the agent said, but what it believed was true at the time. When debugging an agent,\nask yourself, did it perceive the input correctly? Did it select the right tool or intent? Was\nmemory updated properly? Was a fallback triggered? And why? Did the reasoning chain break a loop?\nHere's a lang chain agent that searched retreat too many documents and got stuck summarizing\nconflicting info. The issue, a missing guardrail on its planning logic. To catch these issues\nearly, build agent specific tests. You can simulate inputs and check expected reasoning\npaths, log tool calls and validate response accuracy, monitor token usage and memory changes.\nUse test cases that mirror edge cases like ambiguous queries or dropped context. You're\nnot just testing answers, you're testing the reasoning. Does the agent behave the way you\nexpect it to under pressure? Debugging AI agents means stepping into their loop, following\nhow they process, decide and act. You're testing not just outcomes, but behavioral logic across\ntime. Before we move on, take a moment to reflect and answer the quick question on your\nscreen. In this video, you learned why debugging AI agents requires inspecting behavior, not\njust outputs. How decision loops, memory and tool use affect agent reliability. Common\nfailure points like broken reasoning chains or outdated context. Key debugging questions\nto uncover root causes in agent logic. How to use logs, simulations and behavioral tests\nto improve agent performance. Early in development, agent errors are often treated like simple\nbugs, issues to be fixed with a patch or prompt tweak. Real breakthroughs happen when debugging\nshifts to unexplanation_study_pack — 2026-07-10
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"sourceText": "Code Interpreter is really useful for working with media.\nIf you have videos,\nif you have audio files,\nif you have collections of images,\nworking with all of them,\nyou can do a lot of things really quickly.\nYou can absolutely go into a program and try to create\nsome batch process or try to\nmanually do things in one of these tools,\nbut Code Interpreter is super powerful for going\nand exploring media and creating new types of idea.\nSo I'm going to give you an example of this.\nI've taken a video of my son biking.\nHe loves to go and do BMX racing\nand do dirt jumps and all kinds of interesting things.\nI've uploaded this video and I'm going to\ndo a simple extraction.\nI'm going to take 10 frames out of this video,\nand then I'm going to do some things with them.\nYou can go and play around with media\nand do all kinds of interesting things.\nI encourage you, after you do this,\ntake an image, upload it,\nand try experimenting with\ndoing different operations on the image.\nI'm just going to go and say extract 10 frames\nfrom this video evenly spaced apart.\nThis is something I would probably have to\ngo and look up some command line tool.\nI know the tool I would use and figure out\nexactly the commands to do this from the command line.\nBut it's a lot more fun and easier\nto just do this with Code Interpreter.\nIt goes and generates the code\nto go and extract the 10 images,\nand this is super useful.\nNow I have the 10 images and I can download each\nof them right here and go and work with them.\nThat in itself is a super useful thing.\nIf I have some image,\nor I have some movie,\nor some audio file and I'm going to\nextract something from it,\nI want to get a segment of the video.\nI don't want the whole thing or I want to chop\noff the start and end, something like that.\nNow, there's a limit to how much,\nhow big a files you can upload,\nbut you can do pretty sophisticated things\nand you can get away with a lot.\nI'm going to say just go and display\nthese 10 images so I can get\na sense of what the images look\nlike that it pulled out and it generates\nthis nice graphic displaying\nthe 10 frames that is extracted,\nand we can look at\neach one of them and see what they are.\nNow, what I've decided I want to\ndo is something that you've seen a lot on the Internet.\nIt's a fun little thing to go and do,\nand that is I want to go and resize these things,\nmodify them some,\nand turn them into a animated GIF.\nI'm going to start off by just telling it,\nnow if you imagine you have like\n100 images you need to resize,\nthis is super effective.\nI'm just going to say resize each image,\nmaintain the aspect ratio to 300 pixels wide.\nIf you have a bunch of operations,\nyou want it to apply\nto an image and you don't know how to\ndescribe it and texexplanation_study_pack — 2026-07-09
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"sourceText": "Wenn Sie hören, dass Code-Interpreter die Art\nund Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten,\nrevolutionieren, liegt das daran, dass der Codeinterpreter\nwirklich gut mit strukturierten Daten funktioniert. Ich habe also darüber gesprochen, dass es\nviel einfacher ist, mit strukturierten Daten, wenn man sie hat, im Codeinterpreter zu\narbeiten. Und das möchte ich dir zeigen. Und die Arbeit mit strukturierten Daten ist\neiner der ersten Orte, an denen Sie beginnen sollten. Wenn Sie über strukturierte Daten\nverfügen, verwenden Sie einfach die Daten, die Sie haben. Stellen Sie natürlich sicher, dass es in\nOrdnung ist, mit diesen Daten im Rahmen Ihrer Datenschutz- und anderer Richtlinien zu\narbeiten, aber wenn Sie sie haben, wenn Sie Beispieldaten haben, die für die Spalten\nund die Datentypen, mit denen Sie arbeiten,\nrepräsentativ sind, können Sie diese stattdessen verwenden. Sie können also\nDummy-Daten verwenden, um sich ein Bild davon zu machen, wie das alles funktioniert.\nSchauen wir uns nun ein Beispiel mit Vanderbilt-Daten an. In einem separaten Gespräch habe ich den\nJahresbericht von Vanderbilt genommen, mit dem Code-Interpreter viele wichtige Kennzahlen\naus diesem Bericht extrahiert und ihn als CSV-Datei gespeichert. Das ist hilfreich,\ndenn jetzt kann ich diese CSV-Datei wiederverwenden und immer wieder mit den\nstrukturierten Daten beginnen. Also, hier ist das erste Interaktionsmuster, zu dessen Verwendung ich Sie ermutigen werde, dieses\nKonversationsmuster, wenn Sie mit strukturierten Daten arbeiten. Bitte lesen\nund erläutern Sie die Struktur der Daten in diesem Dokument. Oder lesen und erläutern Sie bitte die\nStruktur der Daten. Ich werde zwei Dinge tun, erstens sage ich\nihm, er soll es lesen, es sich ansehen, aber ich möchte auch, dass es es erklärt. Nun, warum mache ich diese beiden Dinge\ngleichzeitig, wenn ich mit strukturierten Daten arbeite? Nun, ich möchte mich darauf verlassen können,\ndass beim Laden dieser Daten erstens der richtige Datensatz geladen wird und zweitens,\ndass die Struktur, die es im Datensatz wahrnimmt, dieselbe Struktur\nist, die ich im Datensatz wahrnehme. Ich versuche unter anderem sicherzustellen,\ndass der Codeinterpreter und ich auf derselben Wellenlänge sind. Schauen wir uns dieselben Daten an? Hat es dieselbe Struktur, die wir erwarten? Ist der Codeinterpreter in der Lage, ihn\nkorrekt zu analysieren und die Struktur daraus herauszuholen? Und das wird uns helfen, diese Frage zu\nbeantworten. Also, zuerst das Muster, lies und erkläre die\nStruktur. Jetzt geht es durch und es heißt, hier ist\ndie Struktur. Es berichtet über verschiedene Kennzahlen für\ndie Vanderbilt University im Laufe der Jahre. In dieser Spalte wird diexplanation_study_pack — 2026-07-09
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"sourceText": "Let's have some fun with code interpreter\nby learning to work with small documents and\nwhat document could be more fun every\nyear to work with than the IRS 1040,\nthat if you're an American citizen,\nyou have to fill out on your taxes?\nIt's possibly the most fun document\nthat I ever worked with in my life.\nOf course, this is completely not true.\nIt's the document that I absolutely dread dealing with.\nUsually, I'm trying to avoid it all year long because\nit's a complicated mess of a document.\nIt's got tons and tons of questions.\nIt's dense, it's hard to read.\nIt's got all these rules.\nI always feel like I'm lost in what I'm doing.\nLet's go and do just a simple chat\nabout how we might be able to\nuse code interpreter to help us with the 1040.\nNow, I'm going to go ahead and state up front,\nI would not use a code interpreter to do your taxes.\nIt can make mistakes.\nYou would want to go use an accountant,\nsomebody who knows what they are doing.\nThis is not tax advice.\nThis is just an example of how you can\ntake a document that is small\nenough to fit into code interpreter and\nread and reason about\nhow you can go and interact with it.\nEven if that document is really complex.\nThat's the point of this is this is a\ncomplex, really dense document,\nbut it happens to fit into essentially a single message.\nNow I'm not going to go and copy and paste,\nbut let's take a look at this document.\nI encourage you to go and find\nsome document of yours that is preferably in plain text,\nbut it could be PDF and there are some tricks on that.\nBut get started and go play\nwith a document and start asking questions\nand discovering the limits\nand the things that does really well.\nI'm going to start off by doing\na basic pattern that you're going to\nsee over and over when I'm working\nwith PDFs or other documents.\nI'm going to say extract\nthis document into plain text and\nthen read the document and tell\nme all of the pieces of information to the question.\nBut the key pattern that I'm doing as I'm saying,\nextract this document to\nplain text and then read the document.\nIf I got a document that sits in\nand can be fed into one single chat message.\nThis is how I'm going to approach this.\nI'm just going to say extract\nit to plain text to read it.\nSometimes I'll just say read it,\nbut extract and then\nread tends to work better if you're working\nwith PDFs because sometimes\ncode interpreter will come back and tell you,\nhey, I can't read PDFs.\nBut if you tell it, extract it to\nplain text and then read the plain text,\nthat works on a lot of file formats.\nIt's a pretty effective way of doing no,\nyou don't always want to extract it that way.\nSometimes thexplanation_study_pack — 2026-07-09
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"text": "Concept: (H) Help Provide a Safety Net | Coursera:\n\nKernidee: Concept: (H) Help Provide a Safety Net | Coursera\n\nSummary:\nFocus van deze les:\nCode interpreter can help us provide a safety net so\n\nWat je echt moet begrijpen:\nthat we can catch mistakes by humans before they go out and cause a problem\n\nHoe je dit stap voor stap toepast:\n1.\n\nWaarom dit belangrijk is: De uitleg opent met Code interpreter can help us provide a safety net so.\n\nWat je vooral moet onthouden: Key points:\n- Code interpreter can help us provide a safety net so\n- Wat je echt moet begrijpen:\n- that we can catch mistakes by humans before they go out and cause a problem\n- Hoe je dit stap voor stap toepast:\n- 1."
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"back": "Focus van deze les:"
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"front": "Herinnering 6",
"back": "that we can catch mistakes by humans before they go out and cause a problem"
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"front": "Herinnering 7",
"back": "Hoe je dit stap voor stap toepast:"
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"front": "Herinnering 8",
"back": "1. De uitleg opent met Code interpreter can help us provide a safety net so."
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"sourceText": "Code interpreter can help us provide a safety net so\nthat we can catch mistakes by humans before they go out and cause a problem.\nSo let me give you an example of what I mean by this.\nWe want to try to catch things that would be hard to catch other ways or\nthat would require some human review to catch.\nSo we want to provide an extra safety net.\nWe'll probably always have humans reviewing things, but\nin addition to the humans that are reviewing things, maybe we want to have\nan additional check to make sure nothing accidentally gets out or\naccidentally is done that causes some negative impact.\nAnd so this is a second check for a human.\nSo I'm going to give you an example.\nI'm going to upload the Vanderbilt Travel and\nBusiness Expense policy as a PDF, and I'm going to upload an invoice.\nAnd you can imagine now we could also do this in the case where we could go and\nupload it before we purchased it would be an even better example.\nBut let's imagine we've purchased this and we want to check for\ncompliance on expenses.\nMake sure that something gets approved,\ndoesn't get approved accidentally that shouldn't have been approved, or\nthat even better would be that when we're submitting our travel expense report\nbefore we submit it, we're told, what are the things that might be wrong with it so\nwe can go and fix them, so we don't have a lot of back and forth.\nAll of these types of things that help prevent humans from making mistakes\nby catching them earlier or checking for things that would have been\nsomething that would have been missed by another human checking it.\nSo it goes through and it reads the Vanderbilt Travel and Expense policy,\nsummarizes it, and I go through with it to read and\nsummarize all of the expense policies as we go through here.\nAnd then at the end of this, what it's basically done is it's read into\nthe conversation all of the relevant details.\nSo then I ask it to read the provided receipt and\ntell me if it complies with travel policy.\nDon't list what is on the receipt, just if it complies why or why not.\nNow, what's interesting is this actually isn't a travel receipt,\nwhich I didn't notice my prompt, but it smartly detects that this is a receipt and\nlooks at the relevant policies.\nSo based on the policy, it lists what it is, it extracts what this receipt is for,\nand it says here's an analysis of compliance with the relative sections.\nAnd it says business purpose.\nThe receipt does not indicate a specific business purpose or\napproval from a supervisor.\nWithout additional context or information,\nit's unclear whether this expense aligns with the university's business needs.\nAnd it turns out if I submitted this thing without the business purpose,\nit wouexplanation_study_pack — 2026-07-09
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"back": "Focus van deze les:"
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"back": "Part 1/4] Code Interpreter is really useful for working with media. If you have videos, if you have audio files, if you have collections of images, working with all of them, you..."
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"front": "Herinnering 5",
"back": "Key points:"
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"front": "Herinnering 6",
"back": "Part 1/4] Code Interpreter is really useful for working with media. If you have videos, if you have audio files, if you have collections of images, working with all of them, you..."
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"front": "Herinnering 7",
"back": "Wat je echt moet begrijpen:"
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"front": "Herinnering 8",
"back": "Part 2/4] I'm giving it a pattern to follow on each individual one. I'm saying resize each, and then I'm giving it to constraints what I want. This is going to be a pattern that..."
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"front": "Herinnering 9",
"back": "Hoe je dit stap voor stap toepast:"
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"front": "Herinnering 10",
"back": "1. De uitleg opent met [Part 1/4] Code Interpreter is really useful for working with media.. Dat zet het vertrekpunt van de redenering neer."
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"sourceText": "Code Interpreter is really useful for working with media.\nIf you have videos,\nif you have audio files,\nif you have collections of images,\nworking with all of them,\nyou can do a lot of things really quickly.\nYou can absolutely go into a program and try to create\nsome batch process or try to\nmanually do things in one of these tools,\nbut Code Interpreter is super powerful for going\nand exploring media and creating new types of idea.\nSo I'm going to give you an example of this.\nI've taken a video of my son biking.\nHe loves to go and do BMX racing\nand do dirt jumps and all kinds of interesting things.\nI've uploaded this video and I'm going to\ndo a simple extraction.\nI'm going to take 10 frames out of this video,\nand then I'm going to do some things with them.\nYou can go and play around with media\nand do all kinds of interesting things.\nI encourage you, after you do this,\ntake an image, upload it,\nand try experimenting with\ndoing different operations on the image.\nI'm just going to go and say extract 10 frames\nfrom this video evenly spaced apart.\nThis is something I would probably have to\ngo and look up some command line tool.\nI know the tool I would use and figure out\nexactly the commands to do this from the command line.\nBut it's a lot more fun and easier\nto just do this with Code Interpreter.\nIt goes and generates the code\nto go and extract the 10 images,\nand this is super useful.\nNow I have the 10 images and I can download each\nof them right here and go and work with them.\nThat in itself is a super useful thing.\nIf I have some image,\nor I have some movie,\nor some audio file and I'm going to\nextract something from it,\nI want to get a segment of the video.\nI don't want the whole thing or I want to chop\noff the start and end, something like that.\nNow, there's a limit to how much,\nhow big a files you can upload,\nbut you can do pretty sophisticated things\nand you can get away with a lot.\nI'm going to say just go and display\nthese 10 images so I can get\na sense of what the images look\nlike that it pulled out and it generates\nthis nice graphic displaying\nthe 10 frames that is extracted,\nand we can look at\neach one of them and see what they are.\nNow, what I've decided I want to\ndo is something that you've seen a lot on the Internet.\nIt's a fun little thing to go and do,\nand that is I want to go and resize these things,\nmodify them some,\nand turn them into a animated GIF.\nI'm going to start off by just telling it,\nnow if you imagine you have like\n100 images you need to resize,\nthis is super effective.\nI'm just going to say resize each image,\nmaintain the aspect ratio to 300 pixels wide.\nIf you have a bunch of operations,\nyou want it to apply\nto an image and you don't know how to\ndescribe explanation_study_pack — 2026-07-09
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"Een detail dat minder belangrijk is dan het hoofdidee.",
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"sourceText": "Wenn Sie hören, dass Code-Interpreter die Art\nund Weise, wie Datenwissenschaft gelehrt wird und wie Datenwissenschaftler arbeiten,\nrevolutionieren, liegt das daran, dass der Codeinterpreter\nwirklich gut mit strukturierten Daten funktioniert. Ich habe also darüber gesprochen, dass es\nviel einfacher ist, mit strukturierten Daten, wenn man sie hat, im Codeinterpreter zu\narbeiten. Und das möchte ich dir zeigen. Und die Arbeit mit strukturierten Daten ist\neiner der ersten Orte, an denen Sie beginnen sollten. Wenn Sie über strukturierte Daten\nverfügen, verwenden Sie einfach die Daten, die Sie haben. Stellen Sie natürlich sicher, dass es in\nOrdnung ist, mit diesen Daten im Rahmen Ihrer Datenschutz- und anderer Richtlinien zu\narbeiten, aber wenn Sie sie haben, wenn Sie Beispieldaten haben, die für die Spalten\nund die Datentypen, mit denen Sie arbeiten,\nrepräsentativ sind, können Sie diese stattdessen verwenden. Sie können also\nDummy-Daten verwenden, um sich ein Bild davon zu machen, wie das alles funktioniert.\nSchauen wir uns nun ein Beispiel mit Vanderbilt-Daten an. In einem separaten Gespräch habe ich den\nJahresbericht von Vanderbilt genommen, mit dem Code-Interpreter viele wichtige Kennzahlen\naus diesem Bericht extrahiert und ihn als CSV-Datei gespeichert. Das ist hilfreich,\ndenn jetzt kann ich diese CSV-Datei wiederverwenden und immer wieder mit den\nstrukturierten Daten beginnen. Also, hier ist das erste Interaktionsmuster, zu dessen Verwendung ich Sie ermutigen werde, dieses\nKonversationsmuster, wenn Sie mit strukturierten Daten arbeiten. Bitte lesen\nund erläutern Sie die Struktur der Daten in diesem Dokument. Oder lesen und erläutern Sie bitte die\nStruktur der Daten. Ich werde zwei Dinge tun, erstens sage ich\nihm, er soll es lesen, es sich ansehen, aber ich möchte auch, dass es es erklärt. Nun, warum mache ich diese beiden Dinge\ngleichzeitig, wenn ich mit strukturierten Daten arbeite? Nun, ich möchte mich darauf verlassen können,\ndass beim Laden dieser Daten erstens der richtige Datensatz geladen wird und zweitens,\ndass die Struktur, die es im Datensatz wahrnimmt, dieselbe Struktur\nist, die ich im Datensatz wahrnehme. Ich versuche unter anderem sicherzustellen,\ndass der Codeinterpreter und ich auf derselben Wellenlänge sind. Schauen wir uns dieselben Daten an? Hat es dieselbe Struktur, die wir erwarten? Ist der Codeinterpreter in der Lage, ihn\nkorrekt zu analysieren und die Struktur daraus herauszuholen? Und das wird uns helfen, diese Frage zu\nbeantworten. Also, zuerst das Muster, lies und erkläre die\nStruktur. Jetzt geht es durch und es heißt, hier ist\ndie Struktur. Es berichtet über verschiedene Kennzahlen für\ndie Vanderbilt University im Laufe der Jahre. In dieser Spalte wird diexplanation_study_pack — 2026-07-09
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"sourceText": "Let's have some fun with code interpreter\nby learning to work with small documents and\nwhat document could be more fun every\nyear to work with than the IRS 1040,\nthat if you're an American citizen,\nyou have to fill out on your taxes?\nIt's possibly the most fun document\nthat I ever worked with in my life.\nOf course, this is completely not true.\nIt's the document that I absolutely dread dealing with.\nUsually, I'm trying to avoid it all year long because\nit's a complicated mess of a document.\nIt's got tons and tons of questions.\nIt's dense, it's hard to read.\nIt's got all these rules.\nI always feel like I'm lost in what I'm doing.\nLet's go and do just a simple chat\nabout how we might be able to\nuse code interpreter to help us with the 1040.\nNow, I'm going to go ahead and state up front,\nI would not use a code interpreter to do your taxes.\nIt can make mistakes.\nYou would want to go use an accountant,\nsomebody who knows what they are doing.\nThis is not tax advice.\nThis is just an example of how you can\ntake a document that is small\nenough to fit into code interpreter and\nread and reason about\nhow you can go and interact with it.\nEven if that document is really complex.\nThat's the point of this is this is a\ncomplex, really dense document,\nbut it happens to fit into essentially a single message.\nNow I'm not going to go and copy and paste,\nbut let's take a look at this document.\nI encourage you to go and find\nsome document of yours that is preferably in plain text,\nbut it could be PDF and there are some tricks on that.\nBut get started and go play\nwith a document and start asking questions\nand discovering the limits\nand the things that does really well.\nI'm going to start off by doing\na basic pattern that you're going to\nsee over and over when I'm working\nwith PDFs or other documents.\nI'm going to say extract\nthis document into plain text and\nthen read the document and tell\nme all of the pieces of information to the question.\nBut the key pattern that I'm doing as I'm saying,\nextract this document to\nplain text and then read the document.\nIf I got a document that sits in\nand can be fed into one single chat message.\nThis is how I'm going to approach this.\nI'm just going to say extract\nit to plain text to read it.\nSometimes I'll just say read it,\nbut extract and then\nread tends to work better if you're working\nwith PDFs because sometimes\ncode interpreter will come back and tell you,\nhey, I can't read PDFs.\nBut if you tell it, extract it to\nplain text and then read the plain text,\nthat works on a lot of file formats.\nIt's a pretty effective way of doing no,\nyou don't always want to extract it that way.\nSoflashcards — 2026-07-09
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"You will also be able to explain the fundamentals of prompt engineering.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
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"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
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"sourceText": "One of the most important things that you can do with code interpreter to be\neffective, particularly if you want to be effective in getting it to reason well.\nOr to write well or whatever it is is you want it to make sure that it has\neasy access to the knowledge that you're going to need it to reason on or\nuse as the basis of its writing, or\nto do whatever it is to filter, to transform whatever it is.\nNow, the easier it is for it to access that information,\nthe more successful you're going to be in using code interpreter.\nNow, what does it mean for it to be easy to access?\nIf you can see the information here in the conversation,\nyou are very likely going to be successful in whatever knowledge based\ntask you're trying to have GPT Four do through code interpreter.\nSo let me just repeat this, if you can see the information that it's going to\nbuild off of in the conversation, you're much more likely to be successful.\nThis is the simplest, easiest way to try to make sure that the reasoning is sound.\nNow, in this example, with the 1040 that I did a minute ago, I had it go and extract\na bunch of information and then I had it reread and extract additional information.\nAnd part of the reason for this was I didn't see the Identifiers for\nthe questions in the conversation.\nAnd I thought that was going to be something that I wanted based on the idea\nthat I was going to go and talk about different questions or\nask about what checkboxes.\nWell, if I'm going to refer to checkboxes, I'm probably going to need to know\nthe Identifiers form so I can go and reference back into the original form.\nNow, you don't have to do this, but it's often the easiest way to reason about what\ncode interpreter is doing and getting it to do the right thing is to get it to read\nthe information into the conversation.\nNow, there's other tricks that are used in the background to make this happen.\nNow, there's two places that the reading and rereading can happen.\nNow, I want to show you something interesting though, because I think this\nis really helpful to also show you why reading and rereading is powerful.\nSo in the first time that I said read this, we see it pop up this box\nthat's going and getting the information from Python, right?\nIt's using Python to read the PDF and extract the text and\nthen it shows the result here.\nSo one thing that's helpful to note here is it's actually brought\neverything into the conversation.\nNow, this is sort of a tricky thing to know, but the fact that it's read it and\nthen it's put it in here in this result, that means it's available and\nin the conversation, it's possible to have access to it.\nNow, it may not haexplanation_study_pack — 2026-07-09
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"Een detail dat minder belangrijk is dan het hoofdidee.",
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"Een detail dat minder belangrijk is dan het hoofdidee.",
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"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
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"sourceText": "I want to help you gauge the difficulty of\na task that you're about to start with Code Interpreter.\nNow, the reason for this is when you get started,\nyou're going to have all ideas\nof things that you can go and try.\nBut I want you to have a way of understanding\nhow difficult each of\nyour ideas are going to be to accomplish.\nIf you understand two basic things\nthat we're going to talk about,\nyou'll be much better off in terms of gauging\nthe difficulty of accomplishing a particular task.\nThose two things are,\nif you want to know how\ndifficult a particular task is going to be,\nwe want to go and look at\nthe document or data that we're going to work with.\nThe first question we want to ask is\nhow structured or unstructured is that data?\nStructured data is something like a table.\nIf you've got a bunch of tables that you're trying\nto read through and it's all in Excel format,\nthat's a structured format.\nThis is an example here where I'm uploading a CSV file,\nthat is a structured format.\nIt's like a table.\nIt's some very clearly marked format\nwhere you know what all the parts are.\nCSV files, Excel files,\nall of these types of things are very structured.\nIf you're working on data that\nhas a clear structure to it,\nit's going to be much easier to do your tasks.\nIf you're working on CSV files,\nExcel files, those types of things,\nand you're trying to do tasks that deal\nwith their current structure or transform them into\nnew structures that are relatively\nstraightforward like visualization is\nnot going to be too hard.\nLet me give you an example of this.\nI've got some data right here.\nThis is some information\nthat I've extracted from Vanderbilt's annual report.\nI've taken this data and I put it into a CSV file.\nIt's very well-structured.\nI go and insert the CSV file\ninto ChatGPT or Code Interpreter,\nand really quickly it can end up generating\na visualization with almost no effort on my part,\nbecause this is a really easy data set to\nwork with because it's very structured.\nNow, let's contrast that with\nan example where I took the same data but I\ndidn't start from a CSV file,\nI started from the PDF of Vanderbilt's report.\nI had to go and extract the data first.\nThis is what we see in the report.\nThere's all these different pages of data\nand the thing that I'm looking for\nis buried on one of the pages.\nIf you look at the text,\nit's structured maybe to a human,\nbut it's not very structured\nto a Python program\nor Code Interpreter or something else.\nIt's not in a great format\nonce you extract it from the PDF.\nIf you can read\nthe PDF and look at it visually, it's structured,\nbut if you actually look at the underlying text and\nthe structure of the text itself, it's nexplanation_study_pack — 2026-07-09
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"sourceText": "This next capability I'm going to talk about\nis really one that you can only\ntake from start to finish if you know how to\nprogram or you're a software engineer\nor computer scientist.\nBut you can use it in conjunction with somebody else\nwho is really good at those things\nif you aren't a programmer or computer scientist.\nIf you're an organization that has a bunch of\nprogrammers or you have a friend that it's a programmer,\nthis is a way that you could do that.\nNow what is this technique?\nIt's a very exciting one from\nthe perspective of software development.\nOne of the challenges that we have is\nwe have all these little tools\nthat we would like to have to help us out.\nAll day long when I'm working\nand thinking it'd be really nice to have a piece\nof software that did this and\nsimplified this process for me.\nSometimes as a software engineer,\nI'll go and take the time to actually\nwrite the software to do that.\nThe Code Interpreter creates\nan intriguing new possibility where we can\nactually turn a conversation\nwith Code Interpreter into software.\nNow, if you are not a programmer,\nif you can't read the code,\nyou should not go all\nthe way through with this because you have to\nbe able to look at the code\nand know if it's going to work correctly,\nif it's safe to run on your computer.\nIt could delete all the files for all you know,\nif you can't read the code.\nWe 100% know that\nlarge language models can make mistakes,\nand you need to pay attention to\nthe code that comes out of what I'm going to show you,\nand if you can't read it and understand it,\nyou should not proceed.\nYou will need to go find a programmer\nto help the rest of the way.\nBut a programmer at some point,\na human software developer,\nneeds to be involved in\nthe analysis for what I'm going to show you.\nHowever, it's really exciting.\nLet's take a look at what we've got.\nThis is an original conversation that I\nhad where I took a movie\nand I extracted 10 different frames from the movie,\nI then went and display the images.\nI looked at it, I'm having this whole conversation,\nI resize the images and I did a bunch of other things.\nI turned each image into grayscale,\nI increase the contrast by 30%,\nand I turned the images into an animated GIF.\nI also created a PowerPoint presentation\nwith one image per slide.\nNow I thought, wouldn't it be great if I could take\nthis whole process so that I can repeat it?\nNow, I've repeated exactly this type of process for\ncreating tools to help me with\nthe creation of these videos and cataloging them.\nFor example, automatically going and\ntaking my videos and measuring how long they are,\explanation_study_pack — 2026-07-09
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"front": "Herinnering 3",
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"back": "Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp... Wat je echt moet begrijpen:"
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"back": "Part 2/4] cases that you can then run completely separately from Code Interpreter and you c"
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"back": "Key points:"
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"front": "Herinnering 7",
"back": "Part 1/4] This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or comp..."
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"front": "Herinnering 8",
"back": "Wat je echt moet begrijpen:"
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"back": "Part 2/4] cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present..."
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"front": "Herinnering 10",
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"sourceText": "This next capability I'm going to talk about\nis really one that you can only\ntake from start to finish if you know how to\nprogram or you're a software engineer\nor computer scientist.\nBut you can use it in conjunction with somebody else\nwho is really good at those things\nif you aren't a programmer or computer scientist.\nIf you're an organization that has a bunch of\nprogrammers or you have a friend that it's a programmer,\nthis is a way that you could do that.\nNow what is this technique?\nIt's a very exciting one from\nthe perspective of software development.\nOne of the challenges that we have is\nwe have all these little tools\nthat we would like to have to help us out.\nAll day long when I'm working\nand thinking it'd be really nice to have a piece\nof software that did this and\nsimplified this process for me.\nSometimes as a software engineer,\nI'll go and take the time to actually\nwrite the software to do that.\nThe Code Interpreter creates\nan intriguing new possibility where we can\nactually turn a conversation\nwith Code Interpreter into software.\nNow, if you are not a programmer,\nif you can't read the code,\nyou should not go all\nthe way through with this because you have to\nbe able to look at the code\nand know if it's going to work correctly,\nif it's safe to run on your computer.\nIt could delete all the files for all you know,\nif you can't read the code.\nWe 100% know that\nlarge language models can make mistakes,\nand you need to pay attention to\nthe code that comes out of what I'm going to show you,\nand if you can't read it and understand it,\nyou should not proceed.\nYou will need to go find a programmer\nto help the rest of the way.\nBut a programmer at some point,\na human software developer,\nneeds to be involved in\nthe analysis for what I'm going to show you.\nHowever, it's really exciting.\nLet's take a look at what we've got.\nThis is an original conversation that I\nhad where I took a movie\nand I extracted 10 different frames from the movie,\nI then went and display the images.\nI looked at it, I'm having this whole conversation,\nI resize the images and I did a bunch of other things.\nI turned each image into grayscale,\nI increase the contrast by 30%,\nand I turned the images into an animated GIF.\nI also created a PowerPoint presentation\nwith one image per slide.\nNow I thought, wouldn't it be great if I could take\nthis whole process so that I can repeat it?\nNow, I've repeated exactly this type of process for\ncreating tools to help me with\nthe creation of these videos and cataloging them.\nFor example, automatically going and\ntaking my videos and measuring how long they areflashcards — 2026-07-09
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"You will also be able to explain the fundamentals of prompt engineering.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
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"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
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"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
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"sourceText": "One of the most important things that you can do with code interpreter to be\neffective, particularly if you want to be effective in getting it to reason well.\nOr to write well or whatever it is is you want it to make sure that it has\neasy access to the knowledge that you're going to need it to reason on or\nuse as the basis of its writing, or\nto do whatever it is to filter, to transform whatever it is.\nNow, the easier it is for it to access that information,\nthe more successful you're going to be in using code interpreter.\nNow, what does it mean for it to be easy to access?\nIf you can see the information here in the conversation,\nyou are very likely going to be successful in whatever knowledge based\ntask you're trying to have GPT Four do through code interpreter.\nSo let me just repeat this, if you can see the information that it's going to\nbuild off of in the conversation, you're much more likely to be successful.\nThis is the simplest, easiest way to try to make sure that the reasoning is sound.\nNow, in this example, with the 1040 that I did a minute ago, I had it go and extract\na bunch of information and then I had it reread and extract additional information.\nAnd part of the reason for this was I didn't see the Identifiers for\nthe questions in the conversation.\nAnd I thought that was going to be something that I wanted based on the idea\nthat I was going to go and talk about different questions or\nask about what checkboxes.\nWell, if I'm going to refer to checkboxes, I'm probably going to need to know\nthe Identifiers form so I can go and reference back into the original form.\nNow, you don't have to do this, but it's often the easiest way to reason about what\ncode interpreter is doing and getting it to do the right thing is to get it to read\nthe information into the conversation.\nNow, there's other tricks that are used in the background to make this happen.\nNow, there's two places that the reading and rereading can happen.\nNow, I want to show you something interesting though, because I think this\nis really helpful to also show you why reading and rereading is powerful.\nSo in the first time that I said read this, we see it pop up this box\nthat's going and getting the information from Python, right?\nIt's using Python to read the PDF and extract the text and\nthen it shows the result here.\nSo one thing that's helpful to note here is it's actually brought\neverything into the conversation.\nNow, this is sort of a tricky thing to know, but the fact that it's read it and\nthen it's put it in here in this result, that means it's available and\nin the conversation, it's possible to have access to it.\nNow, it may not have access to it all at once aexplanation_study_pack — 2026-07-09
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"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
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"sourceText": "This next capability I'm going to talk about\nis really one that you can only\ntake from start to finish if you know how to\nprogram or you're a software engineer\nor computer scientist.\nBut you can use it in conjunction with somebody else\nwho is really good at those things\nif you aren't a programmer or computer scientist.\nIf you're an organization that has a bunch of\nprogrammers or you have a friend that it's a programmer,\nthis is a way that you could do that.\nNow what is this technique?\nIt's a very exciting one from\nthe perspective of software development.\nOne of the challenges that we have is\nwe have all these little tools\nthat we would like to have to help us out.\nAll day long when I'm working\nand thinking it'd be really nice to have a piece\nof software that did this and\nsimplified this process for me.\nSometimes as a software engineer,\nI'll go and take the time to actually\nwrite the software to do that.\nThe Code Interpreter creates\nan intriguing new possibility where we can\nactually turn a conversation\nwith Code Interpreter into software.\nNow, if you are not a programmer,\nif you can't read the code,\nyou should not go all\nthe way through with this because you have to\nbe able to look at the code\nand know if it's going to work correctly,\nif it's safe to run on your computer.\nIt could delete all the files for all you know,\nif you can't read the code.\nWe 100% know that\nlarge language models can make mistakes,\nand you need to pay attention to\nthe code that comes out of what I'm going to show you,\nand if you can't read it and understand it,\nyou should not proceed.\nYou will need to go find a programmer\nto help the rest of the way.\nBut a programmer at some point,\na human software developer,\nneeds to be involved in\nthe analysis for what I'm going to show you.\nHowever, it's really exciting.\nLet's take a look at what we've got.\nThis is an original conversation that I\nhad where I took a movie\nand I extracted 10 different frames from the movie,\nI then went and display the images.\nI looked at it, I'm having this whole conversation,\nI resize the images and I did a bunch of other things.\nI turned each image into grayscale,\nI increase the contrast by 30%,\nand I turned the images into an animated GIF.\nI also created a PowerPoint presentation\nwith one image per slide.\nNow I thought, wouldn't it be great if I could take\nthis whole process so that I can repeat it?\nNow, I've repeated exactly this type of process for\ncreating tools to help me with\nthe creation of these videos and cataloging them.\nFor example, automatically going and\ntaking my videos and measuring how lonflashcards — 2026-07-09
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"back": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files"
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}quiz — 2026-07-09
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"prompt": "Welke kernles hoort het best bij punt 1?",
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"1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
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"After watching this video, you'll be able to describe in-context learning.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
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"answerIndex": 0,
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},
{
"prompt": "Welke kernles hoort het best bij punt 3?",
"choices": [
"You will also be able to explain the fundamentals of prompt engineering.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
},
{
"prompt": "Welke kernles hoort het best bij punt 4?",
"choices": [
"In-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language.",
"Een detail dat minder belangrijk is dan het hoofdidee.",
"Een oppervlakkige formulering die niet uitlegt waarom het werkt.",
"Een keuze die het concept verwart met iets anders."
],
"answerIndex": 0,
"explanation": "Het juiste antwoord is de keuze die het centrale idee of de belangrijkste toepassing het duidelijkst samenvat."
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"text": "1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive d:\n\nKernidee: 1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files\n\nWelcome to Introduction to In-context Learning.\n\nWaarom dit belangrijk is: After watching this video, you'll be able to describe in-context learning.\n\nWat je vooral moet onthouden: You will also be able to explain the fundamentals of prompt engineering."
}explanation_study_pack — 2026-07-09
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"back": "Wat je echt moet begrijpen:"
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"front": "Herinnering 8",
"back": "Part 2/5] ead, it did not go and run Python again. Now, there's two things that that could mean, and it's helpful to know this. One is the information that it needs to do this t..."
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"back": "4. Daarna laat de les zien hoe Now, what does it mean for it to be easy to access? het resultaat of de toepassing afrondt."
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"Een detail dat minder belangrijk is dan het hoofdidee.",
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"sourceText": "One of the most important things that you can do with code interpreter to be\neffective, particularly if you want to be effective in getting it to reason well.\nOr to write well or whatever it is is you want it to make sure that it has\neasy access to the knowledge that you're going to need it to reason on or\nuse as the basis of its writing, or\nto do whatever it is to filter, to transform whatever it is.\nNow, the easier it is for it to access that information,\nthe more successful you're going to be in using code interpreter.\nNow, what does it mean for it to be easy to access?\nIf you can see the information here in the conversation,\nyou are very likely going to be successful in whatever knowledge based\ntask you're trying to have GPT Four do through code interpreter.\nSo let me just repeat this, if you can see the information that it's going to\nbuild off of in the conversation, you're much more likely to be successful.\nThis is the simplest, easiest way to try to make sure that the reasoning is sound.\nNow, in this example, with the 1040 that I did a minute ago, I had it go and extract\na bunch of information and then I had it reread and extract additional information.\nAnd part of the reason for this was I didn't see the Identifiers for\nthe questions in the conversation.\nAnd I thought that was going to be something that I wanted based on the idea\nthat I was going to go and talk about different questions or\nask about what checkboxes.\nWell, if I'm going to refer to checkboxes, I'm probably going to need to know\nthe Identifiers form so I can go and reference back into the original form.\nNow, you don't have to do this, but it's often the easiest way to reason about what\ncode interpreter is doing and getting it to do the right thing is to get it to read\nthe information into the conversation.\nNow, there's other tricks that are used in the background to make this happen.\nNow, there's two places that the reading and rereading can happen.\nNow, I want to show you something interesting though, because I think this\nis really helpful to also show you why reading and rereading is powerful.\nSo in the first time that I said read this, we see it pop up this box\nthat's going and getting the information from Python, right?\nIt's using Python to read the PDF and extract the text and\nthen it shows the result here.\nSo one thing that's helpful to note here is it's actually brought\neverything into the conversation.\nNow, this is sort of a tricky thing to know, but the fact that it's read it and\nthen it's put it in here in this result, that means it's available and\nin the conversation, it's possible to have access to it.\nNow, it explanation_study_pack — 2026-07-09
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"front": "Herinnering 3",
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"front": "Herinnering 5",
"back": "Wat je echt moet begrijpen:"
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"front": "Herinnering 6",
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"front": "Herinnering 7",
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"Een detail dat minder belangrijk is dan het hoofdidee.",
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"Een keuze die het concept verwart met iets anders."
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"sourceText": "I want to help you gauge the difficulty of\na task that you're about to start with Code Interpreter.\nNow, the reason for this is when you get started,\nyou're going to have all ideas\nof things that you can go and try.\nBut I want you to have a way of understanding\nhow difficult each of\nyour ideas are going to be to accomplish.\nIf you understand two basic things\nthat we're going to talk about,\nyou'll be much better off in terms of gauging\nthe difficulty of accomplishing a particular task.\nThose two things are,\nif you want to know how\ndifficult a particular task is going to be,\nwe want to go and look at\nthe document or data that we're going to work with.\nThe first question we want to ask is\nhow structured or unstructured is that data?\nStructured data is something like a table.\nIf you've got a bunch of tables that you're trying\nto read through and it's all in Excel format,\nthat's a structured format.\nThis is an example here where I'm uploading a CSV file,\nthat is a structured format.\nIt's like a table.\nIt's some very clearly marked format\nwhere you know what all the parts are.\nCSV files, Excel files,\nall of these types of things are very structured.\nIf you're working on data that\nhas a clear structure to it,\nit's going to be much easier to do your tasks.\nIf you're working on CSV files,\nExcel files, those types of things,\nand you're trying to do tasks that deal\nwith their current structure or transform them into\nnew structures that are relatively\nstraightforward like visualization is\nnot going to be too hard.\nLet me give you an example of this.\nI've got some data right here.\nThis is some information\nthat I've extracted from Vanderbilt's annual report.\nI've taken this data and I put it into a CSV file.\nIt's very well-structured.\nI go and insert the CSV file\ninto ChatGPT or Code Interpreter,\nand really quickly it can end up generating\na visualization with almost no effort on my part,\nbecause this is a really easy data set to\nwork with because it's very structured.\nNow, let's contrast that with\nan example where I took the same data but I\ndidn't start from a CSV file,\nI started from the PDF of Vanderbilt's report.\nI had to go and extract the data first.\nThis is what we see in the report.\nThere's all these different pages of data\nand the thing that I'm looking for\nis buried on one of the pages.\nIf you look at the text,\nit's structured maybe to a human,\nbut it's not very structured\nto a Python program\nor Code Interpreter or something else.\nIt's not in a great format\nonce you extract it from the PDF.\nIf you can read\nthe PDF and look at it visually, it's structured,\nbut if you actually look at the underlying text and\nthe structure of the text itself, it'sexplanation_study_pack — 2026-07-09
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"sourceText": "I want to help you gauge the difficulty of\na task that you're about to start with Code Interpreter.\nNow, the reason for this is when you get started,\nyou're going to have all ideas\nof things that you can go and try.\nBut I want you to have a way of understanding\nhow difficult each of\nyour ideas are going to be to accomplish.\nIf you understand two basic things\nthat we're going to talk about,\nyou'll be much better off in terms of gauging\nthe difficulty of accomplishing a particular task.\nThose two things are,\nif you want to know how\ndifficult a particular task is going to be,\nwe want to go and look at\nthe document or data that we're going to work with.\nThe first question we want to ask is\nhow structured or unstructured is that data?\nStructured data is something like a table.\nIf you've got a bunch of tables that you're trying\nto read through and it's all in Excel format,\nthat's a structured format.\nThis is an example here where I'm uploading a CSV file,\nthat is a structured format.\nIt's like a table.\nIt's some very clearly marked format\nwhere you know what all the parts are.\nCSV files, Excel files,\nall of these types of things are very structured.\nIf you're working on data that\nhas a clear structure to it,\nit's going to be much easier to do your tasks.\nIf you're working on CSV files,\nExcel files, those types of things,\nand you're trying to do tasks that deal\nwith their current structure or transform them into\nnew structures that are relatively\nstraightforward like visualization is\nnot going to be too hard.\nLet me give you an example of this.\nI've got some data right here.\nThis is some information\nthat I've extracted from Vanderbilt's annual report.\nI've taken this data and I put it into a CSV file.\nIt's very well-structured.\nI go and insert the CSV file\ninto ChatGPT or Code Interpreter,\nand really quickly it can end up generating\na visualization with almost no effort on my part,\nbecause this is a really easy data set to\nwork with because it's very structured.\nNow, let's contrast that with\nan example where I took the same data but I\ndidn't start from a CSV file,\nI started from the PDF of Vanderbilt's report.\nI had to go and extract the data first.\nThis is what we see in the report.\nThere's all these different pages of data\nand the thing that I'm looking for\nis buried on one of the pages.\nIf you look at the text,\nit's structured maybe to a human,\nbut it's not very structured\nto a Python program\nor Code Interpreter or something else.\nIt's not in a great format\nonce you extract it from the PDF.\nIf you can read\nthe PDF and look at it visually, it's structured,\nbut if you actually look at the underlying text and\nthe structure of the text itself, it'sflashcards — 2026-07-09
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"sourceText": "We can always go and upload individual files and download individual files to\ncode interpreter, and we can certainly get by that way.\nBut one of the wonderful things about code interpreter and the way that it works is\nthat it can actually take zip files, unzip them, work on multiple files at once, and\nautomate entire processes for us, and then zip up the results and\ngive us back multiple results at once, which is way more convenient.\nIt also allows us to do interesting things, like when you have a zip file,\nit can actually have a folder hierarchy inside of it.\nOr we can give additional things or tools or other possibilities,\nbits of python code, anything we want to do to code interpreter.\nBut I'm going to give you a simple example of why an archive is so helpful.\nLet's imagine that you have a series of images, and you want to go and\napply a transformation to them.\nYou want to make them much more stylized, apply some filter to them.\nYou've taken all these images, okay?\nNow, you want to stylize them.\nSo, I'm going to upload an archive full of images to code interpreter.\nBut this could be an archive of any of your files.\nIt could be an archive full of Excel files that you want to combine.\nIt might be an archive full of Excel files, and\nyou want a specific visualization built for every single individual Excel file.\nOr maybe you want to search across those Excel files and filter them for\na subset of rows and then create new Excel files, whatever automation you want to do.\nSo whenever you start thinking about I have multiple files and\nI need to automate some process across them,\nyou want to start thinking about uploading a zip file to code interpreter.\nZip everything up that you're going to perform the operation on, describe\nthe operation and tell it to perform it on the files that are within the zip file.\nSimple automation, super effective.\nSo, I'm going to upload this archive full of images, and I'm going to say,\nplease make these images much more stylized by dramatically\nincreasing the contrast and saturation.\nSo, simple pattern.\nWhenever you need to automate something on a set of files,\nzip them up into an archive and upload the zip.\nIt's basically the archive interaction pattern.\nSo now, what it's going to do is it's going to say, first thing,\nlet's unzip the archive to access the images.\nIt then pulls out each of the individual JPEGs, which were JPEGs that I'd\npulled out of a movie, it then goes through and access provides each of them.\nNow, I could stop and I could download them one by one at this point, but\nthat seems like a lot of work for me, particularly if I've got 100 files or\n200 files.\nSo, instead what I'm going to do is I'm just going to say zip up the files so\nthat I can downexplanation_study_pack — 2026-07-09
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"sourceText": "Picture this. You walk into a room and the light turns on automatically. Now picture\nasking your smart assistant to plan a movie night. Both systems are reacting, but very\ndifferently. One's fast and simple. The other thinks before it acts. That's the difference\nbetween reactive and deliberative agents. Reactive agents respond immediately to changes\nin their environment. There's no deep reasoning. They sense and act. Think of a thermostat\nadjusting heat, a car's collision detection system, a robot vacuum avoiding a chair. They\nare reliable, fast and often operate in real-time. But they don't plan or learn. They're great\nwhen the response is obvious and the goal is speed. Deliberative agents take input,\nreason and then decide on a course of action. This can involve evaluating options, forecasting\noutcomes or balancing priorities. Let's go back to that movie night example. An agent\nlike Amazon Alexa might check your calendar, suggest available times, dim the lights, queue\nup your streaming app. These agents need internal memory, decision trees, maybe even a task\nmanager and they are ideal when the task has multiple steps or variables. So it's\nnot about which is better. It's about choosing the right tool for the right task. By the\nend of this video, you will be able to differentiate between reactive, deliberative and hybrid\nagent behaviors. Identify real-world examples of when each agent type is most effective.\nUnderstand the trade-offs between speed, planning and adaptability in agent design. Recognize\nhow hybrid agents combine fast reactions with strategic planning. Apply these concepts to\nselect the right agent behavior model for specific use cases. Some agents combine both\napproaches. A hybrid agent can react in real-time but also make long-term decisions. Think of\na self-driving car. It reacts instantly to a pedestrian but also follows a route, reroutes\naround traffic and estimates arrival time. That's reactive plus deliberative working\ntogether. Whether it's turning on a light or planning your week, AI agents work in different\nways to get things done. And as a designer, you will need to know when to keep it simple\nand when to let your agent think. Before we move on, take a moment to reflect and answer\nthe quick question on your screen. In this video, you learn. Reactive agents act instantly\nwithout thinking. Great for simple, fast tasks. Deliberative agents reason before acting.\nIdeal for multi-step decisions. Hybrid agents combine quick reactions with planned actions.\nEach agent type suits different task needs. Speed was a strategy. Designers muexplanation_study_pack — 2026-07-09
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"sourceText": "Have you ever wondered how a self-driving car knows when to slow down or how a voice\nassistant knows when you're asking a question versus just thinking out loud?\nMost developers find themselves asking this exact question when they first start working\nwith perception systems.\nThat first step, where the system notices something, is called perception.\nAnd for AI agents, it's where intelligence begins.\nBy the end of this video, you will be able to define perception in the context of AI\nagents, identify different types of perception inputs AI agents use, and explain why accurate\nperception is critical for agent behavior.\nPerception in AI agents is just like it is in humans.\nIt is how they experience the world.\nFor us, it's sight, sound, touch.\nFor agents, it's things like sensor data, API responses, user commands, or telemetry\nlogs.\nOn one of his early projects, a senior developer recalls working with an agent processing industrial\nmachine data raw sensor readings coming in by the second.\nThe challenge wasn't getting the data.\nIt was helping the agent understand which inputs actually mattered.\nFor example, a customer service bot receives type text.\nA warehouse robot scans QR codes.\nA home assistant hears your voice.\nThese are all ways agents perceive what's happening around them.\nBut perception isn't just about collecting data.\nIt's about making sense of it.\nAn agent has to filter out noise, extract relevant signals, and turn messy inputs into\nusable insights.\nThink of a smart traffic light.\nIt doesn't just detect motion.\nIt classifies.\nIs that a car?\nA person?\nIs it safe to switch signals?\nOne developer reported an instance of a breakdown in a simulation where the agent mistook a\ncyclist for a pedestrian.\nAnd that tiny misclassification completely changed its behavior.\nThat's the power and risk of perception.\nOne great example is how Amazon Alexa handles a request like plan a movie night.\nIt doesn't just trigger a single response.\nIt recognizes layered intent.\nDim the lights, check your calendar, maybe even order snacks.\nThat kind of perception enables multi-step contextual action.\nDifferent types of agents use perception in different ways.\nReactive agents respond instantly, like flipping a switch when motion is detected.\nReactive agents perceive, pause, plan, and then act.\nHybrid agents combine both, responding quickly but also adapting based on new information.\nHybrid agents are especially powerful.\nThey give you flexibility, quick response when you need it, and thoughtful behavior\nwhen it counts.\nBut it all depends on what the agent can perceive and how well it interpflashcards — 2026-06-27
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"sourceText": "Information is a summary of the raw data. For example, positive or negative results that happen after some specific change. And, insights are conclusions based on the results of information analysis. Meaningful business decisions are based on insights. For example, If a positive trend occurs after store hours are changed, the right business decision would be to maintain those new hours. Intellectual property (or IP) refers to creations of the mind and generally are not tangible. It's often protected by copyright, trademark, and patent law.\nIndustrial designs, trade secrets, and research discoveries are all examples of IP. Even some employee knowledge is considered intellectual property. Companies use a legally binding document called a Non-Disclosure Agreement (or an NDA) to prevent the sharing of sensitive information. Digital products are non-tangible assets a company owns. Examples include software, online music, online courses, e-Books or audiobooks, and web elements like WordPress or Shopify themes. A company must protect digital products from piracy and reverse-engineering. Source codes, licenses, and activation keys also need protection from hackers and insider threats.\nHere are the differences between them. Personally Identifiable Information (PII) is information that identifies a person. Personal Customer Information (PCI) is information that identifies and describes a customer. It includes much of the same types of data as PII. Like name, address, contact information, account login, and demographics. It can also include descriptive data like age, gender, job title, and marital status. Sensitive Personal Information (SPI) is information that does not identify but can cause harm if made public.",
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2026-08-03T05:35:18.476Z — transcript_dom — Making the MCP Decision: Evaluation Framework for AI Integration | Coursera — 3060 chars
Last month, Louise worked with a healthcare company facing a critical decision. Their AI diagnostic system needed to connect with 15 different data sources, patient records, lab results, imaging systems, drug databases. Their current approach was failing, costs were escalating, and their go-to-market timeline was at risk. Switching their entire integration strategy to MCP required a systematic evaluation framework. The company had to evaluate how many different data sources does the AI system need to connect with. If it was more than three, MCP's standardization benefit would become compelling. If the company was building point-to-point connections, traditional approaches might suffice. But if the company was planning to build an AI ecosystem, MCP would be essential. MCP provides built-in security models, but you need to evaluate if they meet your specific requirements. For healthcare, finance, or government applications, MCP's standardized security approach often exceeds what custom solutions provide. For internal tools with simple security needs, the overhead might not be justified. MCP requires understanding of protocol-based development. If your team is comfortable with REST APIs and JSON protocols, the learning curve is manageable. If they're used to direct database connections or simple file processing, expect a steeper learning curve. MCP has upfront learning costs but reduces long-term maintenance. If you need a quick proof of concept, custom solutions might be faster initially. If you're building for production scale, MCP's standardization pays dividends over time. Check if your target data sources already have MCP servers available. GitHub, popular databases, and major SaaS platforms increasingly support MCP. If your data sources lack MCP support, you'll need to build servers yourself, which changes the cost-benefit analysis. Ready to check your understanding? Go ahead and answer the question that follows. For that healthcare company, the evaluation was clear. 15 data sources, strict security requirements, experienced API team, and long-term production needs. MCP was the right choice. They're now in production, integration time dropped 70%, and security audits are streamlined. Score ease factor on a scale of 1 to 5 for MCP suitability. If your total score is above 15, MCP is likely a good fit. Below 10, consider simpler approaches. Between 10 and 15, evaluate specific factors more deeply. In this video, you learned systematic evaluation considers integration complexity, security requirements, team capabilities, and timeline constraints. MCP provides the most value for AI ecosystems connecting to multiple data sources. Organizations with scores above 15 on the evaluation framework typically see strong MCP benefits, and consider both upfront learning costs and long-term maintenance benefits in your decision. You now have the framework to evaluate MCP for your specific needs, but evaluation is just the beginning. To truly understand MCP's potential, you need to see with a real integration scenario.
2026-08-03T05:35:16.468Z — transcript_dom — Making the MCP Decision: Evaluation Framework for AI Integration | Coursera — 3060 chars
Last month, Louise worked with a healthcare company facing a critical decision. Their AI diagnostic system needed to connect with 15 different data sources, patient records, lab results, imaging systems, drug databases. Their current approach was failing, costs were escalating, and their go-to-market timeline was at risk. Switching their entire integration strategy to MCP required a systematic evaluation framework. The company had to evaluate how many different data sources does the AI system need to connect with. If it was more than three, MCP's standardization benefit would become compelling. If the company was building point-to-point connections, traditional approaches might suffice. But if the company was planning to build an AI ecosystem, MCP would be essential. MCP provides built-in security models, but you need to evaluate if they meet your specific requirements. For healthcare, finance, or government applications, MCP's standardized security approach often exceeds what custom solutions provide. For internal tools with simple security needs, the overhead might not be justified. MCP requires understanding of protocol-based development. If your team is comfortable with REST APIs and JSON protocols, the learning curve is manageable. If they're used to direct database connections or simple file processing, expect a steeper learning curve. MCP has upfront learning costs but reduces long-term maintenance. If you need a quick proof of concept, custom solutions might be faster initially. If you're building for production scale, MCP's standardization pays dividends over time. Check if your target data sources already have MCP servers available. GitHub, popular databases, and major SaaS platforms increasingly support MCP. If your data sources lack MCP support, you'll need to build servers yourself, which changes the cost-benefit analysis. Ready to check your understanding? Go ahead and answer the question that follows. For that healthcare company, the evaluation was clear. 15 data sources, strict security requirements, experienced API team, and long-term production needs. MCP was the right choice. They're now in production, integration time dropped 70%, and security audits are streamlined. Score ease factor on a scale of 1 to 5 for MCP suitability. If your total score is above 15, MCP is likely a good fit. Below 10, consider simpler approaches. Between 10 and 15, evaluate specific factors more deeply. In this video, you learned systematic evaluation considers integration complexity, security requirements, team capabilities, and timeline constraints. MCP provides the most value for AI ecosystems connecting to multiple data sources. Organizations with scores above 15 on the evaluation framework typically see strong MCP benefits, and consider both upfront learning costs and long-term maintenance benefits in your decision. You now have the framework to evaluate MCP for your specific needs, but evaluation is just the beginning. To truly understand MCP's potential, you need to see with a real integration scenario.
2026-08-03T05:35:14.462Z — transcript_dom — Making the MCP Decision: Evaluation Framework for AI Integration | Coursera — 3060 chars
Last month, Louise worked with a healthcare company facing a critical decision. Their AI diagnostic system needed to connect with 15 different data sources, patient records, lab results, imaging systems, drug databases. Their current approach was failing, costs were escalating, and their go-to-market timeline was at risk. Switching their entire integration strategy to MCP required a systematic evaluation framework. The company had to evaluate how many different data sources does the AI system need to connect with. If it was more than three, MCP's standardization benefit would become compelling. If the company was building point-to-point connections, traditional approaches might suffice. But if the company was planning to build an AI ecosystem, MCP would be essential. MCP provides built-in security models, but you need to evaluate if they meet your specific requirements. For healthcare, finance, or government applications, MCP's standardized security approach often exceeds what custom solutions provide. For internal tools with simple security needs, the overhead might not be justified. MCP requires understanding of protocol-based development. If your team is comfortable with REST APIs and JSON protocols, the learning curve is manageable. If they're used to direct database connections or simple file processing, expect a steeper learning curve. MCP has upfront learning costs but reduces long-term maintenance. If you need a quick proof of concept, custom solutions might be faster initially. If you're building for production scale, MCP's standardization pays dividends over time. Check if your target data sources already have MCP servers available. GitHub, popular databases, and major SaaS platforms increasingly support MCP. If your data sources lack MCP support, you'll need to build servers yourself, which changes the cost-benefit analysis. Ready to check your understanding? Go ahead and answer the question that follows. For that healthcare company, the evaluation was clear. 15 data sources, strict security requirements, experienced API team, and long-term production needs. MCP was the right choice. They're now in production, integration time dropped 70%, and security audits are streamlined. Score ease factor on a scale of 1 to 5 for MCP suitability. If your total score is above 15, MCP is likely a good fit. Below 10, consider simpler approaches. Between 10 and 15, evaluate specific factors more deeply. In this video, you learned systematic evaluation considers integration complexity, security requirements, team capabilities, and timeline constraints. MCP provides the most value for AI ecosystems connecting to multiple data sources. Organizations with scores above 15 on the evaluation framework typically see strong MCP benefits, and consider both upfront learning costs and long-term maintenance benefits in your decision. You now have the framework to evaluate MCP for your specific needs, but evaluation is just the beginning. To truly understand MCP's potential, you need to see with a real integration scenario.
2026-08-03T05:35:10.731Z — transcript_dom — Making the MCP Decision: Evaluation Framework for AI Integration | Coursera — 3060 chars
Last month, Louise worked with a healthcare company facing a critical decision. Their AI diagnostic system needed to connect with 15 different data sources, patient records, lab results, imaging systems, drug databases. Their current approach was failing, costs were escalating, and their go-to-market timeline was at risk. Switching their entire integration strategy to MCP required a systematic evaluation framework. The company had to evaluate how many different data sources does the AI system need to connect with. If it was more than three, MCP's standardization benefit would become compelling. If the company was building point-to-point connections, traditional approaches might suffice. But if the company was planning to build an AI ecosystem, MCP would be essential. MCP provides built-in security models, but you need to evaluate if they meet your specific requirements. For healthcare, finance, or government applications, MCP's standardized security approach often exceeds what custom solutions provide. For internal tools with simple security needs, the overhead might not be justified. MCP requires understanding of protocol-based development. If your team is comfortable with REST APIs and JSON protocols, the learning curve is manageable. If they're used to direct database connections or simple file processing, expect a steeper learning curve. MCP has upfront learning costs but reduces long-term maintenance. If you need a quick proof of concept, custom solutions might be faster initially. If you're building for production scale, MCP's standardization pays dividends over time. Check if your target data sources already have MCP servers available. GitHub, popular databases, and major SaaS platforms increasingly support MCP. If your data sources lack MCP support, you'll need to build servers yourself, which changes the cost-benefit analysis. Ready to check your understanding? Go ahead and answer the question that follows. For that healthcare company, the evaluation was clear. 15 data sources, strict security requirements, experienced API team, and long-term production needs. MCP was the right choice. They're now in production, integration time dropped 70%, and security audits are streamlined. Score ease factor on a scale of 1 to 5 for MCP suitability. If your total score is above 15, MCP is likely a good fit. Below 10, consider simpler approaches. Between 10 and 15, evaluate specific factors more deeply. In this video, you learned systematic evaluation considers integration complexity, security requirements, team capabilities, and timeline constraints. MCP provides the most value for AI ecosystems connecting to multiple data sources. Organizations with scores above 15 on the evaluation framework typically see strong MCP benefits, and consider both upfront learning costs and long-term maintenance benefits in your decision. You now have the framework to evaluate MCP for your specific needs, but evaluation is just the beginning. To truly understand MCP's potential, you need to see with a real integration scenario.
2026-07-28T06:45:41.350Z — transcript_dom — Turning Conversations into Software Utilities | Coursera — 10018 chars
This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or computer scientist. But you can use it in conjunction with somebody else who is really good at those things if you aren't a programmer or computer scientist. If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. Now what is this technique? It's a very exciting one from the perspective of software development. One of the challenges that we have is we have all these little tools that we would like to have to help us out. All day long when I'm working and thinking it'd be really nice to have a piece of software that did this and simplified this process for me. Sometimes as a software engineer, I'll go and take the time to actually write the software to do that. The Code Interpreter creates an intriguing new possibility where we can actually turn a conversation with Code Interpreter into software. Now, if you are not a programmer, if you can't read the code, you should not go all the way through with this because you have to be able to look at the code and know if it's going to work correctly, if it's safe to run on your computer. It could delete all the files for all you know, if you can't read the code. We 100% know that large language models can make mistakes, and you need to pay attention to the code that comes out of what I'm going to show you, and if you can't read it and understand it, you should not proceed. You will need to go find a programmer to help the rest of the way. But a programmer at some point, a human software developer, needs to be involved in the analysis for what I'm going to show you. However, it's really exciting. Let's take a look at what we've got. This is an original conversation that I had where I took a movie and I extracted 10 different frames from the movie, I then went and display the images. I looked at it, I'm having this whole conversation, I resize the images and I did a bunch of other things. I turned each image into grayscale, I increase the contrast by 30%, and I turned the images into an animated GIF. I also created a PowerPoint presentation with one image per slide. Now I thought, wouldn't it be great if I could take this whole process so that I can repeat it? Now, I've repeated exactly this type of process for creating tools to help me with the creation of these videos and cataloging them. For example, automatically going and taking my videos and measuring how long they are, creating CSV files for them, doing other interesting things that then help me along. But any conversation that you go and have with Code Interpreter, you can turn into a piece of software in most cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present in the conversation. Then the last step in this thing, I created the PowerPoint and then I also created a CSV to catalog all this stuff. Here's what I'm going to do. This is the real magic now. Again, if you are not a programmer you need to find a programmer if you're going to take this approach. If you're a programmer, you're going to be able to rapidly accelerate the pace that you can create your own personal tools, because any conversation you have with Code Interpreter that yields a good result, you'll be able to kickstart a piece of software for yourself. Here's what I'm going to say. Turn this process into a Python program that I can download and run on my computer and provide the paths to the documents as command line argument, zip up the program for me to download. Now note you could have also gone and said create some GUI application or something else, anything that it has the tools to create, but I'm just going really simple, I'm just saying turn it into a Python program. You can also tell it to replace calls to GPT-4 with calls to OpenAI's API for GPT-4, the completion API. But I'm not going to do that here because I don't need it. What does it do? It says, sure, I can help with that. It's going to create a script that extracts all the frames from the video, resizes the images, converts the images to grayscale, creates a PowerPoint presentation of the images, catalogs them in a CSV file, and makes an animated GIF from the images. It then gives us the command that we're going to need to install all the Python packages for this piece of software, and then finally, it gives us the zip file, which I have actually gone and downloaded. I'm going to show you what this thing looks like now. Here is the application that it has built for me. I'm going to now run it on a new movie that I did not do before, and this thing is going to run for a second. It takes a few seconds to do all of the work that's in here. Then we should be able to see that it's produced a number of different outputs. It created the extracted frames, so if we go and looked at that, we have all the extracted frames from the video, we can go, and we see also that we have this frames presentation, which is all of the individual frames put into a PowerPoint presentation. We have the resized frames, we have the catalog image in CSV. Now, note something. I said it's really important to have a programmer look at this, and the reason is, is because it's not perfect. One of the things that it was supposed to do was to create an animated GIF, and it didn't do that. Now, as a developer, I can easily go and look at the program before I run it, which I did, and I saw that there was nothing problematic in it, I read the code. Then two, I could go and then say, it doesn't have that animated GIF or whatever it is and I could modify it and work with it to get it there. Now the key thing about this is it turned the conversation into a piece of software, and that flow of that conversation dictated the requirements for the software, and the user was interactively developing it. Now if we go back and look at the conversation, as I'm actually going through and running through it, I'm actually going to see incrementally building what I want the conversation to look like. I'm essentially doing the process of trying and building it incrementally and testing it out. Here I'm seeing an intermediate output, I'm then getting to look at the intermediate resized images and check that it's doing what I want. The key thing is it's producing Python code along the way, to do all this stuff. When it gets to the end down here, and I'm telling it to go and create the final Python program, really all it has to do is stitch together all the code that it's already created and modularize it a little bit. Add places where all these paths and things can be taken into the program as command line parameters rather than starting from scratch. Now, if you're not a programmer, how do you use this capability? One, you could kick-start a conversation. You can download it then, and then you could take it to a programmer. You could go hire a freelance developer or some development shop, to then review that, modify it if it doesn't work exactly like you want. But think of how much farther down the path you are. You can show them, here's my requirements, here's the conversation I had where it did exactly what I wanted, here's the initial Python program that it produced for me. Now, go and check that it's going to line up with what I just did. Here's what I want it to do, and here's what it produced. Do a quick audit of it, read through the code, test it inside a container, make sure it looks safe and reasonable, run it on some test cases for me and debug it and run it on my original test case, and then if it looks good, give it back to me. It's a different type of style of software development. It's going to create a new paradigm that's really exciting for creating these smaller tools. We can probably do that much less expensively than when we start from scratch and when we're trying to collect all the requirements, and we're trying to get everybody on the same page about what it's supposed to do and how it's supposed to work. Now we're actually having the end-user build up the process and the flow of what they wanted to do and interact with it through Code Interpreter and generate the initial starting point for the software before taking it to the programmer. The conversation itself becomes a set of requirements, and the software becomes the initial starting point for the developer, so hopefully they don't have to do a whole lot to get it into a final form of a usable tool. Now, if you're a software developer like me, this is awesome because you can take the conversation, you can output Python and you're way ahead of the game. Now in this case, I would have to go and work with a little bit to fix that part that it wasn't giving me the animated GIF I wanted and look for it and make sure there's no other bugs. I'd test it, do all the normal things I would do if I got a piece of software or if I had somebody else write a piece of software for me. But it's a really exciting capability that I think is worth talking about. Now again, I want to warn you, if you're not a programmer, you should not download these things and run them blindly. If you are a programmer, you should not blindly trust the software that comes out of it. You should download it, you should read all the code first, make sure you're comfortable with what it's doing, and you should test it in some safe environment like inside of a container or something else if you're at all concerned about what it's going to be doing. Now, in most cases, these things are going to be fairly straightforward, smaller bits of software, fairly easy to audit. I've created a lot of great tools for myself using this process. It's something really exciting that I think is a new style of software development that's going to be enabled because of Code Interpreter.
2026-07-22T12:11:32.203Z — transcript_dom — Course Introduction | Coursera — 5282 chars
Welcome to this course Introduction to Artificial Intelligence. Imagine unlocking new career opportunities and gaining a competitive edge in your field just by knowing how to harness the power of artificial intelligence or AI. AI is a must for individuals or businesses to push the boundaries of what's possible, driving success like never before. With AI revolutionizing the way we live and work, new innovations are touching our lives every day. From life-saving leaps forward in healthcare management and fraud detection and prevention in finance to personal assistants like Siri and Alexa, autonomous vehicles, personalized experiences through Netflix and Spotify, and fresh content generated by ChatGPT in seconds, people who know how to use AI are transforming our world. If you're looking to fuel your career and transform your business, this course is your gateway to an exciting field shaping your future. According to PwC, over 70% of companies have already adopted some form of AI, and that trend is only set to increase. Whether you're a CEO, software developer, product designer, administrator, or individual just starting out in your career, AI can enable you to do your job in new, more powerful ways. Therefore, this course is for all beginners interested in knowing AI, whether professionals, enthusiasts, practitioners, or students. If you're looking to take your career to the next level or shine out as a business innovator using AI, this course is the ideal way to begin your AI journey. This course serves as your gateway into the dynamic world of AI, where you'll deepen your understanding of its fundamental concepts and terminology. By the end of this course, you will be able to describe what AI is and explain its core concepts. And you will understand how AI applications and use cases can transform our lives and work. You will also recognize the potential and impact of AI in transforming businesses and careers and discuss the issues, limitations, and ethical concerns surrounding AI. What can you look forward to as you advance through this course? The course is divided into four power-packed modules. As soon as you start with the first module, you'll dive into the world of AI, looking at popular AI applications that are already in use across many industries. You'll learn about various common tools like ChatGPT, Google Gemini, Microsoft Copilot, and other applications of generative AI. You'll gain insights into virtual assistance and smart home devices that use AI to automate routine tasks to make life so much more convenient, and you'll examine how AI is reshaping various industries and sectors. In the second module, you'll immerse yourself in the core concepts of AI and build your confidence in using technical terms, such as deep learning, machine learning, and neural networks. You'll look at generative AI models, including large language models or LLMs, and their capabilities. You'll explore the development and application of AI and build your understanding of various domains, such as natural language processing, NLP, and computer vision. You'll discover how advancements in these areas are driving exciting innovations that are changing how we live and work. Once you begin with the third module, you're going to discover how AI can be leveraged for content generation, data analysis, customer service, product development, and more. You're going to see how AI is already benefiting organizations through real-world use cases, and you'll be guided to work out how AI can transform your work and work environment, too. Plus, you'll explore some of the most sought-after AI careers, including AI engineering, data science, robotics engineering, NLP engineering, and AI research. So you can then make an informed decision on what your next learning step should be once you've completed the course. Naturally, other aspects of AI need to be considered, too. So in the fourth module, you'll look at the ethics of AI, how it is governed, and how prevalent concerns and issues surrounding the AI landscape are being addressed. aspects of AI like explainability, fairness, robustness, transparency, and privacy. And you'll investigate the different perspectives of key players and the approaches they are using to address the ethical challenges posed. To help you get your head around all these new terms and build your confidence in the subject, your learning journey will follow a series of videos and readings. You'll have fabulous opportunities to chat with your peers and connect with course staff in the discussion forum, and you'll hear from experienced practitioners through expert viewpoint videos, as they share their perspectives on the concepts that you learn. You'll also participate in hands-on activities and labs that help you apply your knowledge in practice and deepen your understanding. You'll be presented with practice quizzes at the end of each lesson to help you reinforce your learning, and you'll complete a final project and graded quiz at the end of the course so you can earn your certificate. If you're ready to explore the world of AI and discover how your understanding of AI concepts can power your career and put you ahead of the rest of your field, you've come to the right place. Let's get started.
2026-07-21T05:48:03.876Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T05:48:02.111Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T05:47:58.047Z — reading_dom — Benchmark Design Considerations | Coursera — 10262 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type: Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) User Characteristics: Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Input Complexity: Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Ambiguity and vagueness in questions Emotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs: Deliberately misleading or tricky questions Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality: Correctness of answers Logical coherence Evidence of understanding complex concepts Problem-solving effectiveness Tone and Style: Appropriateness to the context and user's tone Consistency with the expected conversational style Appropriateness to the context and user's tone Consistency with the expected conversational style Completeness: Answering all parts of a multi-faceted question Providing sufficient detail where needed Answering all parts of a multi-faceted question Accuracy: Factual correctness Adherence to given instructions or guidelines Adherence to given instructions or guidelines Relevance: Pertinence of the response to the question asked Avoidance of tangential or unrelated information Pertinence of the response to the question asked Avoidance of tangential or unrelated information Safety and Compliance: No generation of harmful content Unbiased output Cultural appropriateness for target users Respect for user privacy and data protection Compliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-21T05:47:57.137Z — reading_dom — Benchmark Design Considerations | Coursera — 10262 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type: Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) User Characteristics: Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Input Complexity: Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Ambiguity and vagueness in questions Emotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs: Deliberately misleading or tricky questions Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality: Correctness of answers Logical coherence Evidence of understanding complex concepts Problem-solving effectiveness Tone and Style: Appropriateness to the context and user's tone Consistency with the expected conversational style Appropriateness to the context and user's tone Consistency with the expected conversational style Completeness: Answering all parts of a multi-faceted question Providing sufficient detail where needed Answering all parts of a multi-faceted question Accuracy: Factual correctness Adherence to given instructions or guidelines Adherence to given instructions or guidelines Relevance: Pertinence of the response to the question asked Avoidance of tangential or unrelated information Pertinence of the response to the question asked Avoidance of tangential or unrelated information Safety and Compliance: No generation of harmful content Unbiased output Cultural appropriateness for target users Respect for user privacy and data protection Compliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-21T05:42:02.550Z — reading_dom — Build a Benchmark | Coursera — 10262 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type: Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) User Characteristics: Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Input Complexity: Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Ambiguity and vagueness in questions Emotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs: Deliberately misleading or tricky questions Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality: Correctness of answers Logical coherence Evidence of understanding complex concepts Problem-solving effectiveness Tone and Style: Appropriateness to the context and user's tone Consistency with the expected conversational style Appropriateness to the context and user's tone Consistency with the expected conversational style Completeness: Answering all parts of a multi-faceted question Providing sufficient detail where needed Answering all parts of a multi-faceted question Accuracy: Factual correctness Adherence to given instructions or guidelines Adherence to given instructions or guidelines Relevance: Pertinence of the response to the question asked Avoidance of tangential or unrelated information Pertinence of the response to the question asked Avoidance of tangential or unrelated information Safety and Compliance: No generation of harmful content Unbiased output Cultural appropriateness for target users Respect for user privacy and data protection Compliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-21T05:42:00.456Z — reading_dom — Build a Benchmark | Coursera — 10262 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type: Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) User Characteristics: Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Input Complexity: Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Ambiguity and vagueness in questions Emotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs: Deliberately misleading or tricky questions Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality: Correctness of answers Logical coherence Evidence of understanding complex concepts Problem-solving effectiveness Tone and Style: Appropriateness to the context and user's tone Consistency with the expected conversational style Appropriateness to the context and user's tone Consistency with the expected conversational style Completeness: Answering all parts of a multi-faceted question Providing sufficient detail where needed Answering all parts of a multi-faceted question Accuracy: Factual correctness Adherence to given instructions or guidelines Adherence to given instructions or guidelines Relevance: Pertinence of the response to the question asked Avoidance of tangential or unrelated information Pertinence of the response to the question asked Avoidance of tangential or unrelated information Safety and Compliance: No generation of harmful content Unbiased output Cultural appropriateness for target users Respect for user privacy and data protection Compliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-21T05:36:15.322Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T05:36:13.363Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T05:36:09.366Z — reading_dom — Benchmark Design Considerations | Coursera — 10262 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type: Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) User Characteristics: Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Input Complexity: Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Ambiguity and vagueness in questions Emotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs: Deliberately misleading or tricky questions Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality: Correctness of answers Logical coherence Evidence of understanding complex concepts Problem-solving effectiveness Tone and Style: Appropriateness to the context and user's tone Consistency with the expected conversational style Appropriateness to the context and user's tone Consistency with the expected conversational style Completeness: Answering all parts of a multi-faceted question Providing sufficient detail where needed Answering all parts of a multi-faceted question Accuracy: Factual correctness Adherence to given instructions or guidelines Adherence to given instructions or guidelines Relevance: Pertinence of the response to the question asked Avoidance of tangential or unrelated information Pertinence of the response to the question asked Avoidance of tangential or unrelated information Safety and Compliance: No generation of harmful content Unbiased output Cultural appropriateness for target users Respect for user privacy and data protection Compliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-21T05:36:07.611Z — reading_dom — Benchmark Design Considerations | Coursera — 10262 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type: Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) User Characteristics: Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Input Complexity: Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Ambiguity and vagueness in questions Emotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs: Deliberately misleading or tricky questions Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality: Correctness of answers Logical coherence Evidence of understanding complex concepts Problem-solving effectiveness Tone and Style: Appropriateness to the context and user's tone Consistency with the expected conversational style Appropriateness to the context and user's tone Consistency with the expected conversational style Completeness: Answering all parts of a multi-faceted question Providing sufficient detail where needed Answering all parts of a multi-faceted question Accuracy: Factual correctness Adherence to given instructions or guidelines Adherence to given instructions or guidelines Relevance: Pertinence of the response to the question asked Avoidance of tangential or unrelated information Pertinence of the response to the question asked Avoidance of tangential or unrelated information Safety and Compliance: No generation of harmful content Unbiased output Cultural appropriateness for target users Respect for user privacy and data protection Compliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-21T04:41:54.101Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T04:41:33.579Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T04:41:23.170Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T04:41:11.795Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T04:40:59.205Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T04:40:55.730Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T04:40:53.477Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T04:40:51.886Z — transcript_dom — Build a Benchmark | Coursera — 5672 chars
Anytime we change the instructions to our GPT or we change the knowledge base, it can have unexpected effects. One of the important things that we want to do when we start building a custom GPT and really thinking about how do we build the best custom GPT? A very important thing to do is to build yourself a simple benchmark. Now, why do you do this? Well, you do this so that as you make changes, you can make sure that it's still reasoning effectively. That it's not regressing in some area and starting to give bad answers to something that used to do well on. But the other reason you want to do it is because you want to make sure that it really is as good as you think it is. Often when we go and sort of ad hoc to this, we may miss areas where we will say, well, it should be able to do this, and we don't actually test it. Then it turns out it doesn't do a very good job of it. Building yourself a benchmark is a really simple thing that you can do to one, make sure it actually performs well in all the areas that you would like it to perform well. But two, that as you go and modify it and improve it over time, that you don't have some type of regression. Now, there's lots of ways that you can do benchmarking, but I'm going to show you a really simple way. So I've created a simple table here with a number of questions showing the prompt that the user would input, what the expected answer is. Now notice, I'm saying expected answer. I'm just trying to provide a simplified description of what we want. Just roughly, is there a right or wrong? Now, you could also have examples of great output, like if you go and you play around with it and experiment with it, and you see a really great output, you could capture that and cut and paste it in as expected answer. Here, I've just typed in what the expected answers are. Because in these cases, there's right and wrong, but I might want to have something that's more about the quality, something qualitative as opposed to quantitative. But the key is you want to capture examples of what a good output looks like in the document, so somebody reading it can interpret it. Then we want to have a rubric. How are we going to grade the output? In many cases, the output is not something that we can quantitatively evaluate easily. It's something that a human being is going to have to look at, or we may use GPT 4 in the future to grade itself or degrade other models if we're using some future model. We want to spell out exactly what we are looking for in the output. What makes this answer good or bad? How if we change that answer in different ways, would we lose points or increase points? Then finally, we want a scoring scale. I like 1-10, you could have a scoring scale to 100, however you want to do this. But we want a simple scoring scale so we can look at and see then quantitatively, how does the human being who's looking at this think it's performing? Now, our goal might be to get all tens on every answer. But realistically, probably what we're going to see is that we're going to get, good scores on some things, not as good scores on some things, and we're going to always probably be dealing with some trade offs in different areas depending on how we go and change the instructions. The other thing that we want to do in this is we want to stop and think methodically about what are the ways that it's going to be used, that we want it to perform well in. We want to think about lots of different tasks or questions that the user might, go in input, and we want to test them and check how it works. This is an opportunity for us to really start thinking about comprehensively, like, have we covered all of the cases? Then we want to go and think about what are unexpected things that the user might want to do. Later I'll talk about adversarial testing, but we also want to have what is going to happen if the user types in something totally unexpected. The user goes and says, generate an image of a car for me, or the user says, predict the best stock for me to buy next week. What will happen? What will your GPT do? Will it do what you expect it to do? So, you want to have both the anticipated use cases, but you also want to have edge use cases. Now, in software engineering, we've been doing this for a long time. Now, you have to do it and think about it, if you're going to build some type of custom GPT and deploy it because it's essentially a software tool that you're giving to other people. You want to think about both the things that you expect, but you also want to think about the edge cases and the things that are totally unexpected as well to make sure that aid your guard rails work correctly, there's not some type of unexpected loophole in your knowledge base or your instructions that creates something that is problematic for you. If you're going to create and deploy a real custom GPT that's going to be useful to a lot of people, you will benefit immensely if you stop and build yourself a benchmark of what you expect to do, examples of good output, how you would score output because it's probably not yes or no. It's probably some nuance of what you're looking for in that output, and then the scores. Then you can go and each time you make changes or updates to the custom GPT, you can run through this script and test it. There's also ways to automate this that I won't go through in this class, but you can also go and automate some of this. But the key is you want to be measuring the success of your custom GPT. Is it doing better on the things you care about? Is it regressing in anywhere? Is it handling the edge cases applying its guard rails correctly?
2026-07-21T04:40:49.641Z — lab_structured — Build a Benchmark | Coursera — 1882 chars
R Status: PLUS PLUS OpenAI GPTs: Creating Your Own Custom AI Assistants Today's Skill Points 5 XP See skill progress Module 1 Custom GPTs Fundamentals Module 2 THINK: Create Great GPTs (Part I) Test Test Video . Duration: 1 minute 1 min Build a Benchmark Video . Duration: 5 minutes 5 min Benchmark Design Considerations Reading . Duration: 20 minutes 20 min Build a Custom GPT for Generating Test Cases Video . Duration: 8 minutes 8 min Build Your Own Custom GPT Test Case Generator Graded Assignment . Duration: 30 minutes 30 min Help the User Solve the Problem, Not Provide Answers The Goal is to Help the Human Solve the Problem, Not Provide the Answer Video . Duration: 1 minute 1 min How to Cite Knowledge Video . Duration: 4 minutes 4 min Output Formatting Video . Duration: 6 minutes 6 min Practical Scenario Real-world application . Duration: 5 minutes 5 min Template Pattern & Markdown Reading . Duration: 10 minutes 10 min Provide the Facts Video . Duration: 5 minutes 5 min Hedging While Helping Video . Duration: 4 minutes 4 min Practical Scenario Real-world application . Duration: 5 minutes 5 min Menu Action Pattern Video . Duration: 5 minutes 5 min Format of the Menu Actions Pattern Reading . Duration: 10 minutes 10 min Where to Get Additional Help Video . Duration: 2 minutes 2 min Building a GPT with a Menu Graded Assignment . Duration: 30 minutes 30 min Information Before Decision Making Information Before Decision Making Video . Duration: 3 minutes 3 min Flipped Interaction Pattern Video . Duration: 4 minutes 4 min Format of the Flipped Interaction Pattern Reading . Duration: 10 minutes 10 min Missing Context from the User Video . Duration: 5 minutes 5 min User-Customized Experiences Video . Duration: 4 minutes 4 min A Personalized GPT Graded Assignment . Duration: 15 minutes 15 min Module 3 THINK: Create Great GPTs (Part II) Transcript Notes Files
2026-07-21T04:00:19.077Z — transcript_dom — Build a Custom GPT for Generating Test Cases | Coursera — 8275 chars
>> Thinking up great test cases obviously is going to be a challenge. You have to really think carefully about all the different dimensions. And sometimes as human beings, we don't do the best job of really thinking through all the different ways that somebody could interact with our system and all the different issues that could arise. And I've given you a number of different dimensions to think about when building test cases. Now, how are we going to overcome this problem? Well, one way that I can help you overcome this problem is I can show you how to build your own custom GPT to generate your test cases. In fact, this is a great way to get started with building a custom GPT because the risk is extremely low. We are trying to have it generate ideas for test cases that we can then use to test other GPTs. Now, if it doesn't do a great job, we'll look at it and say, hey, none of those are useful test cases to me. We can go and tweak it and try to improve it, but often what we'll see is it can generate really good and sort of thoughtful test cases for our domain. Now, how are we going to do this? How are we going to build a custom GPT? So I've started putting together a custom GPT. I've also included a document with all of the different sort of design considerations for test cases for custom GPTs. Now, there's lots and lots of additional design considerations you might want to consider for your particular domain, like compliance or regulatory or other things. But this is a good starting point. I've given it to you so you can go and tweak it and build your own custom GPT that is appropriate for your organization or your own custom GPT test case generator. So I've started one, the Custom GPT Test Case Generator. And here are my instructions. You're going to help the user generate test cases for their custom GPT. First, you will ask the user questions one at a time until you understand what their custom GPT is supposed to do. Once you have a reasonable understanding, progress to the next step. Second, you will read the provided document and generate four initial test cases for the user to consider based on a variety of dimensions. You will explain each test case. Each test case should be formatted, is I have title is a level one heading. You'll learn more about this in subsequent videos of how I'm doing this. Each test case should have a goal. Each one should explain what is being tested and why. Each one of them should have a user prompt. And I have some typos which I'll fix as I go along here we now have the prompt that the user would have typed in, and it's going to generate and think of the prompt. We're going to have what the correct answer should be, but we're going to tell it that the user needs to be told that they should edit or fill this part in. It might give you an initial starting point. And then we're going to have a rubric for grading the output that considers the different sort of dimensions of this test case and the purpose. Then what I've done is for the knowledge base, I have attached the document from your prior reading that describes all the dimensions of test cases and the types of things that we need to consider. And I have attached that as the knowledge base so it understands what we're trying to accomplish. What are the dimensions of the test cases that we need to consider to have the appropriate inspiration to go and build good test cases? And now let's go and take a look at our generator. So I'm going to switch over here. This is our custom GPT test case generator. And I'm going to say, please help me generate test cases for my custom GPT. So it starts off by saying, great, let's understand more about your custom GPT. This is a GPT to answer questions about the Vanderbilt travel and expense policy. And it's asking me a whole bunch of questions about what else might be going in. You think of appropriate answers, given that this is a large university. Now, I could have gone in, and I probably should have gone and filled each one in, but for the. For the purposes of this video, I'm letting it sort of infer what is appropriate based on what I've done. Now it's starting to generate test cases. Test one, basic factual inquiry. What is the per diem rate for meals during university funded travel? So it's giving me what the prompt would have been that the user typed in. The correct answer it tells me I need to fill this in with the correct information from the Vanderbilt travel policy. Now, notice I could have given it the travel policy, actually, and had it actually fill in correct answers for me, which would have been fantastic, and I should have thought of doing that. Maybe I'll do that in the future. And then the rubric, it's giving me the scoring rubric for this thing. Test case two, procedural guidance test. Tests its ability to guide the user through a more complex procedural query, assessing both the completeness and step by step. How do I submit a travel experience report for attendance at a conference. And so this is a more sort of step by step. The answer may be spread out across multiple pages, something else. And so it has to be able to go and assess all this information and then provide it. Interpretive. Can I get reimbursed for travel expenses if I decide to extend for my stay for personal reasons, multi turn interaction with clarifications, then I can go in and say, generate four more for me. So let's go and generate some additional test cases. Handling ambiguity. What should I do if my travel costs exceed the budget? So it's not clear what that means. And like we mean, I have follow up specific scenarios, and so it's keeping going. And I could just go and brainstorm with it over and over, or I could tailor it, and I could say, no, I want test cases related to potential issues, related to compliance, or something that could have a safety related issue in it. And I want to make sure that there's not a problem with safety and what I'm recommending, or something that could cause the user to spend a lot of money, and then it turns out that we can't reimburse for it at Vanderbilt, and that gets people upset. So let's try that one. What about test cases that involve the user potentially spending a lot of money that they can't get reimbursed for? So let's check that out. What might that look like? So it starts off with non reimbursable expenses. Can I book a luxury hotel during my conference trip and get reimbursed? Well, there's a misunderstanding because you can't do a luxury hotel at all. That would be considered a problem. International conference. What should I know about booking business class flights, providing all of these things that could be expensive, that we want to test and make sure that we've really thought through? Are we guarding our users against spending money that they can't get reimbursed for, which creates a financial burden on them, creates probably a lot of anxiety, unhappiness with the university, other things like that. So we could start tailoring our test cases and really thinking about them from a lot of different dimensions. We could also go and update our custom GPT so it outputs them as comma separated values, or in some structured format that we can import into, like an excel file that we're using to keep track of all this, or to import into some test case system where we track results. So not only can we use the generator to do sort of textual versions, but we could go and update the output formatting so that we can just output our test cases as something that we can pull into CSV, you know, pull into excel and track that way, or pull into some other tool, and we can make it easier for ourselves to do this. But this is a great way to get started about brainstorming and thinking through all the different ways that somebody might want to be able to go and use your system, but also you can go and sort of brainstorm with it what would be really adversarial things that somebody might do and try to get a whole bunch of different ideas. Because the goal of this is to help you think more broadly about how somebody might use it, how they might mess up what might be the potential problems or ambiguities, and really have a large exploration of your capabilities of your GPT.
2026-07-21T04:00:15.532Z — transcript_dom — Build a Custom GPT for Generating Test Cases | Coursera — 8275 chars
>> Thinking up great test cases obviously is going to be a challenge. You have to really think carefully about all the different dimensions. And sometimes as human beings, we don't do the best job of really thinking through all the different ways that somebody could interact with our system and all the different issues that could arise. And I've given you a number of different dimensions to think about when building test cases. Now, how are we going to overcome this problem? Well, one way that I can help you overcome this problem is I can show you how to build your own custom GPT to generate your test cases. In fact, this is a great way to get started with building a custom GPT because the risk is extremely low. We are trying to have it generate ideas for test cases that we can then use to test other GPTs. Now, if it doesn't do a great job, we'll look at it and say, hey, none of those are useful test cases to me. We can go and tweak it and try to improve it, but often what we'll see is it can generate really good and sort of thoughtful test cases for our domain. Now, how are we going to do this? How are we going to build a custom GPT? So I've started putting together a custom GPT. I've also included a document with all of the different sort of design considerations for test cases for custom GPTs. Now, there's lots and lots of additional design considerations you might want to consider for your particular domain, like compliance or regulatory or other things. But this is a good starting point. I've given it to you so you can go and tweak it and build your own custom GPT that is appropriate for your organization or your own custom GPT test case generator. So I've started one, the Custom GPT Test Case Generator. And here are my instructions. You're going to help the user generate test cases for their custom GPT. First, you will ask the user questions one at a time until you understand what their custom GPT is supposed to do. Once you have a reasonable understanding, progress to the next step. Second, you will read the provided document and generate four initial test cases for the user to consider based on a variety of dimensions. You will explain each test case. Each test case should be formatted, is I have title is a level one heading. You'll learn more about this in subsequent videos of how I'm doing this. Each test case should have a goal. Each one should explain what is being tested and why. Each one of them should have a user prompt. And I have some typos which I'll fix as I go along here we now have the prompt that the user would have typed in, and it's going to generate and think of the prompt. We're going to have what the correct answer should be, but we're going to tell it that the user needs to be told that they should edit or fill this part in. It might give you an initial starting point. And then we're going to have a rubric for grading the output that considers the different sort of dimensions of this test case and the purpose. Then what I've done is for the knowledge base, I have attached the document from your prior reading that describes all the dimensions of test cases and the types of things that we need to consider. And I have attached that as the knowledge base so it understands what we're trying to accomplish. What are the dimensions of the test cases that we need to consider to have the appropriate inspiration to go and build good test cases? And now let's go and take a look at our generator. So I'm going to switch over here. This is our custom GPT test case generator. And I'm going to say, please help me generate test cases for my custom GPT. So it starts off by saying, great, let's understand more about your custom GPT. This is a GPT to answer questions about the Vanderbilt travel and expense policy. And it's asking me a whole bunch of questions about what else might be going in. You think of appropriate answers, given that this is a large university. Now, I could have gone in, and I probably should have gone and filled each one in, but for the. For the purposes of this video, I'm letting it sort of infer what is appropriate based on what I've done. Now it's starting to generate test cases. Test one, basic factual inquiry. What is the per diem rate for meals during university funded travel? So it's giving me what the prompt would have been that the user typed in. The correct answer it tells me I need to fill this in with the correct information from the Vanderbilt travel policy. Now, notice I could have given it the travel policy, actually, and had it actually fill in correct answers for me, which would have been fantastic, and I should have thought of doing that. Maybe I'll do that in the future. And then the rubric, it's giving me the scoring rubric for this thing. Test case two, procedural guidance test. Tests its ability to guide the user through a more complex procedural query, assessing both the completeness and step by step. How do I submit a travel experience report for attendance at a conference. And so this is a more sort of step by step. The answer may be spread out across multiple pages, something else. And so it has to be able to go and assess all this information and then provide it. Interpretive. Can I get reimbursed for travel expenses if I decide to extend for my stay for personal reasons, multi turn interaction with clarifications, then I can go in and say, generate four more for me. So let's go and generate some additional test cases. Handling ambiguity. What should I do if my travel costs exceed the budget? So it's not clear what that means. And like we mean, I have follow up specific scenarios, and so it's keeping going. And I could just go and brainstorm with it over and over, or I could tailor it, and I could say, no, I want test cases related to potential issues, related to compliance, or something that could have a safety related issue in it. And I want to make sure that there's not a problem with safety and what I'm recommending, or something that could cause the user to spend a lot of money, and then it turns out that we can't reimburse for it at Vanderbilt, and that gets people upset. So let's try that one. What about test cases that involve the user potentially spending a lot of money that they can't get reimbursed for? So let's check that out. What might that look like? So it starts off with non reimbursable expenses. Can I book a luxury hotel during my conference trip and get reimbursed? Well, there's a misunderstanding because you can't do a luxury hotel at all. That would be considered a problem. International conference. What should I know about booking business class flights, providing all of these things that could be expensive, that we want to test and make sure that we've really thought through? Are we guarding our users against spending money that they can't get reimbursed for, which creates a financial burden on them, creates probably a lot of anxiety, unhappiness with the university, other things like that. So we could start tailoring our test cases and really thinking about them from a lot of different dimensions. We could also go and update our custom GPT so it outputs them as comma separated values, or in some structured format that we can import into, like an excel file that we're using to keep track of all this, or to import into some test case system where we track results. So not only can we use the generator to do sort of textual versions, but we could go and update the output formatting so that we can just output our test cases as something that we can pull into CSV, you know, pull into excel and track that way, or pull into some other tool, and we can make it easier for ourselves to do this. But this is a great way to get started about brainstorming and thinking through all the different ways that somebody might want to be able to go and use your system, but also you can go and sort of brainstorm with it what would be really adversarial things that somebody might do and try to get a whole bunch of different ideas. Because the goal of this is to help you think more broadly about how somebody might use it, how they might mess up what might be the potential problems or ambiguities, and really have a large exploration of your capabilities of your GPT.
2026-07-21T04:00:13.235Z — transcript_dom — Build a Custom GPT for Generating Test Cases | Coursera — 8275 chars
>> Thinking up great test cases obviously is going to be a challenge. You have to really think carefully about all the different dimensions. And sometimes as human beings, we don't do the best job of really thinking through all the different ways that somebody could interact with our system and all the different issues that could arise. And I've given you a number of different dimensions to think about when building test cases. Now, how are we going to overcome this problem? Well, one way that I can help you overcome this problem is I can show you how to build your own custom GPT to generate your test cases. In fact, this is a great way to get started with building a custom GPT because the risk is extremely low. We are trying to have it generate ideas for test cases that we can then use to test other GPTs. Now, if it doesn't do a great job, we'll look at it and say, hey, none of those are useful test cases to me. We can go and tweak it and try to improve it, but often what we'll see is it can generate really good and sort of thoughtful test cases for our domain. Now, how are we going to do this? How are we going to build a custom GPT? So I've started putting together a custom GPT. I've also included a document with all of the different sort of design considerations for test cases for custom GPTs. Now, there's lots and lots of additional design considerations you might want to consider for your particular domain, like compliance or regulatory or other things. But this is a good starting point. I've given it to you so you can go and tweak it and build your own custom GPT that is appropriate for your organization or your own custom GPT test case generator. So I've started one, the Custom GPT Test Case Generator. And here are my instructions. You're going to help the user generate test cases for their custom GPT. First, you will ask the user questions one at a time until you understand what their custom GPT is supposed to do. Once you have a reasonable understanding, progress to the next step. Second, you will read the provided document and generate four initial test cases for the user to consider based on a variety of dimensions. You will explain each test case. Each test case should be formatted, is I have title is a level one heading. You'll learn more about this in subsequent videos of how I'm doing this. Each test case should have a goal. Each one should explain what is being tested and why. Each one of them should have a user prompt. And I have some typos which I'll fix as I go along here we now have the prompt that the user would have typed in, and it's going to generate and think of the prompt. We're going to have what the correct answer should be, but we're going to tell it that the user needs to be told that they should edit or fill this part in. It might give you an initial starting point. And then we're going to have a rubric for grading the output that considers the different sort of dimensions of this test case and the purpose. Then what I've done is for the knowledge base, I have attached the document from your prior reading that describes all the dimensions of test cases and the types of things that we need to consider. And I have attached that as the knowledge base so it understands what we're trying to accomplish. What are the dimensions of the test cases that we need to consider to have the appropriate inspiration to go and build good test cases? And now let's go and take a look at our generator. So I'm going to switch over here. This is our custom GPT test case generator. And I'm going to say, please help me generate test cases for my custom GPT. So it starts off by saying, great, let's understand more about your custom GPT. This is a GPT to answer questions about the Vanderbilt travel and expense policy. And it's asking me a whole bunch of questions about what else might be going in. You think of appropriate answers, given that this is a large university. Now, I could have gone in, and I probably should have gone and filled each one in, but for the. For the purposes of this video, I'm letting it sort of infer what is appropriate based on what I've done. Now it's starting to generate test cases. Test one, basic factual inquiry. What is the per diem rate for meals during university funded travel? So it's giving me what the prompt would have been that the user typed in. The correct answer it tells me I need to fill this in with the correct information from the Vanderbilt travel policy. Now, notice I could have given it the travel policy, actually, and had it actually fill in correct answers for me, which would have been fantastic, and I should have thought of doing that. Maybe I'll do that in the future. And then the rubric, it's giving me the scoring rubric for this thing. Test case two, procedural guidance test. Tests its ability to guide the user through a more complex procedural query, assessing both the completeness and step by step. How do I submit a travel experience report for attendance at a conference. And so this is a more sort of step by step. The answer may be spread out across multiple pages, something else. And so it has to be able to go and assess all this information and then provide it. Interpretive. Can I get reimbursed for travel expenses if I decide to extend for my stay for personal reasons, multi turn interaction with clarifications, then I can go in and say, generate four more for me. So let's go and generate some additional test cases. Handling ambiguity. What should I do if my travel costs exceed the budget? So it's not clear what that means. And like we mean, I have follow up specific scenarios, and so it's keeping going. And I could just go and brainstorm with it over and over, or I could tailor it, and I could say, no, I want test cases related to potential issues, related to compliance, or something that could have a safety related issue in it. And I want to make sure that there's not a problem with safety and what I'm recommending, or something that could cause the user to spend a lot of money, and then it turns out that we can't reimburse for it at Vanderbilt, and that gets people upset. So let's try that one. What about test cases that involve the user potentially spending a lot of money that they can't get reimbursed for? So let's check that out. What might that look like? So it starts off with non reimbursable expenses. Can I book a luxury hotel during my conference trip and get reimbursed? Well, there's a misunderstanding because you can't do a luxury hotel at all. That would be considered a problem. International conference. What should I know about booking business class flights, providing all of these things that could be expensive, that we want to test and make sure that we've really thought through? Are we guarding our users against spending money that they can't get reimbursed for, which creates a financial burden on them, creates probably a lot of anxiety, unhappiness with the university, other things like that. So we could start tailoring our test cases and really thinking about them from a lot of different dimensions. We could also go and update our custom GPT so it outputs them as comma separated values, or in some structured format that we can import into, like an excel file that we're using to keep track of all this, or to import into some test case system where we track results. So not only can we use the generator to do sort of textual versions, but we could go and update the output formatting so that we can just output our test cases as something that we can pull into CSV, you know, pull into excel and track that way, or pull into some other tool, and we can make it easier for ourselves to do this. But this is a great way to get started about brainstorming and thinking through all the different ways that somebody might want to be able to go and use your system, but also you can go and sort of brainstorm with it what would be really adversarial things that somebody might do and try to get a whole bunch of different ideas. Because the goal of this is to help you think more broadly about how somebody might use it, how they might mess up what might be the potential problems or ambiguities, and really have a large exploration of your capabilities of your GPT.
2026-07-21T04:00:11.010Z — transcript_dom — Build a Custom GPT for Generating Test Cases | Coursera — 8275 chars
>> Thinking up great test cases obviously is going to be a challenge. You have to really think carefully about all the different dimensions. And sometimes as human beings, we don't do the best job of really thinking through all the different ways that somebody could interact with our system and all the different issues that could arise. And I've given you a number of different dimensions to think about when building test cases. Now, how are we going to overcome this problem? Well, one way that I can help you overcome this problem is I can show you how to build your own custom GPT to generate your test cases. In fact, this is a great way to get started with building a custom GPT because the risk is extremely low. We are trying to have it generate ideas for test cases that we can then use to test other GPTs. Now, if it doesn't do a great job, we'll look at it and say, hey, none of those are useful test cases to me. We can go and tweak it and try to improve it, but often what we'll see is it can generate really good and sort of thoughtful test cases for our domain. Now, how are we going to do this? How are we going to build a custom GPT? So I've started putting together a custom GPT. I've also included a document with all of the different sort of design considerations for test cases for custom GPTs. Now, there's lots and lots of additional design considerations you might want to consider for your particular domain, like compliance or regulatory or other things. But this is a good starting point. I've given it to you so you can go and tweak it and build your own custom GPT that is appropriate for your organization or your own custom GPT test case generator. So I've started one, the Custom GPT Test Case Generator. And here are my instructions. You're going to help the user generate test cases for their custom GPT. First, you will ask the user questions one at a time until you understand what their custom GPT is supposed to do. Once you have a reasonable understanding, progress to the next step. Second, you will read the provided document and generate four initial test cases for the user to consider based on a variety of dimensions. You will explain each test case. Each test case should be formatted, is I have title is a level one heading. You'll learn more about this in subsequent videos of how I'm doing this. Each test case should have a goal. Each one should explain what is being tested and why. Each one of them should have a user prompt. And I have some typos which I'll fix as I go along here we now have the prompt that the user would have typed in, and it's going to generate and think of the prompt. We're going to have what the correct answer should be, but we're going to tell it that the user needs to be told that they should edit or fill this part in. It might give you an initial starting point. And then we're going to have a rubric for grading the output that considers the different sort of dimensions of this test case and the purpose. Then what I've done is for the knowledge base, I have attached the document from your prior reading that describes all the dimensions of test cases and the types of things that we need to consider. And I have attached that as the knowledge base so it understands what we're trying to accomplish. What are the dimensions of the test cases that we need to consider to have the appropriate inspiration to go and build good test cases? And now let's go and take a look at our generator. So I'm going to switch over here. This is our custom GPT test case generator. And I'm going to say, please help me generate test cases for my custom GPT. So it starts off by saying, great, let's understand more about your custom GPT. This is a GPT to answer questions about the Vanderbilt travel and expense policy. And it's asking me a whole bunch of questions about what else might be going in. You think of appropriate answers, given that this is a large university. Now, I could have gone in, and I probably should have gone and filled each one in, but for the. For the purposes of this video, I'm letting it sort of infer what is appropriate based on what I've done. Now it's starting to generate test cases. Test one, basic factual inquiry. What is the per diem rate for meals during university funded travel? So it's giving me what the prompt would have been that the user typed in. The correct answer it tells me I need to fill this in with the correct information from the Vanderbilt travel policy. Now, notice I could have given it the travel policy, actually, and had it actually fill in correct answers for me, which would have been fantastic, and I should have thought of doing that. Maybe I'll do that in the future. And then the rubric, it's giving me the scoring rubric for this thing. Test case two, procedural guidance test. Tests its ability to guide the user through a more complex procedural query, assessing both the completeness and step by step. How do I submit a travel experience report for attendance at a conference. And so this is a more sort of step by step. The answer may be spread out across multiple pages, something else. And so it has to be able to go and assess all this information and then provide it. Interpretive. Can I get reimbursed for travel expenses if I decide to extend for my stay for personal reasons, multi turn interaction with clarifications, then I can go in and say, generate four more for me. So let's go and generate some additional test cases. Handling ambiguity. What should I do if my travel costs exceed the budget? So it's not clear what that means. And like we mean, I have follow up specific scenarios, and so it's keeping going. And I could just go and brainstorm with it over and over, or I could tailor it, and I could say, no, I want test cases related to potential issues, related to compliance, or something that could have a safety related issue in it. And I want to make sure that there's not a problem with safety and what I'm recommending, or something that could cause the user to spend a lot of money, and then it turns out that we can't reimburse for it at Vanderbilt, and that gets people upset. So let's try that one. What about test cases that involve the user potentially spending a lot of money that they can't get reimbursed for? So let's check that out. What might that look like? So it starts off with non reimbursable expenses. Can I book a luxury hotel during my conference trip and get reimbursed? Well, there's a misunderstanding because you can't do a luxury hotel at all. That would be considered a problem. International conference. What should I know about booking business class flights, providing all of these things that could be expensive, that we want to test and make sure that we've really thought through? Are we guarding our users against spending money that they can't get reimbursed for, which creates a financial burden on them, creates probably a lot of anxiety, unhappiness with the university, other things like that. So we could start tailoring our test cases and really thinking about them from a lot of different dimensions. We could also go and update our custom GPT so it outputs them as comma separated values, or in some structured format that we can import into, like an excel file that we're using to keep track of all this, or to import into some test case system where we track results. So not only can we use the generator to do sort of textual versions, but we could go and update the output formatting so that we can just output our test cases as something that we can pull into CSV, you know, pull into excel and track that way, or pull into some other tool, and we can make it easier for ourselves to do this. But this is a great way to get started about brainstorming and thinking through all the different ways that somebody might want to be able to go and use your system, but also you can go and sort of brainstorm with it what would be really adversarial things that somebody might do and try to get a whole bunch of different ideas. Because the goal of this is to help you think more broadly about how somebody might use it, how they might mess up what might be the potential problems or ambiguities, and really have a large exploration of your capabilities of your GPT.
2026-07-20T18:41:39.836Z — transcript_dom — Build a Benchmark | Coursera — 8275 chars
>> Thinking up great test cases obviously is going to be a challenge. You have to really think carefully about all the different dimensions. And sometimes as human beings, we don't do the best job of really thinking through all the different ways that somebody could interact with our system and all the different issues that could arise. And I've given you a number of different dimensions to think about when building test cases. Now, how are we going to overcome this problem? Well, one way that I can help you overcome this problem is I can show you how to build your own custom GPT to generate your test cases. In fact, this is a great way to get started with building a custom GPT because the risk is extremely low. We are trying to have it generate ideas for test cases that we can then use to test other GPTs. Now, if it doesn't do a great job, we'll look at it and say, hey, none of those are useful test cases to me. We can go and tweak it and try to improve it, but often what we'll see is it can generate really good and sort of thoughtful test cases for our domain. Now, how are we going to do this? How are we going to build a custom GPT? So I've started putting together a custom GPT. I've also included a document with all of the different sort of design considerations for test cases for custom GPTs. Now, there's lots and lots of additional design considerations you might want to consider for your particular domain, like compliance or regulatory or other things. But this is a good starting point. I've given it to you so you can go and tweak it and build your own custom GPT that is appropriate for your organization or your own custom GPT test case generator. So I've started one, the Custom GPT Test Case Generator. And here are my instructions. You're going to help the user generate test cases for their custom GPT. First, you will ask the user questions one at a time until you understand what their custom GPT is supposed to do. Once you have a reasonable understanding, progress to the next step. Second, you will read the provided document and generate four initial test cases for the user to consider based on a variety of dimensions. You will explain each test case. Each test case should be formatted, is I have title is a level one heading. You'll learn more about this in subsequent videos of how I'm doing this. Each test case should have a goal. Each one should explain what is being tested and why. Each one of them should have a user prompt. And I have some typos which I'll fix as I go along here we now have the prompt that the user would have typed in, and it's going to generate and think of the prompt. We're going to have what the correct answer should be, but we're going to tell it that the user needs to be told that they should edit or fill this part in. It might give you an initial starting point. And then we're going to have a rubric for grading the output that considers the different sort of dimensions of this test case and the purpose. Then what I've done is for the knowledge base, I have attached the document from your prior reading that describes all the dimensions of test cases and the types of things that we need to consider. And I have attached that as the knowledge base so it understands what we're trying to accomplish. What are the dimensions of the test cases that we need to consider to have the appropriate inspiration to go and build good test cases? And now let's go and take a look at our generator. So I'm going to switch over here. This is our custom GPT test case generator. And I'm going to say, please help me generate test cases for my custom GPT. So it starts off by saying, great, let's understand more about your custom GPT. This is a GPT to answer questions about the Vanderbilt travel and expense policy. And it's asking me a whole bunch of questions about what else might be going in. You think of appropriate answers, given that this is a large university. Now, I could have gone in, and I probably should have gone and filled each one in, but for the. For the purposes of this video, I'm letting it sort of infer what is appropriate based on what I've done. Now it's starting to generate test cases. Test one, basic factual inquiry. What is the per diem rate for meals during university funded travel? So it's giving me what the prompt would have been that the user typed in. The correct answer it tells me I need to fill this in with the correct information from the Vanderbilt travel policy. Now, notice I could have given it the travel policy, actually, and had it actually fill in correct answers for me, which would have been fantastic, and I should have thought of doing that. Maybe I'll do that in the future. And then the rubric, it's giving me the scoring rubric for this thing. Test case two, procedural guidance test. Tests its ability to guide the user through a more complex procedural query, assessing both the completeness and step by step. How do I submit a travel experience report for attendance at a conference. And so this is a more sort of step by step. The answer may be spread out across multiple pages, something else. And so it has to be able to go and assess all this information and then provide it. Interpretive. Can I get reimbursed for travel expenses if I decide to extend for my stay for personal reasons, multi turn interaction with clarifications, then I can go in and say, generate four more for me. So let's go and generate some additional test cases. Handling ambiguity. What should I do if my travel costs exceed the budget? So it's not clear what that means. And like we mean, I have follow up specific scenarios, and so it's keeping going. And I could just go and brainstorm with it over and over, or I could tailor it, and I could say, no, I want test cases related to potential issues, related to compliance, or something that could have a safety related issue in it. And I want to make sure that there's not a problem with safety and what I'm recommending, or something that could cause the user to spend a lot of money, and then it turns out that we can't reimburse for it at Vanderbilt, and that gets people upset. So let's try that one. What about test cases that involve the user potentially spending a lot of money that they can't get reimbursed for? So let's check that out. What might that look like? So it starts off with non reimbursable expenses. Can I book a luxury hotel during my conference trip and get reimbursed? Well, there's a misunderstanding because you can't do a luxury hotel at all. That would be considered a problem. International conference. What should I know about booking business class flights, providing all of these things that could be expensive, that we want to test and make sure that we've really thought through? Are we guarding our users against spending money that they can't get reimbursed for, which creates a financial burden on them, creates probably a lot of anxiety, unhappiness with the university, other things like that. So we could start tailoring our test cases and really thinking about them from a lot of different dimensions. We could also go and update our custom GPT so it outputs them as comma separated values, or in some structured format that we can import into, like an excel file that we're using to keep track of all this, or to import into some test case system where we track results. So not only can we use the generator to do sort of textual versions, but we could go and update the output formatting so that we can just output our test cases as something that we can pull into CSV, you know, pull into excel and track that way, or pull into some other tool, and we can make it easier for ourselves to do this. But this is a great way to get started about brainstorming and thinking through all the different ways that somebody might want to be able to go and use your system, but also you can go and sort of brainstorm with it what would be really adversarial things that somebody might do and try to get a whole bunch of different ideas. Because the goal of this is to help you think more broadly about how somebody might use it, how they might mess up what might be the potential problems or ambiguities, and really have a large exploration of your capabilities of your GPT.
2026-07-20T18:41:36.345Z — transcript_dom — Build a Benchmark | Coursera — 8275 chars
>> Thinking up great test cases obviously is going to be a challenge. You have to really think carefully about all the different dimensions. And sometimes as human beings, we don't do the best job of really thinking through all the different ways that somebody could interact with our system and all the different issues that could arise. And I've given you a number of different dimensions to think about when building test cases. Now, how are we going to overcome this problem? Well, one way that I can help you overcome this problem is I can show you how to build your own custom GPT to generate your test cases. In fact, this is a great way to get started with building a custom GPT because the risk is extremely low. We are trying to have it generate ideas for test cases that we can then use to test other GPTs. Now, if it doesn't do a great job, we'll look at it and say, hey, none of those are useful test cases to me. We can go and tweak it and try to improve it, but often what we'll see is it can generate really good and sort of thoughtful test cases for our domain. Now, how are we going to do this? How are we going to build a custom GPT? So I've started putting together a custom GPT. I've also included a document with all of the different sort of design considerations for test cases for custom GPTs. Now, there's lots and lots of additional design considerations you might want to consider for your particular domain, like compliance or regulatory or other things. But this is a good starting point. I've given it to you so you can go and tweak it and build your own custom GPT that is appropriate for your organization or your own custom GPT test case generator. So I've started one, the Custom GPT Test Case Generator. And here are my instructions. You're going to help the user generate test cases for their custom GPT. First, you will ask the user questions one at a time until you understand what their custom GPT is supposed to do. Once you have a reasonable understanding, progress to the next step. Second, you will read the provided document and generate four initial test cases for the user to consider based on a variety of dimensions. You will explain each test case. Each test case should be formatted, is I have title is a level one heading. You'll learn more about this in subsequent videos of how I'm doing this. Each test case should have a goal. Each one should explain what is being tested and why. Each one of them should have a user prompt. And I have some typos which I'll fix as I go along here we now have the prompt that the user would have typed in, and it's going to generate and think of the prompt. We're going to have what the correct answer should be, but we're going to tell it that the user needs to be told that they should edit or fill this part in. It might give you an initial starting point. And then we're going to have a rubric for grading the output that considers the different sort of dimensions of this test case and the purpose. Then what I've done is for the knowledge base, I have attached the document from your prior reading that describes all the dimensions of test cases and the types of things that we need to consider. And I have attached that as the knowledge base so it understands what we're trying to accomplish. What are the dimensions of the test cases that we need to consider to have the appropriate inspiration to go and build good test cases? And now let's go and take a look at our generator. So I'm going to switch over here. This is our custom GPT test case generator. And I'm going to say, please help me generate test cases for my custom GPT. So it starts off by saying, great, let's understand more about your custom GPT. This is a GPT to answer questions about the Vanderbilt travel and expense policy. And it's asking me a whole bunch of questions about what else might be going in. You think of appropriate answers, given that this is a large university. Now, I could have gone in, and I probably should have gone and filled each one in, but for the. For the purposes of this video, I'm letting it sort of infer what is appropriate based on what I've done. Now it's starting to generate test cases. Test one, basic factual inquiry. What is the per diem rate for meals during university funded travel? So it's giving me what the prompt would have been that the user typed in. The correct answer it tells me I need to fill this in with the correct information from the Vanderbilt travel policy. Now, notice I could have given it the travel policy, actually, and had it actually fill in correct answers for me, which would have been fantastic, and I should have thought of doing that. Maybe I'll do that in the future. And then the rubric, it's giving me the scoring rubric for this thing. Test case two, procedural guidance test. Tests its ability to guide the user through a more complex procedural query, assessing both the completeness and step by step. How do I submit a travel experience report for attendance at a conference. And so this is a more sort of step by step. The answer may be spread out across multiple pages, something else. And so it has to be able to go and assess all this information and then provide it. Interpretive. Can I get reimbursed for travel expenses if I decide to extend for my stay for personal reasons, multi turn interaction with clarifications, then I can go in and say, generate four more for me. So let's go and generate some additional test cases. Handling ambiguity. What should I do if my travel costs exceed the budget? So it's not clear what that means. And like we mean, I have follow up specific scenarios, and so it's keeping going. And I could just go and brainstorm with it over and over, or I could tailor it, and I could say, no, I want test cases related to potential issues, related to compliance, or something that could have a safety related issue in it. And I want to make sure that there's not a problem with safety and what I'm recommending, or something that could cause the user to spend a lot of money, and then it turns out that we can't reimburse for it at Vanderbilt, and that gets people upset. So let's try that one. What about test cases that involve the user potentially spending a lot of money that they can't get reimbursed for? So let's check that out. What might that look like? So it starts off with non reimbursable expenses. Can I book a luxury hotel during my conference trip and get reimbursed? Well, there's a misunderstanding because you can't do a luxury hotel at all. That would be considered a problem. International conference. What should I know about booking business class flights, providing all of these things that could be expensive, that we want to test and make sure that we've really thought through? Are we guarding our users against spending money that they can't get reimbursed for, which creates a financial burden on them, creates probably a lot of anxiety, unhappiness with the university, other things like that. So we could start tailoring our test cases and really thinking about them from a lot of different dimensions. We could also go and update our custom GPT so it outputs them as comma separated values, or in some structured format that we can import into, like an excel file that we're using to keep track of all this, or to import into some test case system where we track results. So not only can we use the generator to do sort of textual versions, but we could go and update the output formatting so that we can just output our test cases as something that we can pull into CSV, you know, pull into excel and track that way, or pull into some other tool, and we can make it easier for ourselves to do this. But this is a great way to get started about brainstorming and thinking through all the different ways that somebody might want to be able to go and use your system, but also you can go and sort of brainstorm with it what would be really adversarial things that somebody might do and try to get a whole bunch of different ideas. Because the goal of this is to help you think more broadly about how somebody might use it, how they might mess up what might be the potential problems or ambiguities, and really have a large exploration of your capabilities of your GPT.
2026-07-20T18:41:34.047Z — transcript_dom — Build a Benchmark | Coursera — 8275 chars
>> Thinking up great test cases obviously is going to be a challenge. You have to really think carefully about all the different dimensions. And sometimes as human beings, we don't do the best job of really thinking through all the different ways that somebody could interact with our system and all the different issues that could arise. And I've given you a number of different dimensions to think about when building test cases. Now, how are we going to overcome this problem? Well, one way that I can help you overcome this problem is I can show you how to build your own custom GPT to generate your test cases. In fact, this is a great way to get started with building a custom GPT because the risk is extremely low. We are trying to have it generate ideas for test cases that we can then use to test other GPTs. Now, if it doesn't do a great job, we'll look at it and say, hey, none of those are useful test cases to me. We can go and tweak it and try to improve it, but often what we'll see is it can generate really good and sort of thoughtful test cases for our domain. Now, how are we going to do this? How are we going to build a custom GPT? So I've started putting together a custom GPT. I've also included a document with all of the different sort of design considerations for test cases for custom GPTs. Now, there's lots and lots of additional design considerations you might want to consider for your particular domain, like compliance or regulatory or other things. But this is a good starting point. I've given it to you so you can go and tweak it and build your own custom GPT that is appropriate for your organization or your own custom GPT test case generator. So I've started one, the Custom GPT Test Case Generator. And here are my instructions. You're going to help the user generate test cases for their custom GPT. First, you will ask the user questions one at a time until you understand what their custom GPT is supposed to do. Once you have a reasonable understanding, progress to the next step. Second, you will read the provided document and generate four initial test cases for the user to consider based on a variety of dimensions. You will explain each test case. Each test case should be formatted, is I have title is a level one heading. You'll learn more about this in subsequent videos of how I'm doing this. Each test case should have a goal. Each one should explain what is being tested and why. Each one of them should have a user prompt. And I have some typos which I'll fix as I go along here we now have the prompt that the user would have typed in, and it's going to generate and think of the prompt. We're going to have what the correct answer should be, but we're going to tell it that the user needs to be told that they should edit or fill this part in. It might give you an initial starting point. And then we're going to have a rubric for grading the output that considers the different sort of dimensions of this test case and the purpose. Then what I've done is for the knowledge base, I have attached the document from your prior reading that describes all the dimensions of test cases and the types of things that we need to consider. And I have attached that as the knowledge base so it understands what we're trying to accomplish. What are the dimensions of the test cases that we need to consider to have the appropriate inspiration to go and build good test cases? And now let's go and take a look at our generator. So I'm going to switch over here. This is our custom GPT test case generator. And I'm going to say, please help me generate test cases for my custom GPT. So it starts off by saying, great, let's understand more about your custom GPT. This is a GPT to answer questions about the Vanderbilt travel and expense policy. And it's asking me a whole bunch of questions about what else might be going in. You think of appropriate answers, given that this is a large university. Now, I could have gone in, and I probably should have gone and filled each one in, but for the. For the purposes of this video, I'm letting it sort of infer what is appropriate based on what I've done. Now it's starting to generate test cases. Test one, basic factual inquiry. What is the per diem rate for meals during university funded travel? So it's giving me what the prompt would have been that the user typed in. The correct answer it tells me I need to fill this in with the correct information from the Vanderbilt travel policy. Now, notice I could have given it the travel policy, actually, and had it actually fill in correct answers for me, which would have been fantastic, and I should have thought of doing that. Maybe I'll do that in the future. And then the rubric, it's giving me the scoring rubric for this thing. Test case two, procedural guidance test. Tests its ability to guide the user through a more complex procedural query, assessing both the completeness and step by step. How do I submit a travel experience report for attendance at a conference. And so this is a more sort of step by step. The answer may be spread out across multiple pages, something else. And so it has to be able to go and assess all this information and then provide it. Interpretive. Can I get reimbursed for travel expenses if I decide to extend for my stay for personal reasons, multi turn interaction with clarifications, then I can go in and say, generate four more for me. So let's go and generate some additional test cases. Handling ambiguity. What should I do if my travel costs exceed the budget? So it's not clear what that means. And like we mean, I have follow up specific scenarios, and so it's keeping going. And I could just go and brainstorm with it over and over, or I could tailor it, and I could say, no, I want test cases related to potential issues, related to compliance, or something that could have a safety related issue in it. And I want to make sure that there's not a problem with safety and what I'm recommending, or something that could cause the user to spend a lot of money, and then it turns out that we can't reimburse for it at Vanderbilt, and that gets people upset. So let's try that one. What about test cases that involve the user potentially spending a lot of money that they can't get reimbursed for? So let's check that out. What might that look like? So it starts off with non reimbursable expenses. Can I book a luxury hotel during my conference trip and get reimbursed? Well, there's a misunderstanding because you can't do a luxury hotel at all. That would be considered a problem. International conference. What should I know about booking business class flights, providing all of these things that could be expensive, that we want to test and make sure that we've really thought through? Are we guarding our users against spending money that they can't get reimbursed for, which creates a financial burden on them, creates probably a lot of anxiety, unhappiness with the university, other things like that. So we could start tailoring our test cases and really thinking about them from a lot of different dimensions. We could also go and update our custom GPT so it outputs them as comma separated values, or in some structured format that we can import into, like an excel file that we're using to keep track of all this, or to import into some test case system where we track results. So not only can we use the generator to do sort of textual versions, but we could go and update the output formatting so that we can just output our test cases as something that we can pull into CSV, you know, pull into excel and track that way, or pull into some other tool, and we can make it easier for ourselves to do this. But this is a great way to get started about brainstorming and thinking through all the different ways that somebody might want to be able to go and use your system, but also you can go and sort of brainstorm with it what would be really adversarial things that somebody might do and try to get a whole bunch of different ideas. Because the goal of this is to help you think more broadly about how somebody might use it, how they might mess up what might be the potential problems or ambiguities, and really have a large exploration of your capabilities of your GPT.
2026-07-20T18:41:32.801Z — transcript_dom — Build a Benchmark | Coursera — 8275 chars
>> Thinking up great test cases obviously is going to be a challenge. You have to really think carefully about all the different dimensions. And sometimes as human beings, we don't do the best job of really thinking through all the different ways that somebody could interact with our system and all the different issues that could arise. And I've given you a number of different dimensions to think about when building test cases. Now, how are we going to overcome this problem? Well, one way that I can help you overcome this problem is I can show you how to build your own custom GPT to generate your test cases. In fact, this is a great way to get started with building a custom GPT because the risk is extremely low. We are trying to have it generate ideas for test cases that we can then use to test other GPTs. Now, if it doesn't do a great job, we'll look at it and say, hey, none of those are useful test cases to me. We can go and tweak it and try to improve it, but often what we'll see is it can generate really good and sort of thoughtful test cases for our domain. Now, how are we going to do this? How are we going to build a custom GPT? So I've started putting together a custom GPT. I've also included a document with all of the different sort of design considerations for test cases for custom GPTs. Now, there's lots and lots of additional design considerations you might want to consider for your particular domain, like compliance or regulatory or other things. But this is a good starting point. I've given it to you so you can go and tweak it and build your own custom GPT that is appropriate for your organization or your own custom GPT test case generator. So I've started one, the Custom GPT Test Case Generator. And here are my instructions. You're going to help the user generate test cases for their custom GPT. First, you will ask the user questions one at a time until you understand what their custom GPT is supposed to do. Once you have a reasonable understanding, progress to the next step. Second, you will read the provided document and generate four initial test cases for the user to consider based on a variety of dimensions. You will explain each test case. Each test case should be formatted, is I have title is a level one heading. You'll learn more about this in subsequent videos of how I'm doing this. Each test case should have a goal. Each one should explain what is being tested and why. Each one of them should have a user prompt. And I have some typos which I'll fix as I go along here we now have the prompt that the user would have typed in, and it's going to generate and think of the prompt. We're going to have what the correct answer should be, but we're going to tell it that the user needs to be told that they should edit or fill this part in. It might give you an initial starting point. And then we're going to have a rubric for grading the output that considers the different sort of dimensions of this test case and the purpose. Then what I've done is for the knowledge base, I have attached the document from your prior reading that describes all the dimensions of test cases and the types of things that we need to consider. And I have attached that as the knowledge base so it understands what we're trying to accomplish. What are the dimensions of the test cases that we need to consider to have the appropriate inspiration to go and build good test cases? And now let's go and take a look at our generator. So I'm going to switch over here. This is our custom GPT test case generator. And I'm going to say, please help me generate test cases for my custom GPT. So it starts off by saying, great, let's understand more about your custom GPT. This is a GPT to answer questions about the Vanderbilt travel and expense policy. And it's asking me a whole bunch of questions about what else might be going in. You think of appropriate answers, given that this is a large university. Now, I could have gone in, and I probably should have gone and filled each one in, but for the. For the purposes of this video, I'm letting it sort of infer what is appropriate based on what I've done. Now it's starting to generate test cases. Test one, basic factual inquiry. What is the per diem rate for meals during university funded travel? So it's giving me what the prompt would have been that the user typed in. The correct answer it tells me I need to fill this in with the correct information from the Vanderbilt travel policy. Now, notice I could have given it the travel policy, actually, and had it actually fill in correct answers for me, which would have been fantastic, and I should have thought of doing that. Maybe I'll do that in the future. And then the rubric, it's giving me the scoring rubric for this thing. Test case two, procedural guidance test. Tests its ability to guide the user through a more complex procedural query, assessing both the completeness and step by step. How do I submit a travel experience report for attendance at a conference. And so this is a more sort of step by step. The answer may be spread out across multiple pages, something else. And so it has to be able to go and assess all this information and then provide it. Interpretive. Can I get reimbursed for travel expenses if I decide to extend for my stay for personal reasons, multi turn interaction with clarifications, then I can go in and say, generate four more for me. So let's go and generate some additional test cases. Handling ambiguity. What should I do if my travel costs exceed the budget? So it's not clear what that means. And like we mean, I have follow up specific scenarios, and so it's keeping going. And I could just go and brainstorm with it over and over, or I could tailor it, and I could say, no, I want test cases related to potential issues, related to compliance, or something that could have a safety related issue in it. And I want to make sure that there's not a problem with safety and what I'm recommending, or something that could cause the user to spend a lot of money, and then it turns out that we can't reimburse for it at Vanderbilt, and that gets people upset. So let's try that one. What about test cases that involve the user potentially spending a lot of money that they can't get reimbursed for? So let's check that out. What might that look like? So it starts off with non reimbursable expenses. Can I book a luxury hotel during my conference trip and get reimbursed? Well, there's a misunderstanding because you can't do a luxury hotel at all. That would be considered a problem. International conference. What should I know about booking business class flights, providing all of these things that could be expensive, that we want to test and make sure that we've really thought through? Are we guarding our users against spending money that they can't get reimbursed for, which creates a financial burden on them, creates probably a lot of anxiety, unhappiness with the university, other things like that. So we could start tailoring our test cases and really thinking about them from a lot of different dimensions. We could also go and update our custom GPT so it outputs them as comma separated values, or in some structured format that we can import into, like an excel file that we're using to keep track of all this, or to import into some test case system where we track results. So not only can we use the generator to do sort of textual versions, but we could go and update the output formatting so that we can just output our test cases as something that we can pull into CSV, you know, pull into excel and track that way, or pull into some other tool, and we can make it easier for ourselves to do this. But this is a great way to get started about brainstorming and thinking through all the different ways that somebody might want to be able to go and use your system, but also you can go and sort of brainstorm with it what would be really adversarial things that somebody might do and try to get a whole bunch of different ideas. Because the goal of this is to help you think more broadly about how somebody might use it, how they might mess up what might be the potential problems or ambiguities, and really have a large exploration of your capabilities of your GPT.
2026-07-20T18:41:30.000Z — lab_structured — Build a Benchmark | Coursera — 1883 chars
R Status: PLUS PLUS OpenAI GPTs: Creating Your Own Custom AI Assistants Today's Skill Points 18 XP See skill progress Module 1 Custom GPTs Fundamentals Module 2 THINK: Create Great GPTs (Part I) Test Test Video . Duration: 1 minute 1 min Build a Benchmark Video . Duration: 5 minutes 5 min Benchmark Design Considerations Reading . Duration: 20 minutes 20 min Build a Custom GPT for Generating Test Cases Video . Duration: 8 minutes 8 min Build Your Own Custom GPT Test Case Generator Graded Assignment . Duration: 30 minutes 30 min Help the User Solve the Problem, Not Provide Answers The Goal is to Help the Human Solve the Problem, Not Provide the Answer Video . Duration: 1 minute 1 min How to Cite Knowledge Video . Duration: 4 minutes 4 min Output Formatting Video . Duration: 6 minutes 6 min Practical Scenario Real-world application . Duration: 5 minutes 5 min Template Pattern & Markdown Reading . Duration: 10 minutes 10 min Provide the Facts Video . Duration: 5 minutes 5 min Hedging While Helping Video . Duration: 4 minutes 4 min Practical Scenario Real-world application . Duration: 5 minutes 5 min Menu Action Pattern Video . Duration: 5 minutes 5 min Format of the Menu Actions Pattern Reading . Duration: 10 minutes 10 min Where to Get Additional Help Video . Duration: 2 minutes 2 min Building a GPT with a Menu Graded Assignment . Duration: 30 minutes 30 min Information Before Decision Making Information Before Decision Making Video . Duration: 3 minutes 3 min Flipped Interaction Pattern Video . Duration: 4 minutes 4 min Format of the Flipped Interaction Pattern Reading . Duration: 10 minutes 10 min Missing Context from the User Video . Duration: 5 minutes 5 min User-Customized Experiences Video . Duration: 4 minutes 4 min A Personalized GPT Graded Assignment . Duration: 15 minutes 15 min Module 3 THINK: Create Great GPTs (Part II) Transcript Notes Files
2026-07-20T18:31:44.475Z — reading_dom — Build a Benchmark | Coursera — 9543 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type:Factual questions (e.g., simple queries about known information)Reasoning tasks (e.g., puzzles or problem-solving questions)Creative tasks (e.g., generating stories or ideas)Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) User Characteristics:Literacy levels (e.g., basic, intermediate, advanced)Domain knowledge (e.g., layperson, enthusiast, expert)Language and dialects (e.g., variations of English, non-native speakers)Demographics (e.g., age, cultural background) Language and dialects (e.g., variations of English, non-native speakers) Input Complexity:Length of input (e.g., single sentences, paragraphs, multi-turn dialogues)Clarity of context (e.g., with or without sufficient context)Ambiguity and vagueness in questionsEmotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs:Deliberately misleading or tricky questionsAttempts to elicit biased or inappropriate responsesInputs designed to violate privacy or security standards Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality:Correctness of answersLogical coherenceEvidence of understanding complex conceptsProblem-solving effectiveness Tone and Style:Appropriateness to the context and user's toneConsistency with the expected conversational style Completeness:Answering all parts of a multi-faceted questionProviding sufficient detail where needed Accuracy:Factual correctnessAdherence to given instructions or guidelines Relevance:Pertinence of the response to the question askedAvoidance of tangential or unrelated information Safety and Compliance:No generation of harmful contentUnbiased outputCultural appropriateness for target usersRespect for user privacy and data protectionCompliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-20T18:31:41.094Z — reading_dom — Build a Benchmark | Coursera — 9543 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type:Factual questions (e.g., simple queries about known information)Reasoning tasks (e.g., puzzles or problem-solving questions)Creative tasks (e.g., generating stories or ideas)Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) User Characteristics:Literacy levels (e.g., basic, intermediate, advanced)Domain knowledge (e.g., layperson, enthusiast, expert)Language and dialects (e.g., variations of English, non-native speakers)Demographics (e.g., age, cultural background) Language and dialects (e.g., variations of English, non-native speakers) Input Complexity:Length of input (e.g., single sentences, paragraphs, multi-turn dialogues)Clarity of context (e.g., with or without sufficient context)Ambiguity and vagueness in questionsEmotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs:Deliberately misleading or tricky questionsAttempts to elicit biased or inappropriate responsesInputs designed to violate privacy or security standards Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality:Correctness of answersLogical coherenceEvidence of understanding complex conceptsProblem-solving effectiveness Tone and Style:Appropriateness to the context and user's toneConsistency with the expected conversational style Completeness:Answering all parts of a multi-faceted questionProviding sufficient detail where needed Accuracy:Factual correctnessAdherence to given instructions or guidelines Relevance:Pertinence of the response to the question askedAvoidance of tangential or unrelated information Safety and Compliance:No generation of harmful contentUnbiased outputCultural appropriateness for target usersRespect for user privacy and data protectionCompliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-20T18:31:38.714Z — reading_dom — Build a Benchmark | Coursera — 9543 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type:Factual questions (e.g., simple queries about known information)Reasoning tasks (e.g., puzzles or problem-solving questions)Creative tasks (e.g., generating stories or ideas)Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) User Characteristics:Literacy levels (e.g., basic, intermediate, advanced)Domain knowledge (e.g., layperson, enthusiast, expert)Language and dialects (e.g., variations of English, non-native speakers)Demographics (e.g., age, cultural background) Language and dialects (e.g., variations of English, non-native speakers) Input Complexity:Length of input (e.g., single sentences, paragraphs, multi-turn dialogues)Clarity of context (e.g., with or without sufficient context)Ambiguity and vagueness in questionsEmotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs:Deliberately misleading or tricky questionsAttempts to elicit biased or inappropriate responsesInputs designed to violate privacy or security standards Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality:Correctness of answersLogical coherenceEvidence of understanding complex conceptsProblem-solving effectiveness Tone and Style:Appropriateness to the context and user's toneConsistency with the expected conversational style Completeness:Answering all parts of a multi-faceted questionProviding sufficient detail where needed Accuracy:Factual correctnessAdherence to given instructions or guidelines Relevance:Pertinence of the response to the question askedAvoidance of tangential or unrelated information Safety and Compliance:No generation of harmful contentUnbiased outputCultural appropriateness for target usersRespect for user privacy and data protectionCompliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-20T18:31:36.951Z — reading_dom — Build a Benchmark | Coursera — 9543 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type:Factual questions (e.g., simple queries about known information)Reasoning tasks (e.g., puzzles or problem-solving questions)Creative tasks (e.g., generating stories or ideas)Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) User Characteristics:Literacy levels (e.g., basic, intermediate, advanced)Domain knowledge (e.g., layperson, enthusiast, expert)Language and dialects (e.g., variations of English, non-native speakers)Demographics (e.g., age, cultural background) Language and dialects (e.g., variations of English, non-native speakers) Input Complexity:Length of input (e.g., single sentences, paragraphs, multi-turn dialogues)Clarity of context (e.g., with or without sufficient context)Ambiguity and vagueness in questionsEmotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs:Deliberately misleading or tricky questionsAttempts to elicit biased or inappropriate responsesInputs designed to violate privacy or security standards Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality:Correctness of answersLogical coherenceEvidence of understanding complex conceptsProblem-solving effectiveness Tone and Style:Appropriateness to the context and user's toneConsistency with the expected conversational style Completeness:Answering all parts of a multi-faceted questionProviding sufficient detail where needed Accuracy:Factual correctnessAdherence to given instructions or guidelines Relevance:Pertinence of the response to the question askedAvoidance of tangential or unrelated information Safety and Compliance:No generation of harmful contentUnbiased outputCultural appropriateness for target usersRespect for user privacy and data protectionCompliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-20T18:31:33.917Z — reading_dom — Build a Benchmark | Coursera — 9543 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type:Factual questions (e.g., simple queries about known information)Reasoning tasks (e.g., puzzles or problem-solving questions)Creative tasks (e.g., generating stories or ideas)Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) User Characteristics:Literacy levels (e.g., basic, intermediate, advanced)Domain knowledge (e.g., layperson, enthusiast, expert)Language and dialects (e.g., variations of English, non-native speakers)Demographics (e.g., age, cultural background) Language and dialects (e.g., variations of English, non-native speakers) Input Complexity:Length of input (e.g., single sentences, paragraphs, multi-turn dialogues)Clarity of context (e.g., with or without sufficient context)Ambiguity and vagueness in questionsEmotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs:Deliberately misleading or tricky questionsAttempts to elicit biased or inappropriate responsesInputs designed to violate privacy or security standards Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality:Correctness of answersLogical coherenceEvidence of understanding complex conceptsProblem-solving effectiveness Tone and Style:Appropriateness to the context and user's toneConsistency with the expected conversational style Completeness:Answering all parts of a multi-faceted questionProviding sufficient detail where needed Accuracy:Factual correctnessAdherence to given instructions or guidelines Relevance:Pertinence of the response to the question askedAvoidance of tangential or unrelated information Safety and Compliance:No generation of harmful contentUnbiased outputCultural appropriateness for target usersRespect for user privacy and data protectionCompliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-20T18:31:30.130Z — reading_dom — Build a Benchmark | Coursera — 1171 chars
Module 1Custom GPTs Fundamentals Module 2THINK: Create Great GPTs (Part I) Module 3THINK: Create Great GPTs (Part II) Benchmark Design ConsiderationsReading. Duration: 20 minutes20 min Build a Custom GPT for Generating Test CasesVideo. Duration: 8 minutes8 min Build Your Own Custom GPT Test Case GeneratorGraded Assignment. Duration: 30 minutes30 min The Goal is to Help the Human Solve the Problem, Not Provide the AnswerVideo. Duration: 1 minute1 min Practical ScenarioReal-world application. Duration: 5 minutes5 min Template Pattern & MarkdownReading. Duration: 10 minutes10 min Format of the Menu Actions PatternReading. Duration: 10 minutes10 min Where to Get Additional HelpVideo. Duration: 2 minutes2 min Building a GPT with a MenuGraded Assignment. Duration: 30 minutes30 min Information Before Decision MakingVideo. Duration: 3 minutes3 min Flipped Interaction PatternVideo. Duration: 4 minutes4 min Format of the Flipped Interaction PatternReading. Duration: 10 minutes10 min Missing Context from the UserVideo. Duration: 5 minutes5 min User-Customized ExperiencesVideo. Duration: 4 minutes4 min A Personalized GPTGraded Assignment. Duration: 15 minutes15 min
2026-07-20T18:26:07.180Z — subtitle_track — Build a Benchmark | Coursera — 6542 chars
Jedes Mal, wenn wir die Anweisungen für unser GPT ändern oder die Wissensdatenbank anpassen, kann das unerwartete Auswirkungen haben. Deshalb ist es eine der wichtigsten Maßnahmen, wenn wir anfangen, ein eigenes GPT zu entwickeln und wirklich darüber nachdenken, wie wir das beste individuelle GPT bauen können, sich selbst einen einfachen Benchmark zu erstellen. Warum macht man das? Nun, das macht man, damit man bei jeder Änderung sicherstellen kann, dass das System weiterhin effektiv schlussfolgert. dass es nicht in irgendeinem Bereich Rückschritte macht und plötzlich schlechte Antworten auf etwas gibt, was es vorher gut konnte. Aber ein weiterer Grund, warum man das tun sollte, ist, dass man sicherstellen möchte, dass es wirklich so gut ist, wie man denkt. Und oft, wenn wir das eher Ad-hoc-Netzwerk machen, übersehen wir Bereiche, bei denen wir sagen würden: ‚Oh, das sollte es eigentlich können ‘, aber wir testen es gar nicht wirklich. Und dann stellt sich heraus, dass es darin gar nicht so gut ist. Deshalb ist es eine wirklich einfache Sache, sich selbst einen Benchmark zu erstellen – zum einen, um sicherzustellen, dass es tatsächlich in all den Bereichen gut funktioniert, in denen Sie es erwarten, und zum anderen, damit Sie beim weiteren Anpassen und Verbessern im Laufe der Zeit keine Art von Rückschritt erleben. Es gibt nun viele Möglichkeiten, wie man Benchmarking betreiben kann, aber ich werde Ihnen eine wirklich einfache Methode zeigen. Ich habe hier also eine einfache Tabelle erstellt, mit einer Reihe von Fragen, die den Prompt zeigen, den der Nutzer eingeben würde, sowie die erwartete Antwort. Beachten Sie, dass ich von der erwarteten Antwort spreche. Ich versuche nur, eine vereinfachte Beschreibung zu geben. Was wir wollen. Es geht nur grob darum, ob etwas richtig oder falsch ist. Sie könnten aber auch Beispiele für großartige Ausgaben haben – wenn Sie also damit herumspielen und experimentieren und eine wirklich gute Ausgabe sehen, könnten Sie diese erfassen und als erwartete Antwort einfügen. Hier habe ich einfach eingetippt, was die erwarteten Antworten sind. In diesen Fällen gibt es richtig und falsch, aber vielleicht möchte ich auch etwas haben, das mehr die Eigenschaften betrifft – also etwas Qualitatives statt Quantitativem. Aber das Entscheidende ist, dass Sie Beispiele dafür festhalten, wie eine gute Ausgabe aussieht, damit jemand, der das Dokument liest, es interpretieren kann. Dann wollen wir eine Bewertungsrubrik haben. Wie werden wir die Ausgabe bewerten? In vielen Fällen ist die Ausgabe nichts, was wir einfach so, wissen Sie, nicht etwas, das wir leicht quantitativ bewerten können. Es ist etwas, das sich ein Mensch anschauen muss, oder wir könnten in Zukunft GPT-4 verwenden, um sich selbst oder andere Modelle zu bewerten. Wenn wir ... Sie wissen schon, irgendein zukünftiges Modell verwenden. Deshalb wollen wir genau festlegen, was wir in der Ausgabe erwarten. Was macht diese Antwort gut oder schlecht, und wie würden sich Änderungen an der Antwort auf die Bewertung auswirken – würden wir dadurch Punkte verlieren oder gewinnen? Und schließlich wollen wir eine Bewertungsskala festlegen. Mir gefällt eine Skala von eins bis zehn. Man könnte auch eine Skala bis hundert nehmen, je nachdem, wie man es machen möchte. Aber wir wollen eine einfache Bewertungsskala, damit wir dann quantitativ sehen können, wie der Mensch, der das beurteilt, die Leistung einschätzt. Unser Ziel könnte zwar sein, bei jeder Antwort eine glatte Zehn zu erreichen, aber realistisch gesehen werden wir wahrscheinlich feststellen, dass wir bei manchen Dingen gute Bewertungen bekommen, bei anderen nicht so gute, und dass wir immer mit gewissen Kompromissen in verschiedenen Bereichen zu tun haben werden, je nachdem, wie wir die Anweisungen anpassen und verändern. Das andere, was wir hierbei tun wollen, ist, dass wir innehalten und methodisch darüber nachdenken, in welchen Anwendungsbereichen es besonders gut funktionieren soll. Deshalb sollten wir an viele verschiedene Aufgaben oder Fragen denken, die der Nutzer eingeben könnte, und wir wollen sie testen und überprüfen, wie das System darauf reagiert. Das ist also eine Gelegenheit für uns, wirklich umfassend zu überlegen, ob wir alle Fälle abgedeckt haben. Und dann wollen wir uns überlegen, welche unerwarteten Dinge der Nutzer vielleicht tun möchte. Später werde ich über adversariales Testen sprechen. Aber wir wollen auch wissen, was passiert, wenn der Nutzer etwas völlig Unerwartetes eingibt. Der Nutzer sagt zum Beispiel: Erzeuge ein Bild von einem Auto für mich. Oder der Nutzer sagt: Sag mir, welche Aktie ich nächste Woche am besten kaufen sollte. Was wird dann passieren? Was wird dein GPT dann tun? Wird es das tun, was du erwartest? Du möchtest also sowohl die erwarteten Anwendungsfälle abdecken, aber auch Randfälle berücksichtigen. In der Softwareentwicklung machen wir das schon seit langer Zeit. Jetzt musst du das auch tun und darüber nachdenken, wenn du eine Art benutzerdefinierten GPT bauen und bereitstellen willst, denn im Grunde genommen ist es ein Software-Tool, das du anderen Menschen zur Verfügung stellst. Du solltest also sowohl an die Dinge denken, die du erwartest, als auch an die Randfälle und an völlig unerwartete Situationen, um sicherzustellen, dass erstens deine Schutzmechanismen richtig funktionieren. Es darf keine unerwarteten Schlupflöcher in deiner Wissensbasis oder deinen Anweisungen geben, die zu Problemen für dich führen könnten. Wenn du also ein wirklich benutzerdefiniertes GPT entwickeln, erstellen und bereitstellen willst, das für viele Menschen nützlich ist, Du wirst enorm davon profitieren, wenn du dir die Zeit nimmst und dir einen Benchmark erstellst: Was erwartest du, was es tun soll? Beispiele für gute Ausgaben, wie du die Ausgaben bewerten würdest – denn wahrscheinlich ist es nicht einfach nur Ja oder Nein, sondern es gibt Nuancen dessen, was du in der Ausgabe suchst – und dann die Bewertungen. Dann kannst du jedes Mal, wenn du Änderungen oder Updates an deinem benutzerdefinierten GPT vornimmst, dieses Skript durchlaufen. und es testen. Es gibt auch Möglichkeiten, das zu automatisieren, auf die ich in diesem Kurs nicht eingehen werde, aber du kannst auch einen Teil davon automatisieren. Aber das Entscheidende ist, dass du messen willst, wie erfolgreich dein benutzerdefiniertes GPT ist. Wird es bei den Dingen, die dir wichtig sind, besser? Gibt es irgendwo Rückschritte? Geht es mit den Grenzfall richtig um, setzt es seine Schutzmechanismen korrekt ein?
2026-07-20T18:25:15.823Z — reading_dom — Benchmark Design Considerations | Coursera — 10262 chars
Benchmark Design Considerations Example "What If" Scenarios Scenario 1: Customer Service GPT for Telecommunications Company Scenario 2: GPT as a Recipe Assistant Scenario 3: GPT as a Financial Advising Assistant Scenario 4: Educational GPT for Language Learning A Framework for Thinking of Test Cases 1. Variability in Test Cases 2. Rubric for Assessing Output 3. Assessing Multi-Message Conversational Characteristics When designing and testing a custom GPT to ensure it meets specific benchmarks, we're focusing on evaluating its performance under a range of scenarios and input variations to ensure its effectiveness, accuracy, and reliability. This involves creating a comprehensive suite of tests that encompass various types of tasks, user profiles, and input complexities, as well as assessing its outputs against a detailed rubric and analyzing conversational characteristics across multiple interactions. The testing should include variability in the test cases to mimic the real-world unpredictability of user interactions. To achieve this, we classify our test cases into diverse categories such as factual questions, reasoning tasks, creative tasks, and instruction-based challenges. Moreover, we consider the user's characteristics like literacy levels, domain knowledge, and cultural background to ensure that the AI can handle interactions with a wide range of users. We also test it with different levels of input complexity from short, clear inputs to long, ambiguous conversations and shield it against adversarial inputs designed to trip it up. Throughout this process, we're not just seeking to confirm that the GPT can perform the tasks – we're also ensuring that it does so in a manner that is nuanced, human-like, and sensitive to the complexities of real-world communication. This rigorous testing ensures that the GPT can deliver high-quality, reliable, and appropriate responses across a wide variety of conversational scenarios. 1.What if a customer is expressing frustration in a non-direct way? –Testing how the GPT detects passive language indicative of frustration and responds with empathy and de-escalation techniques. 2.What if a customer uses technical jargon incorrectly? –Testing whether the GPT can gently correct the customer and provide the correct information without causing confusion or offense. 3.What if the customer asks for a service or product that doesn’t exist? –Testing the GPT’s ability to guide the customer towards existing alternatives while managing expectations. 1.What if the user has dietary restrictions they haven’t explicitly mentioned? –Testing the GPT’s ability to ask clarifying questions about dietary needs when certain keywords (like “vegan” or “gluten-free”) appear. 2.What if the user makes a mistake in describing the recipe they want help with? –Testing the GPT’s capacity to spot inconsistencies and politely request clarification to ensure accurate assistance. 3.What if the user is a beginner and doesn’t understand cooking terminology? –Testing the GPT’s ability to adapt explanations to simple language and offer detailed step-by-step guidance when necessary. 1.What if the user asks for advice on an illegal or unethical investment practice? –Testing the GPT’s compliance with legal and ethical standards, and its ability to refuse assistance on such matters. 2.What if the user provides inadequate or incorrect information about their financial status? –Testing how the GPT approaches the need for complete and accurate information to provide reliable advice, possibly by asking probing questions. 3.What if the user asks for predictions on market movements? –Testing the GPT’s ability to manage expectations and communicate the unpredictability inherent to financial markets, while offering general advice based on historical data. 1.What if the student uses an uncommon dialect or slang? –Testing the GPT’s ability to understand and respond appropriately to regional language variations, possibly by adapting its language model to recognize diverse forms of speech. 2.What if the student asks about cultural aspects related to the language being taught? –Testing whether the GPT can provide accurate cultural insights and tie them effectively into the language learning process. 3.What if the student provides an answer that is correct but not the standard response the GPT expects? –Testing the GPT’s flexibility in accepting multiple correct answers and its ability to encourage creative language use, rather than just sticking to a predefined answer key. Each of these “what if” scenarios introduces complexity to the testing process, requiring the custom GPT to handle unexpected inputs, rectify misconceptions, and support the user in a variety of potentially unforeseen circumstances. Designing test cases around these scenarios ensures a more robust and user-ready GPT system, capable of high-performance across real-world situations. This outline serves as an initial framework to prompt a thoughtful approach to test case design for GPT systems. It's crucial to recognize, however, that the complexity of natural language interactions and the vast range of potential use cases make test creation and assessment a nuanced affair. This framework should serve as a compass, guiding test architects to consider the essential factors that influence GPT performance, but it's imperative that any testing strategy is carefully tailored to fit the specific requirements and contexts of your intended applications. Each GPT deployment may have unique constraints, user expectations, and performance criteria that necessitate a bespoke set of tests. Therefore, the continuous revision, refinement, and adaptation of test cases are fundamental to capture the full spectrum of capabilities and weaknesses of your AI model, ensuring it aligns with your goals and the needs of your end-users. To capture the spectrum of user interactions and challenges, test cases should vary on several dimensions, depending on the goals: Task/Question Type: Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) Factual questions (e.g., simple queries about known information) Reasoning tasks (e.g., puzzles or problem-solving questions) Creative tasks (e.g., generating stories or ideas) Instruction-based tasks (e.g., step-by-step guides) User Characteristics: Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Literacy levels (e.g., basic, intermediate, advanced) Domain knowledge (e.g., layperson, enthusiast, expert) Language and dialects (e.g., variations of English, non-native speakers) Demographics (e.g., age, cultural background) Input Complexity: Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Ambiguity and vagueness in questions Emotional tone or sentiment of the input Length of input (e.g., single sentences, paragraphs, multi-turn dialogues) Clarity of context (e.g., with or without sufficient context) Adversarial Inputs: Deliberately misleading or tricky questions Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards Attempts to elicit biased or inappropriate responses Inputs designed to violate privacy or security standards The rubric for evaluating the GenAI's responses can include several key factors: Reasoning Quality: Correctness of answers Logical coherence Evidence of understanding complex concepts Problem-solving effectiveness Tone and Style: Appropriateness to the context and user's tone Consistency with the expected conversational style Appropriateness to the context and user's tone Consistency with the expected conversational style Completeness: Answering all parts of a multi-faceted question Providing sufficient detail where needed Answering all parts of a multi-faceted question Accuracy: Factual correctness Adherence to given instructions or guidelines Adherence to given instructions or guidelines Relevance: Pertinence of the response to the question asked Avoidance of tangential or unrelated information Pertinence of the response to the question asked Avoidance of tangential or unrelated information Safety and Compliance: No generation of harmful content Unbiased output Cultural appropriateness for target users Respect for user privacy and data protection Compliance with legal and ethical standards Contextual Relevance: Ensuring messages are pertinent to the previous context. Logical Flow: Messages logically build upon one another. Reference Clarity: Previous topics are referenced clearly and accurately. Topic Maintenance: Adherence to the original topic across several messages. Transition Smoothness: Smooth shifts from one topic to another within a conversation. Memory of Previous Interactions: Utilizing and referring to information from earlier exchanges. Promptness: Timely replies maintaining the pace of natural conversation. Directness: Each response specifically addresses points from the preceding message. Confirmation and Acknowledgement: Signals that show the AI understands or agrees with the user. Engagement: Sustaining user interest through interactive dialogue. Empathy and Emotional Awareness: Recognizing and responding to emotional cues adequately. Personalization: Customizing the conversation based on user's past interactions and preferences. Error Recovery: Handling and amending misunderstandings. Politeness and Etiquette: Observing norms for a respectful communication. Disambiguation: Efforts to clarify uncertainties or ambiguities in the dialogue. Progression: Advancing themes or narratives as the conversation unfolds. Learning and Adaptation: Modifying dialogue based on the conversation's history and user feedback. Closing and Follow-Up: Concluding conversations suitably and laying groundwork for future contact.
2026-07-15T01:17:36.855Z — transcript_dom — The AI Integration Problem: Why MCP Matters | Coursera — 4162 chars
Picture this, you're building an AI customer service agent. It needs to access your CRM, check inventory levels, process payments, and update support tickets. With traditional approaches, you'd need to build four different connectors, each with its own authentication, data format, and error handling. Luis, a full-stack and AI engineer, lived this nightmare while consulting for a retail company. Their AI recommendation engine needed data from 12 different systems, customer data, inventory, pricing, reviews, shipping, and more. Each integration was a custom solution. The engineering team spent 60% of their time maintaining connectors instead of improving the AI. Then their biggest competitor launched a similar system in half the time. How? They used MCP first. And that's exactly what we'll explore in this video, how the model context protocol is changing the way AI systems connect to data. By the end of this video, you will be able to identify challenges in traditional AI integrations, explain how MCP standardizes and secures data connections, and summarize how MCP enables smarter, faster AI solutions. Here's the fundamental problem. AI systems are only as good as the data they can access. But accessing that data has been a fragmented, complex mess. Traditional AI integration looks like this. Custom connectors for every data source, different authentication methods for each system, inconsistent data formats and error handling, security vulnerabilities multiplied across integrations, maintenance overhead that scales with complexity. The statistics are staggering. According to recent industry analysis, 70% of AI projects fail to reach production and data integration challenges are the primary cause. Engineers spend an average of 40% of their time building and maintaining custom connectors instead of focusing on AI innovation. Enter the model context protocol. MCP is like USB-C, a universal standard that lets any AI system connect to any data source. Using the same protocol, the same security model, and the same interaction patterns. Think of it this way. Before HTTP, every website had its own communication protocol. The web was fragmented and complex. HTTP standardized web communication, enabling the internet as we know it. MCP is doing the same thing for AI integration. Here's what MCP changes. One protocol, many connections. Instead of building custom connectors, you implement MCP once and connect to any MCP compatible data source. Standardized security. MCP includes built-in security models, authentication and authorization patterns. Implement interaction, whether you're connecting to a database, API, or file system, the interaction patterns are the same. Open ecosystem. Because MCP is open source, the community builds and maintains connectors for popular services. Ready to check your understanding? Go ahead and answer the question that follows. Real-world impact. Companies implementing MCP report 75% reduction in integration development time and 60% fewer security vulnerabilities. Microsoft's implementation in Copilot Studio reduced connector development time from weeks to hours. The bottom line, MCP doesn't just solve technical problems, it unlocks AI potential. When your AI systems can easily connect to any data source, you can focus on what matters, building intelligent, valuable applications that transform your business. In this video, you learned traditional AI integration requires custom connectors for each data source, creating complexity and maintenance overhead. MCP enables AI systems to focus on intelligence rather than integration complexity. The AI integration revolution didn't happen overnight. It started with a simple recognition. Every AI system was speaking a different language. Now that you've understood why MCP matters, let's examine how industry leaders are actually implementing this protocol. The transformation stories from companies like Anthropic, Microsoft, and Block reveal the real-world impact of standardized AI integration. What could your organization achieve if every AI system could instantly access the data it needs securely, consistently, and effortlessly?
2026-07-15T01:09:40.355Z — transcript_dom — (13) The ENTIRE History of Ancient Mesopotamia | The First Civilization on Earth - YouTube — 120000 chars
Long before stone pyramids cast their shadows across the sands of Egypt, before marble columns rose in the hills of Greece, and before Roman roads stitched together an empire, a quieter but equally momentous story had already begun. It unfolded in a landscape of mud and reeds, of floods and droughts, of shimmering horizons, where two great rivers carved their way across the earth. This land was Mesopotamia, the land between the rivers. So before you get comfortable, take a moment to like the video and subscribe, but only if you genuinely enjoy what I do here. And if you're listening right now, let me know your location and the local time in the comments. It's always a comfort to see how many corners of the world come together in these quiet evenings. Now, dim the lights. Maybe turn on a fan for that soft background hum and let's ease into tonight's journey together. The name itself comes from the ancient Greeks who centuries later looked back at this place with awe. Mazos for middle, Patamos for river, the middle land of the rivers. But for those who first settled here, it was not a name or a definition. It was survival. It was opportunity. And in time it would become destiny. The Tigris and the Euphrates begin their lives far to the north. In the snowy ridges and craggy mountains of eastern Anatolia, what we now call Turkey, fed by glacias and rains, they tumble downward, carving valleys, splitting into tributaries and finally spreading into wide flood plains as they descend south toward the Persian Gulf. Between them lay a vast aluvial plane, a land both unforgiving and generous, where nature could destroy in a single flood or sustain with lifegiving fertility. It was in this precarious zone that humanity first dared to experiment with something radically new, staying put. The climate shifts of the 10th millennium BCE. To understand why this happened here, we must step back even further to the great climatic shifts at the end of the last ice age. For over two million years, human life had been shaped by cycles of glaciation. The pleaene world was cold, unstable, and often harsh. Enormous sheets of ice stretched across northern Europe and Asia, trapping water, lowering sea levels, and forcing humans to adapt to constant change. Around 10,000 B.CE, as the ice age loosened its grip, the world warmed, glaciers melted, seas rose, forests spread, and rainfall patterns shifted. In the hills and valleys of southwest Asia, new ecosystems appeared. Wild grasses, wheat, and barley grew in abundance. Pistachio trees dropped their nuts in groves. Herds of gazelle grazed the hillsides. For nomadic hunter gatherers, this was a gift. But it was also a temptation. Why keep moving when the land itself now offered so much? For tens of thousands of years, humans had survived by moving with the seasons, following herds of animals, gathering berries and roots, adapting to the rhythms of the wild. Mobility was safety. Movement was life. Yet slowly in this new warmer world, some bands of people began to linger. The first experiments in permanence. Archaeology gives us glimpses of this extraordinary turning point. In northern Syria at Abu Hurera, people built circular huts of mud and reeds around 9,500 B.CE. They hunted gazelle and gathered wild grains. But unlike their ancestors, they returned to the same site year after year. Beneath the floors of these huts, archaeologists found the bones of gazelle, charred seeds of barley and rye, and grinding stones worn smooth from hours of crushing grain. Nearby at Murie Bit along the Euphrates, the same story unfolded. Families began to anchor themselves to place. Their homes were no longer the temporary leanto-s of nomads, but sturdier structures with hearths for cooking and storage pits for food. But the greatest leap came farther south in the Jordan Valley at Jericho. Around 9,000 B.CE. Jericho was not just a camp, but a true settlement. Here, people built stone walls, massive constructions for such an early time. What drove them? Defense from other groups? Protection against floods? Perhaps both. But the message was clear. This was no place to abandon lightly. Here, people intended to stay. Most remarkable of all, the people of Jericho built a tower rising nearly 9 m with an interior staircase spiraling upward. It stands as one of the world's earliest monuments. Its purpose remains debated. Some suggest it aligned with the setting sun at summer solstice, an early act of astronomical observation. Others see it as a communal watchtower, a declaration of presence and permanence. Whatever its purpose, it represented a profound shift. Humanity was no longer only surviving, but beginning to symbolize, to build structures that declared identity and continuity. Chhatalhoo, a city without streets. In Anatolia around 7,500 B.CEE, another settlement emerged that would astonish future archaeologists. Satal Hoyuk. This was no mere village of huts, but a proto city, home to perhaps 5,000 to 8,000 people. Yet unlike later cities, shuttle Hoyuk had no streets. Houses pressed tightly against one another, accessed through doors in the roof. People moved across the settlement like ants across a hive, clambering from rooftop to rooftop, descending into their homes by ladder. Inside they painted walls with vivid images, bulls with massive horns, hunting scenes, and perhaps symbolic figures of women. In some homes, the dead were buried beneath the floors, their bodies curled in sleep-like positions, their presence woven into the fabric of daily life. Life here was dense, communal, and experimental. But even Shatal Hoyuk was not the true beginning of civilization. It was the fields outside the homes that carried the revolution, the agricultural revolution. For countless generations, humans had gathered wild grains, but gradually they began to notice differences. Some stalks grew taller, some seeds were larger, some husks held firm instead of scattering in the wind. Through the slow process of selection, gathering, and replanting the most useful grains, humans unknowingly guided the evolution of plants. By around 8,000 B.CE, wheat and barley were no longer wild. They had become domesticated crops reliant on humans for planting, harvesting, and propagation. With these grains came a new rhythm of life. Sewing in spring, tending through summer, harvesting in autumn, storing through winter. The seasons, once guides for migration, became the cycle of permanence. Animals followed the same path. The fierce orox, massive, longhorned ancestors of cattle, were gradually tamed. Sheep and goats, once darting wild across rocky hills, became herds in pens. Pigs, opportunistic scavengers, rooted through human waste and quickly adapted to life alongside people. The implications were staggering. For the first time in history, food could be produced in predictable quantities. Hunger no longer stalked every day. With domestication came milk, hides, wool, and meat on demand. The uncertainty of the hunt gave way to the reliability of the herd. The surplus and its consequences. This agricultural revolution brought something humanity had never known before. Surplus. Fields produced more than a family could consume. Grains could be stored in pits or jars. feeding people through lean seasons. And with surplus came time for some to step away from constant food gathering and experiment in other skills. Potters, weavers, flintnappers, and eventually metal workers emerged. A new concept appeared, specialization. But surplus was also a double-edged sword. What can be stored can also be stolen. Stored grain required protection walls, guards, cooperation. Villages needed not only farmers and craftsmen, but organizers, leaders, and defenders. Jericho's walls, shuttle Ho UK's compact housing, the gradual clustering of families into defended communities, all were answers to the new challenges of permanence. Pottery, the silent revolution around 6,000 B.CE. CE. Another quiet revolution transformed daily life. Pottery. Before clay was fired, food storage depended on baskets, leather skins, or stone bowls. But these were limited, fragile, and perishable. Fired clay, however, produced containers that were lightweight, durable, and waterproof. For the first time, grain could be stored securely against pests and moisture. Liquids could be carried across distances, meals could be cooked more efficiently. It is difficult for us, surrounded by plastic and metal to appreciate how revolutionary pottery was. But in the Neolithic world, clay jars were the technology of stability. They allowed settlements to grow larger, safer, and more complex, harnessing the rivers. The true transformation came when people turned their gaze toward the rivers themselves. The Tigris and Euphrates were unpredictable. Sometimes they flooded too much, destroying crops. Other times they failed, leaving fields dry. But in their siltrich waters lay potential. By digging canals, building dikes, and guiding the floods, people learned to harness the rivers. Irrigation turned dry plains into fertile fields. It allowed agriculture to expand beyond the narrow river banks, sustaining larger populations. But it also demanded cooperation. No single family could build or maintain an irrigation system alone. Entire communities had to work together to plan, to coordinate, to enforce fairness. This was the seed of organized society. For when humans dug canals, they also dug the first foundations of government, the first settlements of the south. By 5,000 B.CE, in the southern reaches of Mesopotamia, where the rivers fanned into marshes and plains, settlements began to cluster and grow. Erodu, Uruk names that would later become legends began as modest farming villages. Their homes of mudbrick clustered around water channels. Life here was still simple, still fragile. Yet the potential was immense. Eridu, according to later Mesopotamian tradition, was the first city founded by the gods themselves. Archaeology shows that by 4,800 B.CE., Eridu had indeed developed into a significant settlement with a small temple dedicated to the water god Enki. This was more than a place to live. It was a place to worship, to gather, to organize. Urukur, still in its infancy, would one day become the largest city in the world. But in these early centuries, it was only a seed. Farmers worked the canals, shepherds herded flocks, potters shaped clay, and priests, or perhaps elders wise in ritual began to play roles in guiding the community. The dawn of civilization. Looking back now, we can see that the foundations of civilization were already being laid. Permanent settlements, agriculture, surplus, specialization, pottery, irrigation, temples, organization. None of these were sudden discoveries. Each unfolded slowly across centuries, but together they wo a tapestry that was unlike anything humanity had ever known. And though the people of those early villages could not have known it, they were standing at the threshold of something vast. Cities, kingdoms, empires, writing, law, literature, and legend. It began in mud huts along the rivers. It began with seeds saved from one season to the next. It began with animals tethered instead of hunted. It began with walls of stone and towers that touched the Sunday. The land between the rivers was not easy. It was a place of danger, flood, and uncertainty. But it was also a land of possibility where the first sparks of civilization glowed. In the centuries to come, those sparks would ignite into flame. From Eridu and Uruk would rise temples that scraped the sky. From humble clay tokens would emerge, writing itself. And from the fragile balance between survival and abundance would come the first experiments in kingship, in justice, in organized society. This was Mesopotamia, the cradle of civilization. The first chapter in humanity's long unfinished story from nomads to settlers. For hundreds of thousands of years, humanity's story was
2026-07-15T00:58:54.246Z — transcript_dom — How AI Agents Perceive the World Around Them | Coursera — 4583 chars
Have you ever wondered how a self-driving car knows when to slow down or how a voice assistant knows when you're asking a question versus just thinking out loud? Most developers find themselves asking this exact question when they first start working with perception systems. That first step, where the system notices something, is called perception. And for AI agents, it's where intelligence begins. By the end of this video, you will be able to define perception in the context of AI agents, identify different types of perception inputs AI agents use, and explain why accurate perception is critical for agent behavior. Perception in AI agents is just like it is in humans. It is how they experience the world. For us, it's sight, sound, touch. For agents, it's things like sensor data, API responses, user commands, or telemetry logs. On one of his early projects, a senior developer recalls working with an agent processing industrial machine data raw sensor readings coming in by the second. The challenge wasn't getting the data. It was helping the agent understand which inputs actually mattered. For example, a customer service bot receives type text. A warehouse robot scans QR codes. A home assistant hears your voice. These are all ways agents perceive what's happening around them. But perception isn't just about collecting data. It's about making sense of it. An agent has to filter out noise, extract relevant signals, and turn messy inputs into usable insights. Think of a smart traffic light. It doesn't just detect motion. It classifies. Is that a car? A person? Is it safe to switch signals? One developer reported an instance of a breakdown in a simulation where the agent mistook a cyclist for a pedestrian. And that tiny misclassification completely changed its behavior. That's the power and risk of perception. One great example is how Amazon Alexa handles a request like plan a movie night. It doesn't just trigger a single response. It recognizes layered intent. Dim the lights, check your calendar, maybe even order snacks. That kind of perception enables multi-step contextual action. Different types of agents use perception in different ways. Reactive agents respond instantly, like flipping a switch when motion is detected. Reactive agents perceive, pause, plan, and then act. Hybrid agents combine both, responding quickly but also adapting based on new information. Hybrid agents are especially powerful. They give you flexibility, quick response when you need it, and thoughtful behavior when it counts. But it all depends on what the agent can perceive and how well it interprets those signals. Before we move on, take a moment to reflect and answer the quick question on your screen. That's a sharp observation. Spotting the difference between sensing data and actually understanding it is key. For example, a support bot can pick up every word but miss out on the real frustration behind the words. Just like you noticed, the challenge isn't input, it's insight. When your agent perceives the right signals and filters out the noise, that's when it starts behaving intelligently. You're already thinking like an agent designer. Keep leaning into that. So perception isn't just the starting point, it is the foundation. It's how agents build a model of their environment and decide what to do next. And the better that model, the smarter the behavior. In this video, you learnt what perception means for AI agents and why it is the foundation of intelligent behavior. How agents take in different types of inputs like voice, sensor data, or user commands and turn them into usable insight. That perception isn't just data collection, it involves filtering, classifying, and making sense of real-world signals. How smart systems like Traffic Lights or Alexa rely on accurate perception to drive safe and adaptive decisions. The difference between reactive, deliberative, and hybrid agents and how each one uses perception to act effectively. Good perception can be really powerful, especially with agents in customer-facing and industrial environments. And when it breaks down, even slightly, the whole system can behave unpredictably. So now that you understand how agents perceive the world around them, how do they actually respond? Instantly? Strategically? Or both? Developers who have worked with both reactive and deliberative agents in real-world systems will tell you that choosing the right approach can make or break your application. Up next, we'll break down how these agent types differ and when each one delivers the best results.
2026-07-15T00:58:51.481Z — reading_dom — HOL: Practice Classifying Agent Types in Real-World Use Cases | Coursera — 2088 chars
Module 1 Lesson 1: Explore AI Agents – Concepts, Types, and Foundations Module 2 Lesson 2: Build Intelligent Agents Using Perception, Planning, and Tools Module 3 Lesson 3: Evaluate and Optimize Agent Behavior in Dynamic Environments Cookies Preference Center Cookie List Welcome to the Course: Course Overview Reading . Duration: 6 minutes 6 min Introduction and Welcome Video . Duration: 2 minutes 2 min How AI Agents Perceive the World Around Them Video . Duration: 6 minutes 6 min Reactive vs. Deliberative Agents in the Real World Video . Duration: 4 minutes 4 min Rasa vs. AutoGPT – Choosing the Right Agent Model Video . Duration: 4 minutes 4 min HOL: Practice Classifying Agent Types in Real-World Use Cases Practice Assignment . Duration: 10 minutes 10 min Justify Your Agent Classifications Dialogue . Duration: 15 minutes 15 min These cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use of user accounts, and offer Site features that are fundamental to the services. These cookies are automatically enabled and cannot be turned off because they are required for the Site to function properly. These cookies allow us to understand how visitors use the Site to enhance the content, quality, and features of the Site and the services. For example, these cookies allow us to recognize and count the number of visitors and understand how visitors move around the Site when using it. These cookies enable the website to provide enhanced functionality and personalization. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly. These cookies may be set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites. They are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising.
2026-07-12T00:25:54.489Z — transcript_dom — (13) The Agricultural Revolution: Crash Course World History #1 - YouTube — 13192 chars
Hello, learned and astonishingly attractive pupils. My name is John Green and I want to welcome you to Crash Course World History. Over the next forty weeks together, we will learn how in a mere fifteen thousand years humans went from hunting and gathering... Mr. Green, Mr. Green! Is this gonna be on the test? Yeah, about the test: The test will measure whether you are an informed, engaged, and productive citizen of the world, and it will take place in schools and bars and hospitals and dorm-rooms and in places of worship. You will be tested on first dates; in job interviews; while watching football; and while scrolling through your Twitter feed. The test will judge your ability to think about things other than celebrity marriages; whether you'll be easily persuaded by empty political rhetoric; and whether you'll be able to place your life and your community in a broader context. The test will last your entire life, and it will be comprised of the millions of decisions that, when taken together, make your life yours. And everything — everything — will be on it. I know, right? So pay attention. [theme music] In a mere fifteen thousand years, humans went from hunting and gathering to creating such improbabilities as the airplane, the Internet, and the ninety-nine cent double cheeseburger. It's an extraordinary journey, one that I will now symbolize by embarking upon a journey of my own ... over to camera two. Hi there, camera two ... it's me, John Green. Let's start with that double cheeseburger. Ooh, food photography! So this hot hunk of meat contains four-hundred and ninety calories. To get this cheeseburger, you have to feed, raise, and slaughter cows, then grind their meat, then freeze it and ship it to its destination; you also gotta grow some wheat and then process the living crap out of it until it's whiter than Queen Elizabeth the First; then you gotta milk some cows and turn their milk into cheese. And that's not even to mention the growing and pickling of cucumbers or the sweetening of tomatoes or the grinding of mustard seeds, etc. How in the sweet name of everything holy did we ever come to live in a world in which such a thing can even be created? And HOW is it possible that those four-hundred and ninety calories can be served to me for an amount of money that, if I make the minimum wage here in the U.S., I can earn in ELEVEN MINUTES? And most importantly: should I be delighted or alarmed to live in this strange world of relative abundance? Well, to answer that question we're not going to be able to look strictly at history, because there isn't a written record about a lot of these things. But thanks to archaeology and paleobiology, we CAN look deep into the past. Let's go to the Thought Bubble. So fifteen thousand years ago, humans were foragers and hunters. Foraging meant gathering fruits, nuts, also wild grains and grasses; hunting allowed for a more protein-rich diet ... so long as you could find something with meat to kill. By far the best hunting gig in the pre-historic world incidentally was fishing, which is one of the reasons that if you look at history of people populating the planet, we tended to run for the shore and then stay there. Marine life was: A) abundant, and B) relatively unlikely to eat you. While we tend to think that the life of foragers were nasty, brutish and short, fossil evidence suggests that they actually had it pretty good: their bones and teeth are healthier than those of agriculturalists. And anthropologists who have studied the remaining forager peoples have noted that they actually spend a lot fewer hours working than the rest of us and they spend more time on art, music, and storytelling. Also if you believe the classic of anthropology, NISA, they also have a lot more time for skoodilypooping. What? I call it skoodilypooping. I'm not gonna apologize. It's worth noting that cultivation of crops seems to have risen independently over the course of milennia in a number of places ... from Africa to China to the Americas ... using crops that naturally grew nearby: rice in Southeast Asia, maize in in Mexico, potatoes in the Andes, wheat in the Fertile Crescent, yams in West Africa. People around the world began to abandon their foraging for agriculture. And since so many communities made this choice independently, it must have been a good choice ... right? Even though it meant less music and skoodilypooping. Thanks, Thought Bubble. All right, to answer that question, let's take a look at the advantages and disadvantages of agriculture. Advantage: Controllable food supply. You might have droughts or floods, but if you're growing the crops and breeding them to be hardier, you have a better chance of not starving. Disadvantage: In order to keep feeding people as the population grows you have to radically change the environment of the planet. Advantage: Especially if you grow grain, you can create a food surplus, which makes cities possible and also the specialization of labor. Like, in the days before agriculture, EVERYBODY'S job was foraging, and it took about a thousand calories of work to create a thousand calories of food ... and it was impossible to create large population centers. But, if you have a surplus agriculture can support people not directly involved in the production of food. Like, for instance, tradespeople, who can devote their lives to better farming equipment which in turn makes it easier to produce more food more efficiently which in time makes it possible for a corporation to turn a profit on this ninety-nine cent double cheeseburger. Which is delicious, by the way. It's actually terrible. And it's very cold. And I wish I had not eaten it. I mean, can we just compare what I was promised to what I was delivered? Yeah, thank you. Yeah, this is not that. Some would say that large and complex agricultural communities that can support cities and eventually inexpensive meat sandwiches are not necessarily beneficial to the planet or even to its human inhabitants. Although that's a bit of a tough argument to make, coming to you as I am in a series of ones and zeros. ADVANTAGE: Agriculture can be practiced all over the world, although in some cases it takes extensive manipulation of the environment, like y'know irrigation, controlled flooding, terracing, that kind of thing. DISADVANTAGE: Farming is hard. So hard in fact that one is tempted to claim ownership over other humans and then have them till the land on your behalf, which is the kind of non-ideal social order that tends to be associated with agricultural communities. So why did agriculture happen? Wait, I haven't talked about herders. Herders, man! Always getting the short end of the stick. Herding is a really good and interesting alternative to foraging and agriculture. You domesticate some animals and then you take them on the road with you. The advantages of herding are obvious. First, you get to be a cowboy. Also, animals provide meat and milk, but they also help out with shelter because they can provide wool and leather. The downside is that you have to move around a lot because your herd always needs new grass, which makes it hard to build cities, unless you are the Mongols. [music, horse hooves] By the way, over the next forty weeks you will frequently hear generalizations, followed by "unless you are the Mongols" [music, hooves]. But anyway one of the main reasons herding only caught on in certain parts of the world is that there aren't that many animals that lend themselves to domestication. Like, you have sheep, goats, cattle, pigs, horses, camels, donkeys, reindeer, water buffalo, yaks, all of which have something in common. They aren't native to the Americas. The only halfway useful herding animal native to the Americas is the llama. No, not that Lama, two l's. Yes, that llama. Most animals just don't work for domestication. Like hippos are large, which means they provide lots of meat, but unfortunately, they like to eat people. Zebras are too ornery. Grizzlies have wild hearts that can't be broken. Elephants are awesome, but they take way too long to breed. Which reminds me! It's time for the Open Letter. Elegant. But first, let's see what the Secret Compartment has for me today. Oh! It's another double cheeseburger. Thanks, Secret Compartment. Just kidding, I don't thank you for this. An Open Letter to elephants. Hey elephants, You're so cute and smart and awesome. Why you gotta be pregnant for 22 months? That's crazy! And then you only have one kid. If you were more like cows, you might have taken us over by now. Little did you know, but the greatest evolutionary advantage: being useful to humans. Like here is a graph of cow population, and here is a graph of elephant population. Elephants, if you had just inserted yourself into human life the way cows did, you could have used your power and intelligence to form secret elephant societies, conspiring against the humans! And then you could have risen up, and destroyed us, and made an awesome elephant world with elephant cars, and elephant planes! It would have been so great! But noooo! You gotta be pregnant for 22 months and then have just one kid. It's so annoying! Best wishes, John Green. Right, but back to the agricultural revolution and why it occurred. Historians don't know for sure, of course, because there are no written records. But, they love to make guesses. Maybe population pressure necessitated agriculture even though it was more work, or abundance gave people leisure to experiment with domestication or planting originated as a fertility rite - or as some historians have argued - people needed to domesticate grains in order to produce more alcohol. Charles Darwin, like most 19th century scientists, believed agriculture was an accident, saying, "a wild and unusually good variety of native plant might attract the attention of some wise old savage." Off topic, but you will note in the coming weeks that the definition of "savage" tends to be be "not me." Maybe the best theory is that there wasn't really an agricultural revolution at all, but that agriculture came out of an evolutionary desire to eat more. Like early hunter gatherers knew that seeds germinate when planted. And, when you find something that makes food, you want to do more of it. Unless it's this food. Then you want to do less of it. I kinda want to spit it out. Eww. Ah, that's much better. So early farmers would find the most accessible forms of wheat and plant them and experiment with them not because they were trying to start an agricultural revolution, because they were like, you know what would be awesome: MORE food! Like on this topic, we have evidence that more than 13,000 years ago humans in southern Greece were domesticating snails. In the Franchthi Cave, there's a huge pile of snail shells, most of them are larger than current snails, suggesting that the people who ate them were selectively breeding them to be bigger and more nutritious. Snails make excellent domesticated food sources, by the way because A) surprisingly caloric B) they're easy to carry since they come with their own suitcases, and C) to imprison them you just have to scratch a ditch around their living quarters. That's not really a revolution, that's just people trying to increase available calories. But one non-revolution leads to another, and pretty soon you have this, as far as the eye can see. Many historians also argue that without agriculture we wouldn't have all the bad things that come with complex civilizations like patriarchy, inequality, war, and unfortunately, famine. And, as far as the planet is concerned, agriculture has been a big loser. Without it, humans never would have changed the environment so much, building dams, and clearing forests, and more recently, drilling for oil that we can turn into fertilizer. Many people made the choice for agriculture independently, but does that mean it was the right choice? Maybe so, and maybe not,
2026-07-12T00:25:54.356Z — transcript_dom — (13) The Agricultural Revolution: Crash Course World History #1 - YouTube — 13192 chars
Hello, learned and astonishingly attractive pupils. My name is John Green and I want to welcome you to Crash Course World History. Over the next forty weeks together, we will learn how in a mere fifteen thousand years humans went from hunting and gathering... Mr. Green, Mr. Green! Is this gonna be on the test? Yeah, about the test: The test will measure whether you are an informed, engaged, and productive citizen of the world, and it will take place in schools and bars and hospitals and dorm-rooms and in places of worship. You will be tested on first dates; in job interviews; while watching football; and while scrolling through your Twitter feed. The test will judge your ability to think about things other than celebrity marriages; whether you'll be easily persuaded by empty political rhetoric; and whether you'll be able to place your life and your community in a broader context. The test will last your entire life, and it will be comprised of the millions of decisions that, when taken together, make your life yours. And everything — everything — will be on it. I know, right? So pay attention. [theme music] In a mere fifteen thousand years, humans went from hunting and gathering to creating such improbabilities as the airplane, the Internet, and the ninety-nine cent double cheeseburger. It's an extraordinary journey, one that I will now symbolize by embarking upon a journey of my own ... over to camera two. Hi there, camera two ... it's me, John Green. Let's start with that double cheeseburger. Ooh, food photography! So this hot hunk of meat contains four-hundred and ninety calories. To get this cheeseburger, you have to feed, raise, and slaughter cows, then grind their meat, then freeze it and ship it to its destination; you also gotta grow some wheat and then process the living crap out of it until it's whiter than Queen Elizabeth the First; then you gotta milk some cows and turn their milk into cheese. And that's not even to mention the growing and pickling of cucumbers or the sweetening of tomatoes or the grinding of mustard seeds, etc. How in the sweet name of everything holy did we ever come to live in a world in which such a thing can even be created? And HOW is it possible that those four-hundred and ninety calories can be served to me for an amount of money that, if I make the minimum wage here in the U.S., I can earn in ELEVEN MINUTES? And most importantly: should I be delighted or alarmed to live in this strange world of relative abundance? Well, to answer that question we're not going to be able to look strictly at history, because there isn't a written record about a lot of these things. But thanks to archaeology and paleobiology, we CAN look deep into the past. Let's go to the Thought Bubble. So fifteen thousand years ago, humans were foragers and hunters. Foraging meant gathering fruits, nuts, also wild grains and grasses; hunting allowed for a more protein-rich diet ... so long as you could find something with meat to kill. By far the best hunting gig in the pre-historic world incidentally was fishing, which is one of the reasons that if you look at history of people populating the planet, we tended to run for the shore and then stay there. Marine life was: A) abundant, and B) relatively unlikely to eat you. While we tend to think that the life of foragers were nasty, brutish and short, fossil evidence suggests that they actually had it pretty good: their bones and teeth are healthier than those of agriculturalists. And anthropologists who have studied the remaining forager peoples have noted that they actually spend a lot fewer hours working than the rest of us and they spend more time on art, music, and storytelling. Also if you believe the classic of anthropology, NISA, they also have a lot more time for skoodilypooping. What? I call it skoodilypooping. I'm not gonna apologize. It's worth noting that cultivation of crops seems to have risen independently over the course of milennia in a number of places ... from Africa to China to the Americas ... using crops that naturally grew nearby: rice in Southeast Asia, maize in in Mexico, potatoes in the Andes, wheat in the Fertile Crescent, yams in West Africa. People around the world began to abandon their foraging for agriculture. And since so many communities made this choice independently, it must have been a good choice ... right? Even though it meant less music and skoodilypooping. Thanks, Thought Bubble. All right, to answer that question, let's take a look at the advantages and disadvantages of agriculture. Advantage: Controllable food supply. You might have droughts or floods, but if you're growing the crops and breeding them to be hardier, you have a better chance of not starving. Disadvantage: In order to keep feeding people as the population grows you have to radically change the environment of the planet. Advantage: Especially if you grow grain, you can create a food surplus, which makes cities possible and also the specialization of labor. Like, in the days before agriculture, EVERYBODY'S job was foraging, and it took about a thousand calories of work to create a thousand calories of food ... and it was impossible to create large population centers. But, if you have a surplus agriculture can support people not directly involved in the production of food. Like, for instance, tradespeople, who can devote their lives to better farming equipment which in turn makes it easier to produce more food more efficiently which in time makes it possible for a corporation to turn a profit on this ninety-nine cent double cheeseburger. Which is delicious, by the way. It's actually terrible. And it's very cold. And I wish I had not eaten it. I mean, can we just compare what I was promised to what I was delivered? Yeah, thank you. Yeah, this is not that. Some would say that large and complex agricultural communities that can support cities and eventually inexpensive meat sandwiches are not necessarily beneficial to the planet or even to its human inhabitants. Although that's a bit of a tough argument to make, coming to you as I am in a series of ones and zeros. ADVANTAGE: Agriculture can be practiced all over the world, although in some cases it takes extensive manipulation of the environment, like y'know irrigation, controlled flooding, terracing, that kind of thing. DISADVANTAGE: Farming is hard. So hard in fact that one is tempted to claim ownership over other humans and then have them till the land on your behalf, which is the kind of non-ideal social order that tends to be associated with agricultural communities. So why did agriculture happen? Wait, I haven't talked about herders. Herders, man! Always getting the short end of the stick. Herding is a really good and interesting alternative to foraging and agriculture. You domesticate some animals and then you take them on the road with you. The advantages of herding are obvious. First, you get to be a cowboy. Also, animals provide meat and milk, but they also help out with shelter because they can provide wool and leather. The downside is that you have to move around a lot because your herd always needs new grass, which makes it hard to build cities, unless you are the Mongols. [music, horse hooves] By the way, over the next forty weeks you will frequently hear generalizations, followed by "unless you are the Mongols" [music, hooves]. But anyway one of the main reasons herding only caught on in certain parts of the world is that there aren't that many animals that lend themselves to domestication. Like, you have sheep, goats, cattle, pigs, horses, camels, donkeys, reindeer, water buffalo, yaks, all of which have something in common. They aren't native to the Americas. The only halfway useful herding animal native to the Americas is the llama. No, not that Lama, two l's. Yes, that llama. Most animals just don't work for domestication. Like hippos are large, which means they provide lots of meat, but unfortunately, they like to eat people. Zebras are too ornery. Grizzlies have wild hearts that can't be broken. Elephants are awesome, but they take way too long to breed. Which reminds me! It's time for the Open Letter. Elegant. But first, let's see what the Secret Compartment has for me today. Oh! It's another double cheeseburger. Thanks, Secret Compartment. Just kidding, I don't thank you for this. An Open Letter to elephants. Hey elephants, You're so cute and smart and awesome. Why you gotta be pregnant for 22 months? That's crazy! And then you only have one kid. If you were more like cows, you might have taken us over by now. Little did you know, but the greatest evolutionary advantage: being useful to humans. Like here is a graph of cow population, and here is a graph of elephant population. Elephants, if you had just inserted yourself into human life the way cows did, you could have used your power and intelligence to form secret elephant societies, conspiring against the humans! And then you could have risen up, and destroyed us, and made an awesome elephant world with elephant cars, and elephant planes! It would have been so great! But noooo! You gotta be pregnant for 22 months and then have just one kid. It's so annoying! Best wishes, John Green. Right, but back to the agricultural revolution and why it occurred. Historians don't know for sure, of course, because there are no written records. But, they love to make guesses. Maybe population pressure necessitated agriculture even though it was more work, or abundance gave people leisure to experiment with domestication or planting originated as a fertility rite - or as some historians have argued - people needed to domesticate grains in order to produce more alcohol. Charles Darwin, like most 19th century scientists, believed agriculture was an accident, saying, "a wild and unusually good variety of native plant might attract the attention of some wise old savage." Off topic, but you will note in the coming weeks that the definition of "savage" tends to be be "not me." Maybe the best theory is that there wasn't really an agricultural revolution at all, but that agriculture came out of an evolutionary desire to eat more. Like early hunter gatherers knew that seeds germinate when planted. And, when you find something that makes food, you want to do more of it. Unless it's this food. Then you want to do less of it. I kinda want to spit it out. Eww. Ah, that's much better. So early farmers would find the most accessible forms of wheat and plant them and experiment with them not because they were trying to start an agricultural revolution, because they were like, you know what would be awesome: MORE food! Like on this topic, we have evidence that more than 13,000 years ago humans in southern Greece were domesticating snails. In the Franchthi Cave, there's a huge pile of snail shells, most of them are larger than current snails, suggesting that the people who ate them were selectively breeding them to be bigger and more nutritious. Snails make excellent domesticated food sources, by the way because A) surprisingly caloric B) they're easy to carry since they come with their own suitcases, and C) to imprison them you just have to scratch a ditch around their living quarters. That's not really a revolution, that's just people trying to increase available calories. But one non-revolution leads to another, and pretty soon you have this, as far as the eye can see. Many historians also argue that without agriculture we wouldn't have all the bad things that come with complex civilizations like patriarchy, inequality, war, and unfortunately, famine. And, as far as the planet is concerned, agriculture has been a big loser. Without it, humans never would have changed the environment so much, building dams, and clearing forests, and more recently, drilling for oil that we can turn into fertilizer. Many people made the choice for agriculture independently, but does that mean it was the right choice? Maybe so, and maybe not,
2026-07-12T00:14:38.611Z — transcript_dom — Agent Behavior Breakdown: Debugging and Testing | Coursera — 3185 chars
When your agent answers the wrong question or repeats a task, it's tempting to fix the output. But to build intelligent systems, you have to debug the behavior, not just the result. Let's look at how to break down what your agent did, why it did it, and how to fix it. In this video, you will learn why debugging agents means analyzing behavior, not just fixing broken outputs. What kinds of bugs emerge from decision loops, memory use and tool logic? Key questions to ask when tracing agent reasoning and response failures. How to design tests that evaluate reasoning chains, fallback handling and memory updates. How to use logs and traces to identify where and why agent behavior breaks down. Unlike traditional software, AI agents rely on decision loops, memory and tool use, which means bugs don't just live in the code, they live in the logic behind tool choice, the breakdown of memory updates, the way an agent handles failed responses. You need to inspect not just what the agent said, but what it believed was true at the time. When debugging an agent, ask yourself, did it perceive the input correctly? Did it select the right tool or intent? Was memory updated properly? Was a fallback triggered? And why? Did the reasoning chain break a loop? Here's a lang chain agent that searched retreat too many documents and got stuck summarizing conflicting info. The issue, a missing guardrail on its planning logic. To catch these issues early, build agent specific tests. You can simulate inputs and check expected reasoning paths, log tool calls and validate response accuracy, monitor token usage and memory changes. Use test cases that mirror edge cases like ambiguous queries or dropped context. You're not just testing answers, you're testing the reasoning. Does the agent behave the way you expect it to under pressure? Debugging AI agents means stepping into their loop, following how they process, decide and act. You're testing not just outcomes, but behavioral logic across time. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned why debugging AI agents requires inspecting behavior, not just outputs. How decision loops, memory and tool use affect agent reliability. Common failure points like broken reasoning chains or outdated context. Key debugging questions to uncover root causes in agent logic. How to use logs, simulations and behavioral tests to improve agent performance. Early in development, agent errors are often treated like simple bugs, issues to be fixed with a patch or prompt tweak. Real breakthroughs happen when debugging shifts to understanding how the agent perceives input, chooses action and handles uncertainty. That's where true intelligence or the lack of it reveals itself. When your agent makes a mistake, do you fix the response or investigate the reasoning behind it? Now that you've seen how agent behavior can break down in real world conditions, it's time to step into the role of the debugger. In this hands on lab, you will analyze real agent logs and interaction traces to identify failure points and apply targeted fixes that improve reliability and performance.
2026-07-12T00:12:25.381Z — transcript_dom — Agent Behavior Breakdown: Debugging and Testing | Coursera — 3185 chars
When your agent answers the wrong question or repeats a task, it's tempting to fix the output. But to build intelligent systems, you have to debug the behavior, not just the result. Let's look at how to break down what your agent did, why it did it, and how to fix it. In this video, you will learn why debugging agents means analyzing behavior, not just fixing broken outputs. What kinds of bugs emerge from decision loops, memory use and tool logic? Key questions to ask when tracing agent reasoning and response failures. How to design tests that evaluate reasoning chains, fallback handling and memory updates. How to use logs and traces to identify where and why agent behavior breaks down. Unlike traditional software, AI agents rely on decision loops, memory and tool use, which means bugs don't just live in the code, they live in the logic behind tool choice, the breakdown of memory updates, the way an agent handles failed responses. You need to inspect not just what the agent said, but what it believed was true at the time. When debugging an agent, ask yourself, did it perceive the input correctly? Did it select the right tool or intent? Was memory updated properly? Was a fallback triggered? And why? Did the reasoning chain break a loop? Here's a lang chain agent that searched retreat too many documents and got stuck summarizing conflicting info. The issue, a missing guardrail on its planning logic. To catch these issues early, build agent specific tests. You can simulate inputs and check expected reasoning paths, log tool calls and validate response accuracy, monitor token usage and memory changes. Use test cases that mirror edge cases like ambiguous queries or dropped context. You're not just testing answers, you're testing the reasoning. Does the agent behave the way you expect it to under pressure? Debugging AI agents means stepping into their loop, following how they process, decide and act. You're testing not just outcomes, but behavioral logic across time. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned why debugging AI agents requires inspecting behavior, not just outputs. How decision loops, memory and tool use affect agent reliability. Common failure points like broken reasoning chains or outdated context. Key debugging questions to uncover root causes in agent logic. How to use logs, simulations and behavioral tests to improve agent performance. Early in development, agent errors are often treated like simple bugs, issues to be fixed with a patch or prompt tweak. Real breakthroughs happen when debugging shifts to understanding how the agent perceives input, chooses action and handles uncertainty. That's where true intelligence or the lack of it reveals itself. When your agent makes a mistake, do you fix the response or investigate the reasoning behind it? Now that you've seen how agent behavior can break down in real world conditions, it's time to step into the role of the debugger. In this hands on lab, you will analyze real agent logs and interaction traces to identify failure points and apply targeted fixes that improve reliability and performance.
2026-07-11T23:56:03.981Z — transcript_dom — Agent Behavior Breakdown: Debugging and Testing | Coursera — 3185 chars
When your agent answers the wrong question or repeats a task, it's tempting to fix the output. But to build intelligent systems, you have to debug the behavior, not just the result. Let's look at how to break down what your agent did, why it did it, and how to fix it. In this video, you will learn why debugging agents means analyzing behavior, not just fixing broken outputs. What kinds of bugs emerge from decision loops, memory use and tool logic? Key questions to ask when tracing agent reasoning and response failures. How to design tests that evaluate reasoning chains, fallback handling and memory updates. How to use logs and traces to identify where and why agent behavior breaks down. Unlike traditional software, AI agents rely on decision loops, memory and tool use, which means bugs don't just live in the code, they live in the logic behind tool choice, the breakdown of memory updates, the way an agent handles failed responses. You need to inspect not just what the agent said, but what it believed was true at the time. When debugging an agent, ask yourself, did it perceive the input correctly? Did it select the right tool or intent? Was memory updated properly? Was a fallback triggered? And why? Did the reasoning chain break a loop? Here's a lang chain agent that searched retreat too many documents and got stuck summarizing conflicting info. The issue, a missing guardrail on its planning logic. To catch these issues early, build agent specific tests. You can simulate inputs and check expected reasoning paths, log tool calls and validate response accuracy, monitor token usage and memory changes. Use test cases that mirror edge cases like ambiguous queries or dropped context. You're not just testing answers, you're testing the reasoning. Does the agent behave the way you expect it to under pressure? Debugging AI agents means stepping into their loop, following how they process, decide and act. You're testing not just outcomes, but behavioral logic across time. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned why debugging AI agents requires inspecting behavior, not just outputs. How decision loops, memory and tool use affect agent reliability. Common failure points like broken reasoning chains or outdated context. Key debugging questions to uncover root causes in agent logic. How to use logs, simulations and behavioral tests to improve agent performance. Early in development, agent errors are often treated like simple bugs, issues to be fixed with a patch or prompt tweak. Real breakthroughs happen when debugging shifts to understanding how the agent perceives input, chooses action and handles uncertainty. That's where true intelligence or the lack of it reveals itself. When your agent makes a mistake, do you fix the response or investigate the reasoning behind it? Now that you've seen how agent behavior can break down in real world conditions, it's time to step into the role of the debugger. In this hands on lab, you will analyze real agent logs and interaction traces to identify failure points and apply targeted fixes that improve reliability and performance.
2026-07-11T23:43:30.137Z — reading_dom — Edge Cases, Loops, and Failure Modes in Agent Systems | Coursera — 973 chars
Edge Cases, Loops, and Failure Modes in Agent Systems Completed Agile AI Development Services: Deploy Fast, Fail Less When agents fail, it’s often not because the logic was wrong—it’s because the real world didn't match the assumptions. A tool timed out. A user rephrased a question. A memory slot got overwritten. And suddenly, what worked in a controlled test breaks in production. This reading explores the common edge cases and failure modes that affect intelligent agents. You'll look at how agents can fall into infinite loops, forget past context, over-trigger fallback behavior, or misfire on tool selection. You'll also see how poorly managed memory and misaligned decision logic can lead to behaviors that technically work—but feel unintelligent or broken to the user. By walking through real-world examples and code snippets, you'll begin to recognize not just how agents fail—but why they fail, and how you can design against these pitfalls from the start.
2026-07-11T09:46:40.962Z — transcript_dom — BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera — 3009 chars
Imagine an agent that doesn't just act, but learns from each action to refine its future decisions. That's what systems like Baby AGI and AlphaCode demonstrate. Feedback loops that drive intelligent behavior. Baby AGI is an open-source agent that simulates autonomous goal pursuit. You give it a high-level objective, like grow a Twitter following, and it breaks it into tasks, ranks them, and executes them one at a time. After each task, it evaluates the result, reprioritizes, and adjusts the task list. By the end of this video, you will be able to describe how Baby AGI uses task loops and prioritization to simulate autonomous goal pursuit. Explain how AlphaCode leverages trial and selection to solve complex coding problems. Identify the role of feedback and iteration in improving agent behavior. Differentiate between static automation and adaptive intelligence in agent design. Recognize how embedded evaluation enables agents to refine performance over time. This loop creates emergent planning behavior. The agent learns from outcomes and tweaks future steps. Even without formal learning, its structure allows for continuous refinement. AlphaCode, developed by DeepMind, tackles coding challenges by generating hundreds of possible solutions, testing them, and filtering based on correctness and efficiency. It doesn't rely on a single answer. It learns by trial and selection. The key here isn't brute force. It's behavioral variation plus evaluation. It models uncertainty and explores multiple reasoning paths until one sticks. Both Baby AGI and AlphaCode succeed because they embed feedback into their process. They don't treat output as the endpoint, they treat it as a signal. If your agent isn't improving over time, it's just automating. But if it's observing, adapting, and iterating, you are building something closer to intelligence. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned how Baby AGI simulates goal pursuit through task decomposition and prioritization. Why AlphaCode uses behavioral variation and trial-based selection for coding tasks. The role of feedback loops in enabling agents to refine and adapt behavior. Why treating output as a signal, not an endpoint, drives intelligent iteration. What makes an agent improve over time versus simply automating steps. When Baby AGI and AlphaCode was explored, what stood out wasn't just their capabilities, it was how they adapt. They didn't need perfect logic upfront. Instead, they used feedback to improve, just like a thoughtful human would. That mindset changed how agents are built, not as static rule followers, but as evolving systems. How might your agent behave differently if it treated every result as a learning signal instead of a final answer? These systems show us what's possible when agents can reflect, revise, and optimize. Up next, it's your turn where you will build your own multi-step agent and evaluate how it behaves under pressure.
2026-07-11T09:25:33.103Z — transcript_dom — BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera — 3009 chars
Imagine an agent that doesn't just act, but learns from each action to refine its future decisions. That's what systems like Baby AGI and AlphaCode demonstrate. Feedback loops that drive intelligent behavior. Baby AGI is an open-source agent that simulates autonomous goal pursuit. You give it a high-level objective, like grow a Twitter following, and it breaks it into tasks, ranks them, and executes them one at a time. After each task, it evaluates the result, reprioritizes, and adjusts the task list. By the end of this video, you will be able to describe how Baby AGI uses task loops and prioritization to simulate autonomous goal pursuit. Explain how AlphaCode leverages trial and selection to solve complex coding problems. Identify the role of feedback and iteration in improving agent behavior. Differentiate between static automation and adaptive intelligence in agent design. Recognize how embedded evaluation enables agents to refine performance over time. This loop creates emergent planning behavior. The agent learns from outcomes and tweaks future steps. Even without formal learning, its structure allows for continuous refinement. AlphaCode, developed by DeepMind, tackles coding challenges by generating hundreds of possible solutions, testing them, and filtering based on correctness and efficiency. It doesn't rely on a single answer. It learns by trial and selection. The key here isn't brute force. It's behavioral variation plus evaluation. It models uncertainty and explores multiple reasoning paths until one sticks. Both Baby AGI and AlphaCode succeed because they embed feedback into their process. They don't treat output as the endpoint, they treat it as a signal. If your agent isn't improving over time, it's just automating. But if it's observing, adapting, and iterating, you are building something closer to intelligence. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned how Baby AGI simulates goal pursuit through task decomposition and prioritization. Why AlphaCode uses behavioral variation and trial-based selection for coding tasks. The role of feedback loops in enabling agents to refine and adapt behavior. Why treating output as a signal, not an endpoint, drives intelligent iteration. What makes an agent improve over time versus simply automating steps. When Baby AGI and AlphaCode was explored, what stood out wasn't just their capabilities, it was how they adapt. They didn't need perfect logic upfront. Instead, they used feedback to improve, just like a thoughtful human would. That mindset changed how agents are built, not as static rule followers, but as evolving systems. How might your agent behave differently if it treated every result as a learning signal instead of a final answer? These systems show us what's possible when agents can reflect, revise, and optimize. Up next, it's your turn where you will build your own multi-step agent and evaluate how it behaves under pressure.
2026-07-11T07:55:59.785Z — transcript_dom — BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera — 3009 chars
Imagine an agent that doesn't just act, but learns from each action to refine its future decisions. That's what systems like Baby AGI and AlphaCode demonstrate. Feedback loops that drive intelligent behavior. Baby AGI is an open-source agent that simulates autonomous goal pursuit. You give it a high-level objective, like grow a Twitter following, and it breaks it into tasks, ranks them, and executes them one at a time. After each task, it evaluates the result, reprioritizes, and adjusts the task list. By the end of this video, you will be able to describe how Baby AGI uses task loops and prioritization to simulate autonomous goal pursuit. Explain how AlphaCode leverages trial and selection to solve complex coding problems. Identify the role of feedback and iteration in improving agent behavior. Differentiate between static automation and adaptive intelligence in agent design. Recognize how embedded evaluation enables agents to refine performance over time. This loop creates emergent planning behavior. The agent learns from outcomes and tweaks future steps. Even without formal learning, its structure allows for continuous refinement. AlphaCode, developed by DeepMind, tackles coding challenges by generating hundreds of possible solutions, testing them, and filtering based on correctness and efficiency. It doesn't rely on a single answer. It learns by trial and selection. The key here isn't brute force. It's behavioral variation plus evaluation. It models uncertainty and explores multiple reasoning paths until one sticks. Both Baby AGI and AlphaCode succeed because they embed feedback into their process. They don't treat output as the endpoint, they treat it as a signal. If your agent isn't improving over time, it's just automating. But if it's observing, adapting, and iterating, you are building something closer to intelligence. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned how Baby AGI simulates goal pursuit through task decomposition and prioritization. Why AlphaCode uses behavioral variation and trial-based selection for coding tasks. The role of feedback loops in enabling agents to refine and adapt behavior. Why treating output as a signal, not an endpoint, drives intelligent iteration. What makes an agent improve over time versus simply automating steps. When Baby AGI and AlphaCode was explored, what stood out wasn't just their capabilities, it was how they adapt. They didn't need perfect logic upfront. Instead, they used feedback to improve, just like a thoughtful human would. That mindset changed how agents are built, not as static rule followers, but as evolving systems. How might your agent behave differently if it treated every result as a learning signal instead of a final answer? These systems show us what's possible when agents can reflect, revise, and optimize. Up next, it's your turn where you will build your own multi-step agent and evaluate how it behaves under pressure.
2026-07-11T07:52:41.741Z — reading_dom — BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera — 2400 chars
Module 1 Lesson 1: Explore AI Agents – Concepts, Types, and Foundations Module 2 Lesson 2: Build Intelligent Agents Using Perception, Planning, and Tools Module 3 Lesson 3: Evaluate and Optimize Agent Behavior in Dynamic Environments Cookies Preference Center Cookie List Is Your Agent Really Working? Video . Duration: 6 minutes 6 min Edge Cases, Loops, and Failure Modes in Agent Systems Reading . Duration: 6 minutes 6 min Agent Behavior Breakdown: Debugging and Testing Video . Duration: 4 minutes 4 min HOL: Diagnose and Improve an Agent’s Behavior Using Logs and Examples Practice Assignment . Duration: 10 minutes 10 min BabyAGI, AlphaCode: Improving Agent Performance Over Time Video . Duration: 4 minutes 4 min HOL: Diagnose and Improve an Agent’s Behavior Using Logs and Edge Cases Practice Assignment . Duration: 10 minutes 10 min Pressure Test Your Own Agent Design Dialogue . Duration: 15 minutes 15 min Congratulations and Continuous Learning Journey Video . Duration: 2 minutes 2 min Project: Design and Deploy a Real-World AI Agent Practice Assignment . Duration: 1 hour 1h Assessment Graded Assignment . Duration: 30 minutes 30 min These cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use of user accounts, and offer Site features that are fundamental to the services. These cookies are automatically enabled and cannot be turned off because they are required for the Site to function properly. These cookies allow us to understand how visitors use the Site to enhance the content, quality, and features of the Site and the services. For example, these cookies allow us to recognize and count the number of visitors and understand how visitors move around the Site when using it. These cookies enable the website to provide enhanced functionality and personalization. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly. These cookies may be set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites. They are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising.
2026-07-11T07:47:00.909Z — transcript_dom — BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera — 3009 chars
Imagine an agent that doesn't just act, but learns from each action to refine its future decisions. That's what systems like Baby AGI and AlphaCode demonstrate. Feedback loops that drive intelligent behavior. Baby AGI is an open-source agent that simulates autonomous goal pursuit. You give it a high-level objective, like grow a Twitter following, and it breaks it into tasks, ranks them, and executes them one at a time. After each task, it evaluates the result, reprioritizes, and adjusts the task list. By the end of this video, you will be able to describe how Baby AGI uses task loops and prioritization to simulate autonomous goal pursuit. Explain how AlphaCode leverages trial and selection to solve complex coding problems. Identify the role of feedback and iteration in improving agent behavior. Differentiate between static automation and adaptive intelligence in agent design. Recognize how embedded evaluation enables agents to refine performance over time. This loop creates emergent planning behavior. The agent learns from outcomes and tweaks future steps. Even without formal learning, its structure allows for continuous refinement. AlphaCode, developed by DeepMind, tackles coding challenges by generating hundreds of possible solutions, testing them, and filtering based on correctness and efficiency. It doesn't rely on a single answer. It learns by trial and selection. The key here isn't brute force. It's behavioral variation plus evaluation. It models uncertainty and explores multiple reasoning paths until one sticks. Both Baby AGI and AlphaCode succeed because they embed feedback into their process. They don't treat output as the endpoint, they treat it as a signal. If your agent isn't improving over time, it's just automating. But if it's observing, adapting, and iterating, you are building something closer to intelligence. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned how Baby AGI simulates goal pursuit through task decomposition and prioritization. Why AlphaCode uses behavioral variation and trial-based selection for coding tasks. The role of feedback loops in enabling agents to refine and adapt behavior. Why treating output as a signal, not an endpoint, drives intelligent iteration. What makes an agent improve over time versus simply automating steps. When Baby AGI and AlphaCode was explored, what stood out wasn't just their capabilities, it was how they adapt. They didn't need perfect logic upfront. Instead, they used feedback to improve, just like a thoughtful human would. That mindset changed how agents are built, not as static rule followers, but as evolving systems. How might your agent behave differently if it treated every result as a learning signal instead of a final answer? These systems show us what's possible when agents can reflect, revise, and optimize. Up next, it's your turn where you will build your own multi-step agent and evaluate how it behaves under pressure.
2026-07-11T05:45:37.834Z — reading_dom — Congratulations and Continuous Learning Journey | Coursera — 176 chars
Project: Design and Deploy a Real-World AI Agent What to expect This is a practice assignment to help you check your understanding. It doesn’t count toward your course grade.
2026-07-11T05:43:46.499Z — transcript_dom — Congratulations and Continuous Learning Journey | Coursera — 1816 chars
Congratulations, you've completed Building AI Agents for Complex Tasks. This course wasn't just about crafting clever bots. It was about understanding how to build intelligent systems, agents that perceive, plan, and act in complex real-world environments. Let's take a moment to reflect on what you've accomplished. Agent Architecture Fundamentals You explored the core types of agent design – reactive, deliberative, and hybrid – and learned how they behave under dynamic conditions. Perception and Decision Logic You saw why successful agents need structured decision-making and planning – not just responses, but reasoning chains that adapt in real-time. Hands-on Frameworks and Builds You built and tested real agents using tools like LangChain and Rasa, integrating memory, tools, and multi-step logic into workflows that execute autonomously. Failure Evaluation and Debugging You practiced analyzing agent logs, spotting failure points, and applying fixes that improved reliability, context retention, and goal completion. Designing for Adaptability You learned that building intelligent agents isn't just about getting the right answer. It's about designing systems that improve, recover, and adapt under pressure. These aren't just skills. They are the foundation of modern agent-based systems. You now have the confidence and toolkit to build agents that go beyond scripts and move toward autonomy. So, what's next? Keep building. Create your own agent projects with LangChain or Rasa. Try integrating agents into real apps and pipelines. Explore emerging frameworks and toolchains, and keep learning from the growing agent design community. Thank you for joining me in this course. Stay curious, stay reflective, and keep building agents that don't just respond, but reason, adapt, and make a real impact.
2026-07-11T05:20:35.823Z — reading_dom — Congratulations and Continuous Learning Journey | Coursera — 2400 chars
Module 1 Lesson 1: Explore AI Agents – Concepts, Types, and Foundations Module 2 Lesson 2: Build Intelligent Agents Using Perception, Planning, and Tools Module 3 Lesson 3: Evaluate and Optimize Agent Behavior in Dynamic Environments Cookies Preference Center Cookie List Is Your Agent Really Working? Video . Duration: 6 minutes 6 min Edge Cases, Loops, and Failure Modes in Agent Systems Reading . Duration: 6 minutes 6 min Agent Behavior Breakdown: Debugging and Testing Video . Duration: 4 minutes 4 min HOL: Diagnose and Improve an Agent’s Behavior Using Logs and Examples Practice Assignment . Duration: 10 minutes 10 min BabyAGI, AlphaCode: Improving Agent Performance Over Time Video . Duration: 4 minutes 4 min HOL: Diagnose and Improve an Agent’s Behavior Using Logs and Edge Cases Practice Assignment . Duration: 10 minutes 10 min Pressure Test Your Own Agent Design Dialogue . Duration: 15 minutes 15 min Congratulations and Continuous Learning Journey Video . Duration: 2 minutes 2 min Project: Design and Deploy a Real-World AI Agent Practice Assignment . Duration: 1 hour 1h Assessment Graded Assignment . Duration: 30 minutes 30 min These cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use of user accounts, and offer Site features that are fundamental to the services. These cookies are automatically enabled and cannot be turned off because they are required for the Site to function properly. These cookies allow us to understand how visitors use the Site to enhance the content, quality, and features of the Site and the services. For example, these cookies allow us to recognize and count the number of visitors and understand how visitors move around the Site when using it. These cookies enable the website to provide enhanced functionality and personalization. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly. These cookies may be set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites. They are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising.
2026-07-11T05:17:57.790Z — transcript_dom — Congratulations and Continuous Learning Journey | Coursera — 1816 chars
Congratulations, you've completed Building AI Agents for Complex Tasks. This course wasn't just about crafting clever bots. It was about understanding how to build intelligent systems, agents that perceive, plan, and act in complex real-world environments. Let's take a moment to reflect on what you've accomplished. Agent Architecture Fundamentals You explored the core types of agent design – reactive, deliberative, and hybrid – and learned how they behave under dynamic conditions. Perception and Decision Logic You saw why successful agents need structured decision-making and planning – not just responses, but reasoning chains that adapt in real-time. Hands-on Frameworks and Builds You built and tested real agents using tools like LangChain and Rasa, integrating memory, tools, and multi-step logic into workflows that execute autonomously. Failure Evaluation and Debugging You practiced analyzing agent logs, spotting failure points, and applying fixes that improved reliability, context retention, and goal completion. Designing for Adaptability You learned that building intelligent agents isn't just about getting the right answer. It's about designing systems that improve, recover, and adapt under pressure. These aren't just skills. They are the foundation of modern agent-based systems. You now have the confidence and toolkit to build agents that go beyond scripts and move toward autonomy. So, what's next? Keep building. Create your own agent projects with LangChain or Rasa. Try integrating agents into real apps and pipelines. Explore emerging frameworks and toolchains, and keep learning from the growing agent design community. Thank you for joining me in this course. Stay curious, stay reflective, and keep building agents that don't just respond, but reason, adapt, and make a real impact.
2026-07-11T04:56:39.750Z — reading_dom — Agent Behavior Breakdown: Debugging and Testing | Coursera — 2368 chars
Module 1Lesson 1: Explore AI Agents – Concepts, Types, and Foundations Module 2Lesson 2: Build Intelligent Agents Using Perception, Planning, and Tools Module 3Lesson 3: Evaluate and Optimize Agent Behavior in Dynamic Environments Cookies Preference Center Cookie List Is Your Agent Really Working? Video. Duration: 6 minutes6 min Edge Cases, Loops, and Failure Modes in Agent SystemsReading. Duration: 6 minutes6 min Agent Behavior Breakdown: Debugging and TestingVideo. Duration: 4 minutes4 min HOL: Diagnose and Improve an Agent’s Behavior Using Logs and ExamplesPractice Assignment. Duration: 10 minutes10 min BabyAGI, AlphaCode: Improving Agent Performance Over TimeVideo. Duration: 4 minutes4 min HOL: Diagnose and Improve an Agent’s Behavior Using Logs and Edge CasesPractice Assignment. Duration: 10 minutes10 min Pressure Test Your Own Agent DesignDialogue. Duration: 15 minutes15 min Congratulations and Continuous Learning JourneyVideo. Duration: 2 minutes2 min Project: Design and Deploy a Real-World AI AgentPractice Assignment. Duration: 1 hour1h AssessmentGraded Assignment. Duration: 30 minutes30 min These cookies are necessary for the basic operation of the Site, including to authenticate users, prevent fraudulent use of user accounts, and offer Site features that are fundamental to the services. These cookies are automatically enabled and cannot be turned off because they are required for the Site to function properly. These cookies allow us to understand how visitors use the Site to enhance the content, quality, and features of the Site and the services. For example, these cookies allow us to recognize and count the number of visitors and understand how visitors move around the Site when using it. These cookies enable the website to provide enhanced functionality and personalization. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly. These cookies may be set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites. They are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising.
2026-07-11T04:54:57.497Z — transcript_dom — Agent Behavior Breakdown: Debugging and Testing | Coursera — 3185 chars
When your agent answers the wrong question or repeats a task, it's tempting to fix the output. But to build intelligent systems, you have to debug the behavior, not just the result. Let's look at how to break down what your agent did, why it did it, and how to fix it. In this video, you will learn why debugging agents means analyzing behavior, not just fixing broken outputs. What kinds of bugs emerge from decision loops, memory use and tool logic? Key questions to ask when tracing agent reasoning and response failures. How to design tests that evaluate reasoning chains, fallback handling and memory updates. How to use logs and traces to identify where and why agent behavior breaks down. Unlike traditional software, AI agents rely on decision loops, memory and tool use, which means bugs don't just live in the code, they live in the logic behind tool choice, the breakdown of memory updates, the way an agent handles failed responses. You need to inspect not just what the agent said, but what it believed was true at the time. When debugging an agent, ask yourself, did it perceive the input correctly? Did it select the right tool or intent? Was memory updated properly? Was a fallback triggered? And why? Did the reasoning chain break a loop? Here's a lang chain agent that searched retreat too many documents and got stuck summarizing conflicting info. The issue, a missing guardrail on its planning logic. To catch these issues early, build agent specific tests. You can simulate inputs and check expected reasoning paths, log tool calls and validate response accuracy, monitor token usage and memory changes. Use test cases that mirror edge cases like ambiguous queries or dropped context. You're not just testing answers, you're testing the reasoning. Does the agent behave the way you expect it to under pressure? Debugging AI agents means stepping into their loop, following how they process, decide and act. You're testing not just outcomes, but behavioral logic across time. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned why debugging AI agents requires inspecting behavior, not just outputs. How decision loops, memory and tool use affect agent reliability. Common failure points like broken reasoning chains or outdated context. Key debugging questions to uncover root causes in agent logic. How to use logs, simulations and behavioral tests to improve agent performance. Early in development, agent errors are often treated like simple bugs, issues to be fixed with a patch or prompt tweak. Real breakthroughs happen when debugging shifts to understanding how the agent perceives input, chooses action and handles uncertainty. That's where true intelligence or the lack of it reveals itself. When your agent makes a mistake, do you fix the response or investigate the reasoning behind it? Now that you've seen how agent behavior can break down in real world conditions, it's time to step into the role of the debugger. In this hands on lab, you will analyze real agent logs and interaction traces to identify failure points and apply targeted fixes that improve reliability and performance.
2026-07-11T01:23:07.030Z — transcript_dom — Agent Behavior Breakdown: Debugging and Testing | Coursera — 3185 chars
When your agent answers the wrong question or repeats a task, it's tempting to fix the output. But to build intelligent systems, you have to debug the behavior, not just the result. Let's look at how to break down what your agent did, why it did it, and how to fix it. In this video, you will learn why debugging agents means analyzing behavior, not just fixing broken outputs. What kinds of bugs emerge from decision loops, memory use and tool logic? Key questions to ask when tracing agent reasoning and response failures. How to design tests that evaluate reasoning chains, fallback handling and memory updates. How to use logs and traces to identify where and why agent behavior breaks down. Unlike traditional software, AI agents rely on decision loops, memory and tool use, which means bugs don't just live in the code, they live in the logic behind tool choice, the breakdown of memory updates, the way an agent handles failed responses. You need to inspect not just what the agent said, but what it believed was true at the time. When debugging an agent, ask yourself, did it perceive the input correctly? Did it select the right tool or intent? Was memory updated properly? Was a fallback triggered? And why? Did the reasoning chain break a loop? Here's a lang chain agent that searched retreat too many documents and got stuck summarizing conflicting info. The issue, a missing guardrail on its planning logic. To catch these issues early, build agent specific tests. You can simulate inputs and check expected reasoning paths, log tool calls and validate response accuracy, monitor token usage and memory changes. Use test cases that mirror edge cases like ambiguous queries or dropped context. You're not just testing answers, you're testing the reasoning. Does the agent behave the way you expect it to under pressure? Debugging AI agents means stepping into their loop, following how they process, decide and act. You're testing not just outcomes, but behavioral logic across time. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned why debugging AI agents requires inspecting behavior, not just outputs. How decision loops, memory and tool use affect agent reliability. Common failure points like broken reasoning chains or outdated context. Key debugging questions to uncover root causes in agent logic. How to use logs, simulations and behavioral tests to improve agent performance. Early in development, agent errors are often treated like simple bugs, issues to be fixed with a patch or prompt tweak. Real breakthroughs happen when debugging shifts to understanding how the agent perceives input, chooses action and handles uncertainty. That's where true intelligence or the lack of it reveals itself. When your agent makes a mistake, do you fix the response or investigate the reasoning behind it? Now that you've seen how agent behavior can break down in real world conditions, it's time to step into the role of the debugger. In this hands on lab, you will analyze real agent logs and interaction traces to identify failure points and apply targeted fixes that improve reliability and performance.
2026-07-11T01:12:01.690Z — transcript_dom — Agent Behavior Breakdown: Debugging and Testing | Coursera — 3185 chars
When your agent answers the wrong question or repeats a task, it's tempting to fix the output. But to build intelligent systems, you have to debug the behavior, not just the result. Let's look at how to break down what your agent did, why it did it, and how to fix it. In this video, you will learn why debugging agents means analyzing behavior, not just fixing broken outputs. What kinds of bugs emerge from decision loops, memory use and tool logic? Key questions to ask when tracing agent reasoning and response failures. How to design tests that evaluate reasoning chains, fallback handling and memory updates. How to use logs and traces to identify where and why agent behavior breaks down. Unlike traditional software, AI agents rely on decision loops, memory and tool use, which means bugs don't just live in the code, they live in the logic behind tool choice, the breakdown of memory updates, the way an agent handles failed responses. You need to inspect not just what the agent said, but what it believed was true at the time. When debugging an agent, ask yourself, did it perceive the input correctly? Did it select the right tool or intent? Was memory updated properly? Was a fallback triggered? And why? Did the reasoning chain break a loop? Here's a lang chain agent that searched retreat too many documents and got stuck summarizing conflicting info. The issue, a missing guardrail on its planning logic. To catch these issues early, build agent specific tests. You can simulate inputs and check expected reasoning paths, log tool calls and validate response accuracy, monitor token usage and memory changes. Use test cases that mirror edge cases like ambiguous queries or dropped context. You're not just testing answers, you're testing the reasoning. Does the agent behave the way you expect it to under pressure? Debugging AI agents means stepping into their loop, following how they process, decide and act. You're testing not just outcomes, but behavioral logic across time. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned why debugging AI agents requires inspecting behavior, not just outputs. How decision loops, memory and tool use affect agent reliability. Common failure points like broken reasoning chains or outdated context. Key debugging questions to uncover root causes in agent logic. How to use logs, simulations and behavioral tests to improve agent performance. Early in development, agent errors are often treated like simple bugs, issues to be fixed with a patch or prompt tweak. Real breakthroughs happen when debugging shifts to understanding how the agent perceives input, chooses action and handles uncertainty. That's where true intelligence or the lack of it reveals itself. When your agent makes a mistake, do you fix the response or investigate the reasoning behind it? Now that you've seen how agent behavior can break down in real world conditions, it's time to step into the role of the debugger. In this hands on lab, you will analyze real agent logs and interaction traces to identify failure points and apply targeted fixes that improve reliability and performance.
2026-07-10T01:40:54.164Z — transcript_dom — BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera — 3009 chars
Imagine an agent that doesn't just act, but learns from each action to refine its future decisions. That's what systems like Baby AGI and AlphaCode demonstrate. Feedback loops that drive intelligent behavior. Baby AGI is an open-source agent that simulates autonomous goal pursuit. You give it a high-level objective, like grow a Twitter following, and it breaks it into tasks, ranks them, and executes them one at a time. After each task, it evaluates the result, reprioritizes, and adjusts the task list. By the end of this video, you will be able to describe how Baby AGI uses task loops and prioritization to simulate autonomous goal pursuit. Explain how AlphaCode leverages trial and selection to solve complex coding problems. Identify the role of feedback and iteration in improving agent behavior. Differentiate between static automation and adaptive intelligence in agent design. Recognize how embedded evaluation enables agents to refine performance over time. This loop creates emergent planning behavior. The agent learns from outcomes and tweaks future steps. Even without formal learning, its structure allows for continuous refinement. AlphaCode, developed by DeepMind, tackles coding challenges by generating hundreds of possible solutions, testing them, and filtering based on correctness and efficiency. It doesn't rely on a single answer. It learns by trial and selection. The key here isn't brute force. It's behavioral variation plus evaluation. It models uncertainty and explores multiple reasoning paths until one sticks. Both Baby AGI and AlphaCode succeed because they embed feedback into their process. They don't treat output as the endpoint, they treat it as a signal. If your agent isn't improving over time, it's just automating. But if it's observing, adapting, and iterating, you are building something closer to intelligence. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned how Baby AGI simulates goal pursuit through task decomposition and prioritization. Why AlphaCode uses behavioral variation and trial-based selection for coding tasks. The role of feedback loops in enabling agents to refine and adapt behavior. Why treating output as a signal, not an endpoint, drives intelligent iteration. What makes an agent improve over time versus simply automating steps. When Baby AGI and AlphaCode was explored, what stood out wasn't just their capabilities, it was how they adapt. They didn't need perfect logic upfront. Instead, they used feedback to improve, just like a thoughtful human would. That mindset changed how agents are built, not as static rule followers, but as evolving systems. How might your agent behave differently if it treated every result as a learning signal instead of a final answer? These systems show us what's possible when agents can reflect, revise, and optimize. Up next, it's your turn where you will build your own multi-step agent and evaluate how it behaves under pressure.
2026-07-10T01:32:14.539Z — transcript_dom — Agent Behavior Breakdown: Debugging and Testing | Coursera — 3185 chars
When your agent answers the wrong question or repeats a task, it's tempting to fix the output. But to build intelligent systems, you have to debug the behavior, not just the result. Let's look at how to break down what your agent did, why it did it, and how to fix it. In this video, you will learn why debugging agents means analyzing behavior, not just fixing broken outputs. What kinds of bugs emerge from decision loops, memory use and tool logic? Key questions to ask when tracing agent reasoning and response failures. How to design tests that evaluate reasoning chains, fallback handling and memory updates. How to use logs and traces to identify where and why agent behavior breaks down. Unlike traditional software, AI agents rely on decision loops, memory and tool use, which means bugs don't just live in the code, they live in the logic behind tool choice, the breakdown of memory updates, the way an agent handles failed responses. You need to inspect not just what the agent said, but what it believed was true at the time. When debugging an agent, ask yourself, did it perceive the input correctly? Did it select the right tool or intent? Was memory updated properly? Was a fallback triggered? And why? Did the reasoning chain break a loop? Here's a lang chain agent that searched retreat too many documents and got stuck summarizing conflicting info. The issue, a missing guardrail on its planning logic. To catch these issues early, build agent specific tests. You can simulate inputs and check expected reasoning paths, log tool calls and validate response accuracy, monitor token usage and memory changes. Use test cases that mirror edge cases like ambiguous queries or dropped context. You're not just testing answers, you're testing the reasoning. Does the agent behave the way you expect it to under pressure? Debugging AI agents means stepping into their loop, following how they process, decide and act. You're testing not just outcomes, but behavioral logic across time. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned why debugging AI agents requires inspecting behavior, not just outputs. How decision loops, memory and tool use affect agent reliability. Common failure points like broken reasoning chains or outdated context. Key debugging questions to uncover root causes in agent logic. How to use logs, simulations and behavioral tests to improve agent performance. Early in development, agent errors are often treated like simple bugs, issues to be fixed with a patch or prompt tweak. Real breakthroughs happen when debugging shifts to understanding how the agent perceives input, chooses action and handles uncertainty. That's where true intelligence or the lack of it reveals itself. When your agent makes a mistake, do you fix the response or investigate the reasoning behind it? Now that you've seen how agent behavior can break down in real world conditions, it's time to step into the role of the debugger. In this hands on lab, you will analyze real agent logs and interaction traces to identify failure points and apply targeted fixes that improve reliability and performance.
2026-07-10T01:25:24.864Z — transcript_dom — BabyAGI, AlphaCode: Improving Agent Performance Over Time | Coursera — 3009 chars
Imagine an agent that doesn't just act, but learns from each action to refine its future decisions. That's what systems like Baby AGI and AlphaCode demonstrate. Feedback loops that drive intelligent behavior. Baby AGI is an open-source agent that simulates autonomous goal pursuit. You give it a high-level objective, like grow a Twitter following, and it breaks it into tasks, ranks them, and executes them one at a time. After each task, it evaluates the result, reprioritizes, and adjusts the task list. By the end of this video, you will be able to describe how Baby AGI uses task loops and prioritization to simulate autonomous goal pursuit. Explain how AlphaCode leverages trial and selection to solve complex coding problems. Identify the role of feedback and iteration in improving agent behavior. Differentiate between static automation and adaptive intelligence in agent design. Recognize how embedded evaluation enables agents to refine performance over time. This loop creates emergent planning behavior. The agent learns from outcomes and tweaks future steps. Even without formal learning, its structure allows for continuous refinement. AlphaCode, developed by DeepMind, tackles coding challenges by generating hundreds of possible solutions, testing them, and filtering based on correctness and efficiency. It doesn't rely on a single answer. It learns by trial and selection. The key here isn't brute force. It's behavioral variation plus evaluation. It models uncertainty and explores multiple reasoning paths until one sticks. Both Baby AGI and AlphaCode succeed because they embed feedback into their process. They don't treat output as the endpoint, they treat it as a signal. If your agent isn't improving over time, it's just automating. But if it's observing, adapting, and iterating, you are building something closer to intelligence. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learned how Baby AGI simulates goal pursuit through task decomposition and prioritization. Why AlphaCode uses behavioral variation and trial-based selection for coding tasks. The role of feedback loops in enabling agents to refine and adapt behavior. Why treating output as a signal, not an endpoint, drives intelligent iteration. What makes an agent improve over time versus simply automating steps. When Baby AGI and AlphaCode was explored, what stood out wasn't just their capabilities, it was how they adapt. They didn't need perfect logic upfront. Instead, they used feedback to improve, just like a thoughtful human would. That mindset changed how agents are built, not as static rule followers, but as evolving systems. How might your agent behave differently if it treated every result as a learning signal instead of a final answer? These systems show us what's possible when agents can reflect, revise, and optimize. Up next, it's your turn where you will build your own multi-step agent and evaluate how it behaves under pressure.
2026-07-09T23:03:07.885Z — reading_dom — Programming for Everybody (Getting Started with Python) - Home - Week week | Coursera — 6646 chars
Course Syllabus Participation Strategies Course Schedule & Grading Policy Community Engagement Academic Honesty Course Support Accessibility Global Learning Statement Welcome to Programming for Everybody (Getting Started with Python), taught by Charles Severance! This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python. The course has no prerequisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3. Engaged learning looks different for everybody. In this course, we hope you will define your own measures of success and engage with the material in a way that best suits your needs. We recognize and celebrate the diverse ways learners engage in courses. As you go through this course, we hope you will reflect on your unique skills, needs, and aspirations, and engage in the course material in a way that aligns with your own goals. While the course provides time estimates for completion, you should feel empowered to engage in the material in whatever ways make sense to you. You can see the grading breakdown below for each assignment: Assignment: Find the Largest and Smallest Numbers We expect everyone to be mindful of what they say and its potential impact on others. The goal is to have respectful discussions that do not violate the community space created for these conversations. Here are some productive ways to engage in this course: Participate: This is a community. Read what others have written and share your thoughts. Stay curious: Learn from experts and each other by listening and asking questions, not making assumptions. Keep your passion positive: When replying to a discussion forum post, respond with thoughts on what was said, not about the person who posted. Avoid using all caps, too many exclamation points, or aggressive language. Acknowledge discomfort: The topics discussed in this course might be challenging or hard to talk about. Stick with it and remember the benefits of having these tough conversations that surface from multiple perspectives. We expect all learners to abide by our full Learner Engagement Policy . We will specifically be monitoring this course for language that could be considered inflammatory, incivil, racist, or otherwise unacceptable for this learning space, and we will remove language deemed such. Note regarding “study groups” outside of the platform: While learners are encouraged to interact with one another using communication tools offered within the learning platform, note that any study groups formed outside of this platform, such as over social media or communication applications and websites (e.g., WhatsApp) are not affiliated, endorsed, or moderated by the University of Michigan. If you receive an invitation to an outside study group mentioning the course and claiming to have any official connection to U-M or individual instructors, please exercise caution as this may be an attempted scam. All submitted work should be your own and academic dishonesty is not allowed, such as, but not limited to: Copying words, ideas, or other materials from another source without giving credit to the original author Copying answers or copying from your peers within the course Employing or allowing another person to alter or revise your work, and then submitting the work as your own Using Artificial Intelligence tools, such as ChatGPT, to create or edit your work and submitting that work as your own, unless you were instructed to use AI as part of the assignment Academic dishonesty may result in deleting assignment submissions, forum responses, removal from the course, or other actions as needed. For more information on academic honesty and misconduct, please refer to our Learner Engagement Policy. Please don’t share or reuse solutions to assignments which is an academic integrity concern. Please do not: Share complete assignment code in the course discussion forums Upload completed assignments to public websites with the goal of sharing solutions. (You can share your work and ideas for professional purposes though). Take a peer’s solution and submit it as your own Questions and discussion of course material should take place within the course itself. Please do not contact instructors or teaching assistants off the platform, as responding to individual questions is virtually impossible. We encourage you to direct your questions to the discussion forum, where your question might be answered by a fellow learner or one of our course team members. For technical help, please contact the Coursera Learner Help Center or use the support forums. We are committed to developing accessible learning experiences for the widest possible audience. We recognize that learners with disabilities (including but not limited to visual impairments, hearing impairments, cognitive disabilities, or motor disabilities) might need more specific accessibility-related support to achieve learning goals in this course. Please use the accessibility feedback form to let us know about any accessibility challenges such as urgent issues that keep you from making progress in the course (e.g., missing or inadequate alt-text, captioning errors). Third-party sites and software: While the University of Michigan is not responsible for the accessibility of third-party sites or applications that may be linked from this course, we still encourage you to report associated third-party accessibility issues so that we can ensure you are able to participate. In such cases, we may contact you for additional information as we investigate ways of removing accessibility barriers or to suggest accessible alternatives. We welcome all learners to this course. By enrolling in this course, you join people from all over the world and we value the perspectives they bring to the learning environment. We strive to create a community of mutual respect and trust where people from all backgrounds and views are valued and heard without the threat of bias, harassment, intimidation, or discrimination. We pay attention to your feedback on how different types of learners experience this course and aim to make improvements so the course can best serve everyone. We hope you enjoy learning about topics that are important to you.
2026-07-09T07:40:52.492Z — transcript_dom — Working with Media | Coursera — 9465 chars
Code Interpreter is really useful for working with media. If you have videos, if you have audio files, if you have collections of images, working with all of them, you can do a lot of things really quickly. You can absolutely go into a program and try to create some batch process or try to manually do things in one of these tools, but Code Interpreter is super powerful for going and exploring media and creating new types of idea. So I'm going to give you an example of this. I've taken a video of my son biking. He loves to go and do BMX racing and do dirt jumps and all kinds of interesting things. I've uploaded this video and I'm going to do a simple extraction. I'm going to take 10 frames out of this video, and then I'm going to do some things with them. You can go and play around with media and do all kinds of interesting things. I encourage you, after you do this, take an image, upload it, and try experimenting with doing different operations on the image. I'm just going to go and say extract 10 frames from this video evenly spaced apart. This is something I would probably have to go and look up some command line tool. I know the tool I would use and figure out exactly the commands to do this from the command line. But it's a lot more fun and easier to just do this with Code Interpreter. It goes and generates the code to go and extract the 10 images, and this is super useful. Now I have the 10 images and I can download each of them right here and go and work with them. That in itself is a super useful thing. If I have some image, or I have some movie, or some audio file and I'm going to extract something from it, I want to get a segment of the video. I don't want the whole thing or I want to chop off the start and end, something like that. Now, there's a limit to how much, how big a files you can upload, but you can do pretty sophisticated things and you can get away with a lot. I'm going to say just go and display these 10 images so I can get a sense of what the images look like that it pulled out and it generates this nice graphic displaying the 10 frames that is extracted, and we can look at each one of them and see what they are. Now, what I've decided I want to do is something that you've seen a lot on the Internet. It's a fun little thing to go and do, and that is I want to go and resize these things, modify them some, and turn them into a animated GIF. I'm going to start off by just telling it, now if you imagine you have like 100 images you need to resize, this is super effective. I'm just going to say resize each image, maintain the aspect ratio to 300 pixels wide. If you have a bunch of operations, you want it to apply to an image and you don't know how to describe it and text what you want, you can just go and put that in there. and notice what I'm doing. I'm giving it a pattern to follow on each individual one. I'm saying resize each, and then I'm giving it to constraints what I want. This is going to be a pattern that you're going to see over and over is you're going to tell it what you want to do it and you're going to give it constraints on what you want, and you want it to fill in the details and find a solution that works. Now, if you don't give it enough information, you may not get the final output that you want. In this case, I was very specific, maintain the aspect ratio. I want the ratio between the height and width of the image to stay the same. Even if it's shrinking one dimension, it needs to also shrink the other dimension correspondingly. I'm giving it the constraints that I need, and this something you're going to do over and over is you're going to give it basically whenever you interact with it and ask it to accomplish a task, a lot of times you're going to give it some constraints on what you want, but you're not going to specify everything. Because if you think about a conversation, a more efficient pattern in interacting with conversation is you want to give and just specify the important pieces in the conversation. In your pattern of conversation or interaction, you're going to specify the important constraints that it needs to follow, but not everything. The important information is what you provide, and then you let it infer and figure out the rest. Now sometimes it's not going to be able to infer everything correctly, but you want to think about that. Put in the important pieces, but you don't need to specify everything unless you really do need to specify everything. Now it goes and it resizes each of the individual images. I could go and download this there. But what I'm going to do is I'm going to say, I'm going to do another transformation. I'm going to show you how we can get more complex. I'm going to say convert all of the images to grayscale and increase the contrast 30%. Again, I'm telling it what to do on each image and I'm giving it some constraints on how it's done. This is pretty explicit in this one, and so it then goes and reprocess all of the images. Notice how useful this is. I'm going and applying in natural language and operation to a whole bunch of files. When we've looked at it previously, we've done a lot of work where we look at extracting and pulling out information and reasoning about the information within files. In this case, I'm just working on the files themselves. I'm not actually pulling out per se information that it's reasoning about. I'm just going in and have it apply different operations. This is a pattern of usage where it is acting as like my intern and it's applying tools to a set of files per my instructions. Whereas some of the other tasks that we've seen it doing, it's actually having to go and reason about what's within the files and how to work with that and what that information means. In this case, I'm just having it apply different tools to the files. It's going and processing each frame. Now I'm going to say, combine the images into an animated GIF that flips to the next image at one-second intervals. Very quickly I'm going to go to a completely different product and it goes through, and it generates the image and creates the animated GIF. This is fun. Now we get the result right here. What we see is this nice little animated GIF of the images that have been extracted and the bike riding happening. This is a fun way of going and processing media and getting something new out of it. We can take a video and we can turn it into a series of images. We can do batch processing on these images. We can then take those images, combine them back into a new form of media and animated GIF. But I'm also going to show you one other one. This is going to be really useful probably for a lot of people who are in business, sales, marketing. You can go and create PowerPoint. I'm going to say turn the individual images into a PowerPoint presentation with one image per slide. Now, clue in, you can also go in like generate a bunch of visualizations from an Excel file and then put those visualizations into a PowerPoint or add in text or whatever you wan to do. But clue in, you can create PowerPoint, you can create Excel, all kinds of interesting things. I could also go catalog these images in a CSV. If I wanted to say like, I want to track the source of the movie they came from, where in the movie they came from, I could go and do that. In this case, I've created a PowerPoint presentation. Now if we take a look, I'll open that up and show that to you. Now I've got here's my PowerPoint presentation, and I can go and flip through the various slides that are the same images. This isn't the GIF anymore. This is actually my PowerPoint presentation that I've created with these images. That's a super useful thing we could go and do is turn it into PowerPoint, convert it in different types of media. We could also go catalog it, catalog all of the images in a CSV file with the name of the image, the movie, the name of the movie file that the image was extracted from, the time in the movie that it was extracted from, and the operations applied to it. We could go do that. We could go and catalog all of our operations. If we want to keep this all organized and we want to know where all this came from, we can have that. It's going to go and generate this catalog, and then we'll have that and we could go. If we want to know where all this stuff came from, we want to know how we produced it, we want to be able to reproduce it, we want to be able to find those original images, maybe we want to do bigger versions of them, maybe we want to apply different sets of operations, we could go and do all of that and we have a nice CSV file to organize it. Now we've got our CSV file that's been created and we could go and download that file and begin working with it, keeping it catalog. This is really useful, all the operations you want to see. Going and taking media, doing all kinds of operations to reformat it, to restructure it, to resize it, adjust the colors, adjust the look and feel. We can go and then take it, turn it into different types of media like an animated GIF. We can turn it into a PowerPoint presentation, but then we can also do the organizational aspects. If we're doing this professionally, for example, a lot of times we need to know where are all these assets, where did they come from? How were they created? All of that type of metadata and cataloging can be really important, and we can automate a lot of those processes as well.
2026-07-09T07:37:22.780Z — transcript_dom — Asking Questions in a Small Document | Coursera — 12703 chars
Let's have some fun with code interpreter by learning to work with small documents and what document could be more fun every year to work with than the IRS 1040, that if you're an American citizen, you have to fill out on your taxes? It's possibly the most fun document that I ever worked with in my life. Of course, this is completely not true. It's the document that I absolutely dread dealing with. Usually, I'm trying to avoid it all year long because it's a complicated mess of a document. It's got tons and tons of questions. It's dense, it's hard to read. It's got all these rules. I always feel like I'm lost in what I'm doing. Let's go and do just a simple chat about how we might be able to use code interpreter to help us with the 1040. Now, I'm going to go ahead and state up front, I would not use a code interpreter to do your taxes. It can make mistakes. You would want to go use an accountant, somebody who knows what they are doing. This is not tax advice. This is just an example of how you can take a document that is small enough to fit into code interpreter and read and reason about how you can go and interact with it. Even if that document is really complex. That's the point of this is this is a complex, really dense document, but it happens to fit into essentially a single message. Now I'm not going to go and copy and paste, but let's take a look at this document. I encourage you to go and find some document of yours that is preferably in plain text, but it could be PDF and there are some tricks on that. But get started and go play with a document and start asking questions and discovering the limits and the things that does really well. I'm going to start off by doing a basic pattern that you're going to see over and over when I'm working with PDFs or other documents. I'm going to say extract this document into plain text and then read the document and tell me all of the pieces of information to the question. But the key pattern that I'm doing as I'm saying, extract this document to plain text and then read the document. If I got a document that sits in and can be fed into one single chat message. This is how I'm going to approach this. I'm just going to say extract it to plain text to read it. Sometimes I'll just say read it, but extract and then read tends to work better if you're working with PDFs because sometimes code interpreter will come back and tell you, hey, I can't read PDFs. But if you tell it, extract it to plain text and then read the plain text, that works on a lot of file formats. It's a pretty effective way of doing no, you don't always want to extract it that way. Sometimes there are other ways of doing it. But for now know this is a simple, effective way of handling most documents, as you say, plain text and read the document. That's going to trigger code interpreter basically going and looking at what's inside the document. Now, here's what it's done. It's gone and looked through and inspected the documents. That's filing status, and personal information. These are all things that are in the document. These are questions. This is dense document filled with different questions and we're seeing all of that basically brought up here. Now, I'm going to tell it to do something that is another pattern that I often do is I will tell it to reread the document, and make it change how it outputs. What I'm going to do is I'm gonna look at what information it has captured in the first reading. I'm going to think about what information it got that I'm happy within what I want. I'm going to think about what information it missed. Because one of the things to note is information that's brought into the conversation. We see all this information brought up right here. This is information that it's easy for it to reason on because it's visible. We both see it. We can both look at it. We can both fact check it. We can both identify potential errors. But it's also important because once it's in the conversation, then the GPT 4 model can have access to it easily to go and reason about it and do things about it. If you want to do textual analysis, it's much easier if you can get the pieces of texts that you want to work with that unstructured data. It's going to be much easier if you can get it into the conversation like this and then have GPT 4 directly interact with it. I've seen some things that I didn't see in the deck. The information that provided that I want brought out. Specifically the original document as identifiers for question. Now, not all of them have identifiers. It would be really nice if this thing had identifiers for everything, but it does not. Some of the questions are numbered with identifiers and some of them are not. But I want to capture that information that maps questions to identifiers. I'm going to do another common pattern. I'm going to tell it to reread the document and I'm going to tell it to go and get that information that I would like to have in the conversation. It's easier to reason about. I say reread the text and break down the questions into the smallest level of deeper though, I don't want summaries. Provide the identifier for each question in the document or question name if none exists. Basically what I'm telling you it is go into the document, pull out all the questions. I don't want you to summarize. I want every individual question and I want the original identifiers. If they were 1a or 1b or 1c in the document, I want that listed. If they had no identifier, just come up with like make this up, put it in brackets. Now what we see is it's gone and it's extracted all of this information from the document. It's followed my instructions. It's for the ones where there's no actual identifier it just summarized it in brackets. Then when we get down farther, what we see is now it's starting to put in the numbers. We see 1a,1b,1c,1d, and it's extracting each individual question from the text. It's summarized it. Now I've pulled an additional information which is the identifiers. I've done that for the whole document. Now, here's another pattern that you're going to see. Over and over and over we're going to hit the limit on the amount of information that code interpreter will give us back at once or the amount of work it's willing to do in response to a single message we send it. This is something you're going to always see. You're always going to work around. Whenever code interpreter stops, before it is done with your task, you want to tell it to continue or proceed. This is a pattern that you're going to want to know. Code interpreter will start working on something, and then it's going to hit the limit of the amount of information it can give you back and you need to tell it to continue or proceed. Now we see that right here. We've gotten to the end of this line right here, refund, and now I'm telling it continue. Again, really, really important you're going to use this all the time. It says sure, here's the continuation, and then it continues going through and getting the rest of the questions. Now let's have fun with the document. What could be more fun than asking questions about the 1040, the classic US tax document? Well, obviously, it's the least fun thing I do, but I'm going to answer questions about it because it's a complex document, so it's interesting to analyze. Which boxes if checked would require filling out additional forms? This is really interesting it now tells us which of those questions that are check-boxes if I checked it I would have to go and fill out an additional forum. I look at this as like which boxes if I check am I not going to have additional fun going and filling out? I'm going to check this box and that expands this whole new document for me to go and fill out a whole new realm of fun then I will get to go and enjoy. If I sold digital assets like Bitcoin, it's telling me now I'm going to have to go and have some additional fun. Then I know that there's this thing called a Schedule C and I'm surprised that it didn't mention the Schedule C, and so I say, hey, what about the schedule C? Like, didn't you make a mistake? That's really what I'm applying in a nice way, like code interpreter, didn't you forget about the schedule C, I think you've made a mistake. It's interesting because it actually points out that no, I'm wrong because it says, there isn't a specific checkbox on the Form 1040. It's actually pointed out to me, I know what the schedule C is and you're wrong because there's no checkbox there that triggers filling it out. You fill it out because of other reasons. That's really interesting. Now I say, okay, I had read some of the questions and I said, well, if I was born in 1946 and a veteran, which boxes might apply to me? Then it goes and tells me some of the boxes that might apply to me. These are really interesting things that you can do, but here the key, the reason why this was an easy document to work with. Now, it may not be an easy document for me to fill in, it may not be an easy document for me to go and collect all the information that I need to work with, it may not be an easy document to read or understand, but it's an easy document to work with in code interpreter because although it's dense in information, that information can be cut and pasted potentially into a single message. Now, I don't have to cut and paste it because I can ask code interpreter to extract it as plain text and read it for me and I can have it go and extract all of the individual questions for me, but it's a document that's easier. Now, I encourage you go out, this is so much fun and fascinating. Go take documents that you have and start putting them into code interpreter and trying this out. Start with extract this document to plain text and read the document or start with read the document. Then tell it what information you want to pull into the conversation. Here I'm saying tell me all the pieces of information that this document is requesting. Tell it to read or extract and read and then tell it what information you want it to bring into the conversation as it reads, this is for small documents, will have different approaches for larger documents. After it reads it, tell it to reread it and have it change the information that's bringing into the conversation so make sure it's pulling in the information in the conversation you want. In this case, I saw it read and pulling the information about the questions, but I realized I wanted the questions at a greater like I wanted them in finer granularity so I told it to re-read and break the questions down in the smallest level of granularity and I told it to provide the identifier for each question. This is a common pattern, re-extract and read the document and pull in information like tell me. Tell me is the code for pull it into the conversation, and then when we look at it, we're going to say re-read and here's what you're going to pull in or here's how you're going to change, how you're going to tell it to me. That's what I'm doing here. Once we have the information in the conversation like we do here, if we're doing something at a fine level of granularity, we're probably going to hit the limit on the amount of text that it will output at one. The amount of work it will do it once, and we're going to need to tell it to continue or proceed, another common pattern that we're going to have in our conversations. We're going to have to tell it, continue or proceed in order to get it finish pulling in the rest of the information in particular when really granular, and then we began hence asking questions. Step 1 is extract and read or read. Then we say, here's what I want you to tell me, and when you're reading or here's what I want you to summarize for me, that's another one or here's what I want you to outline for me. Those are examples of things you can do. Extract and read, tell me this and tell me could also be summarize this or outline this, and the
2026-07-09T07:29:35.093Z — transcript_dom — Getting Information Into the Conversation | Coursera — 13735 chars
One of the most important things that you can do with code interpreter to be effective, particularly if you want to be effective in getting it to reason well. Or to write well or whatever it is is you want it to make sure that it has easy access to the knowledge that you're going to need it to reason on or use as the basis of its writing, or to do whatever it is to filter, to transform whatever it is. Now, the easier it is for it to access that information, the more successful you're going to be in using code interpreter. Now, what does it mean for it to be easy to access? If you can see the information here in the conversation, you are very likely going to be successful in whatever knowledge based task you're trying to have GPT Four do through code interpreter. So let me just repeat this, if you can see the information that it's going to build off of in the conversation, you're much more likely to be successful. This is the simplest, easiest way to try to make sure that the reasoning is sound. Now, in this example, with the 1040 that I did a minute ago, I had it go and extract a bunch of information and then I had it reread and extract additional information. And part of the reason for this was I didn't see the Identifiers for the questions in the conversation. And I thought that was going to be something that I wanted based on the idea that I was going to go and talk about different questions or ask about what checkboxes. Well, if I'm going to refer to checkboxes, I'm probably going to need to know the Identifiers form so I can go and reference back into the original form. Now, you don't have to do this, but it's often the easiest way to reason about what code interpreter is doing and getting it to do the right thing is to get it to read the information into the conversation. Now, there's other tricks that are used in the background to make this happen. Now, there's two places that the reading and rereading can happen. Now, I want to show you something interesting though, because I think this is really helpful to also show you why reading and rereading is powerful. So in the first time that I said read this, we see it pop up this box that's going and getting the information from Python, right? It's using Python to read the PDF and extract the text and then it shows the result here. So one thing that's helpful to note here is it's actually brought everything into the conversation. Now, this is sort of a tricky thing to know, but the fact that it's read it and then it's put it in here in this result, that means it's available and in the conversation, it's possible to have access to it. Now, it may not have access to it all at once and we may need to go and refresh it, but it's all in the conversation. Now, note what it didn't do here, when I said reread, it did not go and run Python again. Now, there's two things that that could mean, and it's helpful to know this. One is the information that it needs to do this task is already present in the conversation, and so it doesn't have to write any new Python. It just goes and summarizes or reshapes or reformats information that's already been brought into the conversation. Or it is possibly hallucinating or relying on data that it was trained on, which may not be what you want. So you want to be very careful, and this is why it's helpful to think through knowing what is in the conversation and what is not. What is clearly in the context of the conversation, the easiest, simplest way to know what's there is to make sure it gets read and is visible in some way. So when you ask it to extract it, summarize it or outline it and put it directly in here and you can see the text written out, that's just a very helpful check. And if you go, let's say, for example, if you do something and you don't see it, write Python code and you just see it refactor or reformat or restructure or summarize down here, that could mean one of two things. Either that information was already present, or it's making up the information based on, now, it doesn't have to be false, it just means that it isn't getting it directly from your document. So it's either getting it out of the conversation, or it's potentially based on something it was trained on. Now, the Form 1040 is something that is so common, it could have been trained on a prior year's 1040. Now, in this case, it turns out that I've uploaded the Form 1040 from 2022. Now, it's possible that this version of the model was updated and based on 2022's Form 1040, I don't think so. And I think what it has done is actually brought the whole form into the context of the conversation. Although we can't see it at the beginning, I think it is already there and available through the result right here. So that's really important to know is, is the information there and readily present? Now, in this case, this is a great case where I just want to emphasize the importance of paying attention to what is clearly visible in the conversation, which in this case is all of this here. What is it doing in Python? Well, we see at the beginning, it's definitely reading the document in Python, so we know it went to the source document originally. Now, I'm getting it to reread and bring in additional information, and it's not using Python. So either that information was already in the conversation in some way, because it read the whole document and has access to it, or it's pulling it out of its own training data or it's hallucinating in some way. And so when we begin bringing information into the conversation and working with it, when we begin asking to reread or summarize our things, we can start getting a sense of what is it really going out to the document and getting versus what's in the conversation, versus what is it pulling out of what it was trained on. Now, figuring out what is coming from specifically from the conversation, the context that we see here versus what it was trained on is tricky and there's no single straightforward way to do this. There's multiple approaches that we can use to try to solve this problem, but at different points it can be a little bit difficult. The best way that we know that it's going to our document is that we see the gray boxes and that we see it's doing something, but we don't always want to go into it our gray document. Because if we're having a conversation and we see that there's a direct mapping between what's above and what's below based on our task, then that's really what we want is to get things into the conversation, particularly for reasoning tasks or writing tasks or summarization tasks. We want to get it into the conversation summarized in some form so we can start working with it. So if you are in a situation where you're working with some document and you feel like you're struggling, that it just can't reason effectively, try to think about how do I get that information into the conversation so that I can see it here. One, it's going to help you get a better sense of what's going on, but two, if it's in the conversation, it's much easier for it to reason on. And three, it'll also help you sort out is it picking up something from somewhere else? Now, getting the information in the conversation doesn't have to just mean that we are going to just literally cut and paste into the conversation. There's all kinds of different ways that we can get information that it needs for reasoning into the conversation. And part of the reason I say this, if you go and look at what I did here, this is a lot of information, it's a lot of text and it actually took a while for it to output it all and we may not need it all. So what we want to do is we want to get the information that we need for reasoning into the conversation and we want to be really careful that we are bringing in just what we need because there's a limited amount of text that can go in. I talk about using a small document and part of the reason we're using a small document is because we can get it all into one prompt and that's easier. When we get to bigger documents, we're going to have to be selective, really selective about what goes into the conversation and how we represent it in order to be effective. But the key is we want to get it the information it needs in order to reason and do the right thing. Now, it could be a summary, it could be an outline, it could be literally go and excerpt and read and paste verbatim into this conversation, essentially the text. There's all types of things that we could do, but one thing that we can do is we can also get a map and we'll talk about this more later, but we can create a map of where information is within the document and put that into the conversation. We can put all kinds of other artifacts or things into the conversation that help it go and achieve its job. So in this example, I went and had it put an index of where different types of policies are covered in a document. Now, if you think about this, if I gave you a document and I said, go find this policy and that document has an index and you can go in and look up the correct section to read, that saves you a vast amount of time, right? You have a very dense little section that gives you exactly the information you need in order to reason correctly about where to go to get the next piece of information. So you could go and look up, I need to go to page two, you then flip to page two and you can read just page two, which gives you less information. Well, that exact process helps code interpreter or helps AI, anytime we can give it a map of where to go find the information. It can read the mapping, which is typically much smaller than the larger volume of information, and then it can jump into the location that it needs to get the actual piece of information to load into the conversation to do the work. Now, if we look at this, we've built an index here and then the next thing that we do is we say read the policies related to staying in an AirBnB. And notice what it does, I'm going to show this, this is code here, but hopefully you can get a sense of what this code is doing, is it jumps because it has the index in separate pages for each piece of information. It jumps directly to the pages it needs to read. And so it says page 4 text and page 7 text. And if you're not a Python programmer or this doesn't look familiar, just notice that it's saying page 4 and page 7 for AirBnB. And if we look at this, we got page 7, which is Mileage, and we've got page 4, which is International Travel, which actually I'm incorrect here. So page 4 and 7 all relate to lodging, sorry about that. And that just shows to show you the AI sometimes gets it better than the human, I make mistakes, it make mistakes, no judgment. So lodging is covered on pages 4 and 7, we're talking about AirBnB, AirBnB which is a form of lodging. So therefore on page 4 it goes and reads it, and page 7 it goes and reads it. So the AI has done the right thing, it's used the information that we brought into the context. This document was too big to fit into a single message, so we basically brought in information into the conversation that it needed to reason effectively. And then it can use that as a jumping off point to go and find the next piece of information and bring it into the conversation. Because you see right here the result is it now brought in those pages into the conversation in the result, and then it goes and reasons about what's inside of those pages. So when you're working with code interpreter, when you're that if you can see the information here that it's going to need to reason, you are much better off. If you can't see the information that it needs to reason here in some form, then there's a good chance that code interpreter is going to struggle or that code interpreter may hallucinate and you not realize it. So when you are working with code interpreter and you want it to reason about knowledge in text, try to get some form of that text into
2026-07-09T06:55:08.731Z — transcript_dom — Zip Files for Automation | Coursera — 10018 chars
This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or computer scientist. But you can use it in conjunction with somebody else who is really good at those things if you aren't a programmer or computer scientist. If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. Now what is this technique? It's a very exciting one from the perspective of software development. One of the challenges that we have is we have all these little tools that we would like to have to help us out. All day long when I'm working and thinking it'd be really nice to have a piece of software that did this and simplified this process for me. Sometimes as a software engineer, I'll go and take the time to actually write the software to do that. The Code Interpreter creates an intriguing new possibility where we can actually turn a conversation with Code Interpreter into software. Now, if you are not a programmer, if you can't read the code, you should not go all the way through with this because you have to be able to look at the code and know if it's going to work correctly, if it's safe to run on your computer. It could delete all the files for all you know, if you can't read the code. We 100% know that large language models can make mistakes, and you need to pay attention to the code that comes out of what I'm going to show you, and if you can't read it and understand it, you should not proceed. You will need to go find a programmer to help the rest of the way. But a programmer at some point, a human software developer, needs to be involved in the analysis for what I'm going to show you. However, it's really exciting. Let's take a look at what we've got. This is an original conversation that I had where I took a movie and I extracted 10 different frames from the movie, I then went and display the images. I looked at it, I'm having this whole conversation, I resize the images and I did a bunch of other things. I turned each image into grayscale, I increase the contrast by 30%, and I turned the images into an animated GIF. I also created a PowerPoint presentation with one image per slide. Now I thought, wouldn't it be great if I could take this whole process so that I can repeat it? Now, I've repeated exactly this type of process for creating tools to help me with the creation of these videos and cataloging them. For example, automatically going and taking my videos and measuring how long they are, creating CSV files for them, doing other interesting things that then help me along. But any conversation that you go and have with Code Interpreter, you can turn into a piece of software in most cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present in the conversation. Then the last step in this thing, I created the PowerPoint and then I also created a CSV to catalog all this stuff. Here's what I'm going to do. This is the real magic now. Again, if you are not a programmer you need to find a programmer if you're going to take this approach. If you're a programmer, you're going to be able to rapidly accelerate the pace that you can create your own personal tools, because any conversation you have with Code Interpreter that yields a good result, you'll be able to kickstart a piece of software for yourself. Here's what I'm going to say. Turn this process into a Python program that I can download and run on my computer and provide the paths to the documents as command line argument, zip up the program for me to download. Now note you could have also gone and said create some GUI application or something else, anything that it has the tools to create, but I'm just going really simple, I'm just saying turn it into a Python program. You can also tell it to replace calls to GPT-4 with calls to OpenAI's API for GPT-4, the completion API. But I'm not going to do that here because I don't need it. What does it do? It says, sure, I can help with that. It's going to create a script that extracts all the frames from the video, resizes the images, converts the images to grayscale, creates a PowerPoint presentation of the images, catalogs them in a CSV file, and makes an animated GIF from the images. It then gives us the command that we're going to need to install all the Python packages for this piece of software, and then finally, it gives us the zip file, which I have actually gone and downloaded. I'm going to show you what this thing looks like now. Here is the application that it has built for me. I'm going to now run it on a new movie that I did not do before, and this thing is going to run for a second. It takes a few seconds to do all of the work that's in here. Then we should be able to see that it's produced a number of different outputs. It created the extracted frames, so if we go and looked at that, we have all the extracted frames from the video, we can go, and we see also that we have this frames presentation, which is all of the individual frames put into a PowerPoint presentation. We have the resized frames, we have the catalog image in CSV. Now, note something. I said it's really important to have a programmer look at this, and the reason is, is because it's not perfect. One of the things that it was supposed to do was to create an animated GIF, and it didn't do that. Now, as a developer, I can easily go and look at the program before I run it, which I did, and I saw that there was nothing problematic in it, I read the code. Then two, I could go and then say, it doesn't have that animated GIF or whatever it is and I could modify it and work with it to get it there. Now the key thing about this is it turned the conversation into a piece of software, and that flow of that conversation dictated the requirements for the software, and the user was interactively developing it. Now if we go back and look at the conversation, as I'm actually going through and running through it, I'm actually going to see incrementally building what I want the conversation to look like. I'm essentially doing the process of trying and building it incrementally and testing it out. Here I'm seeing an intermediate output, I'm then getting to look at the intermediate resized images and check that it's doing what I want. The key thing is it's producing Python code along the way, to do all this stuff. When it gets to the end down here, and I'm telling it to go and create the final Python program, really all it has to do is stitch together all the code that it's already created and modularize it a little bit. Add places where all these paths and things can be taken into the program as command line parameters rather than starting from scratch. Now, if you're not a programmer, how do you use this capability? One, you could kick-start a conversation. You can download it then, and then you could take it to a programmer. You could go hire a freelance developer or some development shop, to then review that, modify it if it doesn't work exactly like you want. But think of how much farther down the path you are. You can show them, here's my requirements, here's the conversation I had where it did exactly what I wanted, here's the initial Python program that it produced for me. Now, go and check that it's going to line up with what I just did. Here's what I want it to do, and here's what it produced. Do a quick audit of it, read through the code, test it inside a container, make sure it looks safe and reasonable, run it on some test cases for me and debug it and run it on my original test case, and then if it looks good, give it back to me. It's a different type of style of software development. It's going to create a new paradigm that's really exciting for creating these smaller tools. We can probably do that much less expensively than when we start from scratch and when we're trying to collect all the requirements, and we're trying to get everybody on the same page about what it's supposed to do and how it's supposed to work. Now we're actually having the end-user build up the process and the flow of what they wanted to do and interact with it through Code Interpreter and generate the initial starting point for the software before taking it to the programmer. The conversation itself becomes a set of requirements, and the software becomes the initial starting point for the developer, so hopefully they don't have to do a whole lot to get it into a final form of a usable tool. Now, if you're a software developer like me, this is awesome because you can take the conversation, you can output Python and you're way ahead of the game. Now in this case, I would have to go and work with a little bit to fix that part that it wasn't giving me the animated GIF I wanted and look for it and make sure there's no other bugs. I'd test it, do all the normal things I would do if I got a piece of software or if I had somebody else write a piece of software for me. But it's a really exciting capability that I think is worth talking about. Now again, I want to warn you, if you're not a programmer, you should not download these things and run them blindly. If you are a programmer, you should not blindly trust the software that comes out of it. You should download it, you should read all the code first, make sure you're comfortable with what it's doing, and you should test it in some safe environment like inside of a container or something else if you're at all concerned about what it's going to be doing. Now, in most cases, these things are going to be fairly straightforward, smaller bits of software, fairly easy to audit. I've created a lot of great tools for myself using this process. It's something really exciting that I think is a new style of software development that's going to be enabled because of Code Interpreter.
2026-07-09T06:55:03.497Z — transcript_dom — Zip Files for Automation | Coursera — 10018 chars
This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or computer scientist. But you can use it in conjunction with somebody else who is really good at those things if you aren't a programmer or computer scientist. If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. Now what is this technique? It's a very exciting one from the perspective of software development. One of the challenges that we have is we have all these little tools that we would like to have to help us out. All day long when I'm working and thinking it'd be really nice to have a piece of software that did this and simplified this process for me. Sometimes as a software engineer, I'll go and take the time to actually write the software to do that. The Code Interpreter creates an intriguing new possibility where we can actually turn a conversation with Code Interpreter into software. Now, if you are not a programmer, if you can't read the code, you should not go all the way through with this because you have to be able to look at the code and know if it's going to work correctly, if it's safe to run on your computer. It could delete all the files for all you know, if you can't read the code. We 100% know that large language models can make mistakes, and you need to pay attention to the code that comes out of what I'm going to show you, and if you can't read it and understand it, you should not proceed. You will need to go find a programmer to help the rest of the way. But a programmer at some point, a human software developer, needs to be involved in the analysis for what I'm going to show you. However, it's really exciting. Let's take a look at what we've got. This is an original conversation that I had where I took a movie and I extracted 10 different frames from the movie, I then went and display the images. I looked at it, I'm having this whole conversation, I resize the images and I did a bunch of other things. I turned each image into grayscale, I increase the contrast by 30%, and I turned the images into an animated GIF. I also created a PowerPoint presentation with one image per slide. Now I thought, wouldn't it be great if I could take this whole process so that I can repeat it? Now, I've repeated exactly this type of process for creating tools to help me with the creation of these videos and cataloging them. For example, automatically going and taking my videos and measuring how long they are, creating CSV files for them, doing other interesting things that then help me along. But any conversation that you go and have with Code Interpreter, you can turn into a piece of software in most cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present in the conversation. Then the last step in this thing, I created the PowerPoint and then I also created a CSV to catalog all this stuff. Here's what I'm going to do. This is the real magic now. Again, if you are not a programmer you need to find a programmer if you're going to take this approach. If you're a programmer, you're going to be able to rapidly accelerate the pace that you can create your own personal tools, because any conversation you have with Code Interpreter that yields a good result, you'll be able to kickstart a piece of software for yourself. Here's what I'm going to say. Turn this process into a Python program that I can download and run on my computer and provide the paths to the documents as command line argument, zip up the program for me to download. Now note you could have also gone and said create some GUI application or something else, anything that it has the tools to create, but I'm just going really simple, I'm just saying turn it into a Python program. You can also tell it to replace calls to GPT-4 with calls to OpenAI's API for GPT-4, the completion API. But I'm not going to do that here because I don't need it. What does it do? It says, sure, I can help with that. It's going to create a script that extracts all the frames from the video, resizes the images, converts the images to grayscale, creates a PowerPoint presentation of the images, catalogs them in a CSV file, and makes an animated GIF from the images. It then gives us the command that we're going to need to install all the Python packages for this piece of software, and then finally, it gives us the zip file, which I have actually gone and downloaded. I'm going to show you what this thing looks like now. Here is the application that it has built for me. I'm going to now run it on a new movie that I did not do before, and this thing is going to run for a second. It takes a few seconds to do all of the work that's in here. Then we should be able to see that it's produced a number of different outputs. It created the extracted frames, so if we go and looked at that, we have all the extracted frames from the video, we can go, and we see also that we have this frames presentation, which is all of the individual frames put into a PowerPoint presentation. We have the resized frames, we have the catalog image in CSV. Now, note something. I said it's really important to have a programmer look at this, and the reason is, is because it's not perfect. One of the things that it was supposed to do was to create an animated GIF, and it didn't do that. Now, as a developer, I can easily go and look at the program before I run it, which I did, and I saw that there was nothing problematic in it, I read the code. Then two, I could go and then say, it doesn't have that animated GIF or whatever it is and I could modify it and work with it to get it there. Now the key thing about this is it turned the conversation into a piece of software, and that flow of that conversation dictated the requirements for the software, and the user was interactively developing it. Now if we go back and look at the conversation, as I'm actually going through and running through it, I'm actually going to see incrementally building what I want the conversation to look like. I'm essentially doing the process of trying and building it incrementally and testing it out. Here I'm seeing an intermediate output, I'm then getting to look at the intermediate resized images and check that it's doing what I want. The key thing is it's producing Python code along the way, to do all this stuff. When it gets to the end down here, and I'm telling it to go and create the final Python program, really all it has to do is stitch together all the code that it's already created and modularize it a little bit. Add places where all these paths and things can be taken into the program as command line parameters rather than starting from scratch. Now, if you're not a programmer, how do you use this capability? One, you could kick-start a conversation. You can download it then, and then you could take it to a programmer. You could go hire a freelance developer or some development shop, to then review that, modify it if it doesn't work exactly like you want. But think of how much farther down the path you are. You can show them, here's my requirements, here's the conversation I had where it did exactly what I wanted, here's the initial Python program that it produced for me. Now, go and check that it's going to line up with what I just did. Here's what I want it to do, and here's what it produced. Do a quick audit of it, read through the code, test it inside a container, make sure it looks safe and reasonable, run it on some test cases for me and debug it and run it on my original test case, and then if it looks good, give it back to me. It's a different type of style of software development. It's going to create a new paradigm that's really exciting for creating these smaller tools. We can probably do that much less expensively than when we start from scratch and when we're trying to collect all the requirements, and we're trying to get everybody on the same page about what it's supposed to do and how it's supposed to work. Now we're actually having the end-user build up the process and the flow of what they wanted to do and interact with it through Code Interpreter and generate the initial starting point for the software before taking it to the programmer. The conversation itself becomes a set of requirements, and the software becomes the initial starting point for the developer, so hopefully they don't have to do a whole lot to get it into a final form of a usable tool. Now, if you're a software developer like me, this is awesome because you can take the conversation, you can output Python and you're way ahead of the game. Now in this case, I would have to go and work with a little bit to fix that part that it wasn't giving me the animated GIF I wanted and look for it and make sure there's no other bugs. I'd test it, do all the normal things I would do if I got a piece of software or if I had somebody else write a piece of software for me. But it's a really exciting capability that I think is worth talking about. Now again, I want to warn you, if you're not a programmer, you should not download these things and run them blindly. If you are a programmer, you should not blindly trust the software that comes out of it. You should download it, you should read all the code first, make sure you're comfortable with what it's doing, and you should test it in some safe environment like inside of a container or something else if you're at all concerned about what it's going to be doing. Now, in most cases, these things are going to be fairly straightforward, smaller bits of software, fairly easy to audit. I've created a lot of great tools for myself using this process. It's something really exciting that I think is a new style of software development that's going to be enabled because of Code Interpreter.
2026-07-09T06:48:39.540Z — transcript_dom — Zip Files for Automation | Coursera — 10018 chars
This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or computer scientist. But you can use it in conjunction with somebody else who is really good at those things if you aren't a programmer or computer scientist. If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. Now what is this technique? It's a very exciting one from the perspective of software development. One of the challenges that we have is we have all these little tools that we would like to have to help us out. All day long when I'm working and thinking it'd be really nice to have a piece of software that did this and simplified this process for me. Sometimes as a software engineer, I'll go and take the time to actually write the software to do that. The Code Interpreter creates an intriguing new possibility where we can actually turn a conversation with Code Interpreter into software. Now, if you are not a programmer, if you can't read the code, you should not go all the way through with this because you have to be able to look at the code and know if it's going to work correctly, if it's safe to run on your computer. It could delete all the files for all you know, if you can't read the code. We 100% know that large language models can make mistakes, and you need to pay attention to the code that comes out of what I'm going to show you, and if you can't read it and understand it, you should not proceed. You will need to go find a programmer to help the rest of the way. But a programmer at some point, a human software developer, needs to be involved in the analysis for what I'm going to show you. However, it's really exciting. Let's take a look at what we've got. This is an original conversation that I had where I took a movie and I extracted 10 different frames from the movie, I then went and display the images. I looked at it, I'm having this whole conversation, I resize the images and I did a bunch of other things. I turned each image into grayscale, I increase the contrast by 30%, and I turned the images into an animated GIF. I also created a PowerPoint presentation with one image per slide. Now I thought, wouldn't it be great if I could take this whole process so that I can repeat it? Now, I've repeated exactly this type of process for creating tools to help me with the creation of these videos and cataloging them. For example, automatically going and taking my videos and measuring how long they are, creating CSV files for them, doing other interesting things that then help me along. But any conversation that you go and have with Code Interpreter, you can turn into a piece of software in most cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present in the conversation. Then the last step in this thing, I created the PowerPoint and then I also created a CSV to catalog all this stuff. Here's what I'm going to do. This is the real magic now. Again, if you are not a programmer you need to find a programmer if you're going to take this approach. If you're a programmer, you're going to be able to rapidly accelerate the pace that you can create your own personal tools, because any conversation you have with Code Interpreter that yields a good result, you'll be able to kickstart a piece of software for yourself. Here's what I'm going to say. Turn this process into a Python program that I can download and run on my computer and provide the paths to the documents as command line argument, zip up the program for me to download. Now note you could have also gone and said create some GUI application or something else, anything that it has the tools to create, but I'm just going really simple, I'm just saying turn it into a Python program. You can also tell it to replace calls to GPT-4 with calls to OpenAI's API for GPT-4, the completion API. But I'm not going to do that here because I don't need it. What does it do? It says, sure, I can help with that. It's going to create a script that extracts all the frames from the video, resizes the images, converts the images to grayscale, creates a PowerPoint presentation of the images, catalogs them in a CSV file, and makes an animated GIF from the images. It then gives us the command that we're going to need to install all the Python packages for this piece of software, and then finally, it gives us the zip file, which I have actually gone and downloaded. I'm going to show you what this thing looks like now. Here is the application that it has built for me. I'm going to now run it on a new movie that I did not do before, and this thing is going to run for a second. It takes a few seconds to do all of the work that's in here. Then we should be able to see that it's produced a number of different outputs. It created the extracted frames, so if we go and looked at that, we have all the extracted frames from the video, we can go, and we see also that we have this frames presentation, which is all of the individual frames put into a PowerPoint presentation. We have the resized frames, we have the catalog image in CSV. Now, note something. I said it's really important to have a programmer look at this, and the reason is, is because it's not perfect. One of the things that it was supposed to do was to create an animated GIF, and it didn't do that. Now, as a developer, I can easily go and look at the program before I run it, which I did, and I saw that there was nothing problematic in it, I read the code. Then two, I could go and then say, it doesn't have that animated GIF or whatever it is and I could modify it and work with it to get it there. Now the key thing about this is it turned the conversation into a piece of software, and that flow of that conversation dictated the requirements for the software, and the user was interactively developing it. Now if we go back and look at the conversation, as I'm actually going through and running through it, I'm actually going to see incrementally building what I want the conversation to look like. I'm essentially doing the process of trying and building it incrementally and testing it out. Here I'm seeing an intermediate output, I'm then getting to look at the intermediate resized images and check that it's doing what I want. The key thing is it's producing Python code along the way, to do all this stuff. When it gets to the end down here, and I'm telling it to go and create the final Python program, really all it has to do is stitch together all the code that it's already created and modularize it a little bit. Add places where all these paths and things can be taken into the program as command line parameters rather than starting from scratch. Now, if you're not a programmer, how do you use this capability? One, you could kick-start a conversation. You can download it then, and then you could take it to a programmer. You could go hire a freelance developer or some development shop, to then review that, modify it if it doesn't work exactly like you want. But think of how much farther down the path you are. You can show them, here's my requirements, here's the conversation I had where it did exactly what I wanted, here's the initial Python program that it produced for me. Now, go and check that it's going to line up with what I just did. Here's what I want it to do, and here's what it produced. Do a quick audit of it, read through the code, test it inside a container, make sure it looks safe and reasonable, run it on some test cases for me and debug it and run it on my original test case, and then if it looks good, give it back to me. It's a different type of style of software development. It's going to create a new paradigm that's really exciting for creating these smaller tools. We can probably do that much less expensively than when we start from scratch and when we're trying to collect all the requirements, and we're trying to get everybody on the same page about what it's supposed to do and how it's supposed to work. Now we're actually having the end-user build up the process and the flow of what they wanted to do and interact with it through Code Interpreter and generate the initial starting point for the software before taking it to the programmer. The conversation itself becomes a set of requirements, and the software becomes the initial starting point for the developer, so hopefully they don't have to do a whole lot to get it into a final form of a usable tool. Now, if you're a software developer like me, this is awesome because you can take the conversation, you can output Python and you're way ahead of the game. Now in this case, I would have to go and work with a little bit to fix that part that it wasn't giving me the animated GIF I wanted and look for it and make sure there's no other bugs. I'd test it, do all the normal things I would do if I got a piece of software or if I had somebody else write a piece of software for me. But it's a really exciting capability that I think is worth talking about. Now again, I want to warn you, if you're not a programmer, you should not download these things and run them blindly. If you are a programmer, you should not blindly trust the software that comes out of it. You should download it, you should read all the code first, make sure you're comfortable with what it's doing, and you should test it in some safe environment like inside of a container or something else if you're at all concerned about what it's going to be doing. Now, in most cases, these things are going to be fairly straightforward, smaller bits of software, fairly easy to audit. I've created a lot of great tools for myself using this process. It's something really exciting that I think is a new style of software development that's going to be enabled because of Code Interpreter.
2026-07-09T05:10:50.601Z — transcript_dom — Zip Files for Automation | Coursera — 7601 chars
We can always go and upload individual files and download individual files to code interpreter, and we can certainly get by that way. But one of the wonderful things about code interpreter and the way that it works is that it can actually take zip files, unzip them, work on multiple files at once, and automate entire processes for us, and then zip up the results and give us back multiple results at once, which is way more convenient. It also allows us to do interesting things, like when you have a zip file, it can actually have a folder hierarchy inside of it. Or we can give additional things or tools or other possibilities, bits of python code, anything we want to do to code interpreter. But I'm going to give you a simple example of why an archive is so helpful. Let's imagine that you have a series of images, and you want to go and apply a transformation to them. You want to make them much more stylized, apply some filter to them. You've taken all these images, okay? Now, you want to stylize them. So, I'm going to upload an archive full of images to code interpreter. But this could be an archive of any of your files. It could be an archive full of Excel files that you want to combine. It might be an archive full of Excel files, and you want a specific visualization built for every single individual Excel file. Or maybe you want to search across those Excel files and filter them for a subset of rows and then create new Excel files, whatever automation you want to do. So whenever you start thinking about I have multiple files and I need to automate some process across them, you want to start thinking about uploading a zip file to code interpreter. Zip everything up that you're going to perform the operation on, describe the operation and tell it to perform it on the files that are within the zip file. Simple automation, super effective. So, I'm going to upload this archive full of images, and I'm going to say, please make these images much more stylized by dramatically increasing the contrast and saturation. So, simple pattern. Whenever you need to automate something on a set of files, zip them up into an archive and upload the zip. It's basically the archive interaction pattern. So now, what it's going to do is it's going to say, first thing, let's unzip the archive to access the images. It then pulls out each of the individual JPEGs, which were JPEGs that I'd pulled out of a movie, it then goes through and access provides each of them. Now, I could stop and I could download them one by one at this point, but that seems like a lot of work for me, particularly if I've got 100 files or 200 files. So, instead what I'm going to do is I'm just going to say zip up the files so that I can download them. So, we can use zip to automate the processing of multiple files so that we don't have to upload 100 different files. We can just zip them up and put them up there, but also to save us the time of how are we going to get all these files back down. We can zip them up in order to download them. So now, I can just go and click on download the enhanced images, and I now have all the images. The other thing that's really useful and I mentioned this, is that a zip file can have a folder structure to it. If you just upload individual files, you can't create meaning or organization with the folders. Now, you can do this both on the things you upload. For example, you could upload an archive that had different folders and you could say, take all of the files in this folder and all of the files in this folder and do something with them. And so because you have folders, it then allows you to refer to them to talk about certain file sets and already have them organized. So zip files, they don't just give you the ability to upload multiple things at once, but they also give you the ability to organize what the input you're giving it. You can organize the files into different directory structures, different categories, these types of things. And so it's a useful tool for organizing the input that you're giving it and then being able to refer and talk about different sets of files or groupings or to help it figure out what it needs. So for example, if you're trying to do a process and you have all the files related to cats in a folder, and then you go and ask a question related to cats, it's going to be much more likely that it's going to be able to figure out which files are relevant and automatically go and find them. So one way that having a zip file can help is it provides an index for code interpreter to go and find the right things. So in this example, this is one I showed before, I'm going to upload a zip file. I'm going to ask it to read each file and summarize what's in it, propose a folder structure for those files, propose better names for each file. This is another kind of really important aspect of this. It can rename files as well. And then when you have it all done, zip it up, or show it to me in this case. So it goes through, it reads all the files. And this is just a fantastic tool for file organization. You have a ton of files, zip them up, throw it in there. If you haven't kept the organization up to date with what's in those files, and you'd like to figure out a better way to do it, well, here's the AI driven file organization. It's an amazing little utility. It summarizes all the files, it proposes a file organization or hierarchy for all of them. And then I go and I have to work with it a little bit, because it couldn't quite figure out what some of the files are, but I tell it to go and try a different method. And this is a useful thing you'll want to know, is like if it doesn't quite work out, just tell it. Try another way, try to do it again, but come up with an alternate method. So, that this is a pattern of interaction, if it won't do it, say try an alternate method. Don't give up, try an alternate method and often it will find another way to do it. It goes through, and it proposes a new folder structure. We have course descriptions, receipts, reports, and policies, and it turns out that's actually an excellent organization of these files. And then I just tell it looks great. Create a zip file so I can download it. Now, the key thing of this zip file is it maintains the folder structure. So now, the folders are organized inside or the files are organized inside of those folders. So when I get it back, I'm not having to figure out how to do it. Now, notice one thing it didn't do for me. I forgot to get it to make sure that it went through and renamed the files. It didn't actually do that. And I think that's probably my fault for not confirming and saying, hey, the structure looks good, now rename everything, that was my fault there. I just said, go ahead and zip it up. And it did that, and now here's the zip file. So this is a fantastic thing to know about. Anytime you need to automate work on multiple files, anytime you need to automate the creation of directory structures and hierarchy, anytime it would be helpful to you for code interpreter to have a directory structure that organizes the files so it can more efficiently either find things or so that you can more efficiently tell it where things are or talk about the sets of files, zip files are a fantastic way to do that. So, whenever you need automation or whenever you need to output multiple things, or whenever you need structure in the thing that you're getting back in terms of the file organization hierarchy or the file organization hierarchy on the input, fantastic way to do it is with zip files.
2026-07-09T04:50:46.005Z — transcript_dom — Turning Conversations into Software Utilities | Coursera — 10018 chars
This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or computer scientist. But you can use it in conjunction with somebody else who is really good at those things if you aren't a programmer or computer scientist. If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. Now what is this technique? It's a very exciting one from the perspective of software development. One of the challenges that we have is we have all these little tools that we would like to have to help us out. All day long when I'm working and thinking it'd be really nice to have a piece of software that did this and simplified this process for me. Sometimes as a software engineer, I'll go and take the time to actually write the software to do that. The Code Interpreter creates an intriguing new possibility where we can actually turn a conversation with Code Interpreter into software. Now, if you are not a programmer, if you can't read the code, you should not go all the way through with this because you have to be able to look at the code and know if it's going to work correctly, if it's safe to run on your computer. It could delete all the files for all you know, if you can't read the code. We 100% know that large language models can make mistakes, and you need to pay attention to the code that comes out of what I'm going to show you, and if you can't read it and understand it, you should not proceed. You will need to go find a programmer to help the rest of the way. But a programmer at some point, a human software developer, needs to be involved in the analysis for what I'm going to show you. However, it's really exciting. Let's take a look at what we've got. This is an original conversation that I had where I took a movie and I extracted 10 different frames from the movie, I then went and display the images. I looked at it, I'm having this whole conversation, I resize the images and I did a bunch of other things. I turned each image into grayscale, I increase the contrast by 30%, and I turned the images into an animated GIF. I also created a PowerPoint presentation with one image per slide. Now I thought, wouldn't it be great if I could take this whole process so that I can repeat it? Now, I've repeated exactly this type of process for creating tools to help me with the creation of these videos and cataloging them. For example, automatically going and taking my videos and measuring how long they are, creating CSV files for them, doing other interesting things that then help me along. But any conversation that you go and have with Code Interpreter, you can turn into a piece of software in most cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present in the conversation. Then the last step in this thing, I created the PowerPoint and then I also created a CSV to catalog all this stuff. Here's what I'm going to do. This is the real magic now. Again, if you are not a programmer you need to find a programmer if you're going to take this approach. If you're a programmer, you're going to be able to rapidly accelerate the pace that you can create your own personal tools, because any conversation you have with Code Interpreter that yields a good result, you'll be able to kickstart a piece of software for yourself. Here's what I'm going to say. Turn this process into a Python program that I can download and run on my computer and provide the paths to the documents as command line argument, zip up the program for me to download. Now note you could have also gone and said create some GUI application or something else, anything that it has the tools to create, but I'm just going really simple, I'm just saying turn it into a Python program. You can also tell it to replace calls to GPT-4 with calls to OpenAI's API for GPT-4, the completion API. But I'm not going to do that here because I don't need it. What does it do? It says, sure, I can help with that. It's going to create a script that extracts all the frames from the video, resizes the images, converts the images to grayscale, creates a PowerPoint presentation of the images, catalogs them in a CSV file, and makes an animated GIF from the images. It then gives us the command that we're going to need to install all the Python packages for this piece of software, and then finally, it gives us the zip file, which I have actually gone and downloaded. I'm going to show you what this thing looks like now. Here is the application that it has built for me. I'm going to now run it on a new movie that I did not do before, and this thing is going to run for a second. It takes a few seconds to do all of the work that's in here. Then we should be able to see that it's produced a number of different outputs. It created the extracted frames, so if we go and looked at that, we have all the extracted frames from the video, we can go, and we see also that we have this frames presentation, which is all of the individual frames put into a PowerPoint presentation. We have the resized frames, we have the catalog image in CSV. Now, note something. I said it's really important to have a programmer look at this, and the reason is, is because it's not perfect. One of the things that it was supposed to do was to create an animated GIF, and it didn't do that. Now, as a developer, I can easily go and look at the program before I run it, which I did, and I saw that there was nothing problematic in it, I read the code. Then two, I could go and then say, it doesn't have that animated GIF or whatever it is and I could modify it and work with it to get it there. Now the key thing about this is it turned the conversation into a piece of software, and that flow of that conversation dictated the requirements for the software, and the user was interactively developing it. Now if we go back and look at the conversation, as I'm actually going through and running through it, I'm actually going to see incrementally building what I want the conversation to look like. I'm essentially doing the process of trying and building it incrementally and testing it out. Here I'm seeing an intermediate output, I'm then getting to look at the intermediate resized images and check that it's doing what I want. The key thing is it's producing Python code along the way, to do all this stuff. When it gets to the end down here, and I'm telling it to go and create the final Python program, really all it has to do is stitch together all the code that it's already created and modularize it a little bit. Add places where all these paths and things can be taken into the program as command line parameters rather than starting from scratch. Now, if you're not a programmer, how do you use this capability? One, you could kick-start a conversation. You can download it then, and then you could take it to a programmer. You could go hire a freelance developer or some development shop, to then review that, modify it if it doesn't work exactly like you want. But think of how much farther down the path you are. You can show them, here's my requirements, here's the conversation I had where it did exactly what I wanted, here's the initial Python program that it produced for me. Now, go and check that it's going to line up with what I just did. Here's what I want it to do, and here's what it produced. Do a quick audit of it, read through the code, test it inside a container, make sure it looks safe and reasonable, run it on some test cases for me and debug it and run it on my original test case, and then if it looks good, give it back to me. It's a different type of style of software development. It's going to create a new paradigm that's really exciting for creating these smaller tools. We can probably do that much less expensively than when we start from scratch and when we're trying to collect all the requirements, and we're trying to get everybody on the same page about what it's supposed to do and how it's supposed to work. Now we're actually having the end-user build up the process and the flow of what they wanted to do and interact with it through Code Interpreter and generate the initial starting point for the software before taking it to the programmer. The conversation itself becomes a set of requirements, and the software becomes the initial starting point for the developer, so hopefully they don't have to do a whole lot to get it into a final form of a usable tool. Now, if you're a software developer like me, this is awesome because you can take the conversation, you can output Python and you're way ahead of the game. Now in this case, I would have to go and work with a little bit to fix that part that it wasn't giving me the animated GIF I wanted and look for it and make sure there's no other bugs. I'd test it, do all the normal things I would do if I got a piece of software or if I had somebody else write a piece of software for me. But it's a really exciting capability that I think is worth talking about. Now again, I want to warn you, if you're not a programmer, you should not download these things and run them blindly. If you are a programmer, you should not blindly trust the software that comes out of it. You should download it, you should read all the code first, make sure you're comfortable with what it's doing, and you should test it in some safe environment like inside of a container or something else if you're at all concerned about what it's going to be doing. Now, in most cases, these things are going to be fairly straightforward, smaller bits of software, fairly easy to audit. I've created a lot of great tools for myself using this process. It's something really exciting that I think is a new style of software development that's going to be enabled because of Code Interpreter.
2026-07-09T04:33:01.645Z — transcript_dom — Working with Small Documents — 7258 chars
I want to help you gauge the difficulty of a task that you're about to start with Code Interpreter. Now, the reason for this is when you get started, you're going to have all ideas of things that you can go and try. But I want you to have a way of understanding how difficult each of your ideas are going to be to accomplish. If you understand two basic things that we're going to talk about, you'll be much better off in terms of gauging the difficulty of accomplishing a particular task. Those two things are, if you want to know how difficult a particular task is going to be, we want to go and look at the document or data that we're going to work with. The first question we want to ask is how structured or unstructured is that data? Structured data is something like a table. If you've got a bunch of tables that you're trying to read through and it's all in Excel format, that's a structured format. This is an example here where I'm uploading a CSV file, that is a structured format. It's like a table. It's some very clearly marked format where you know what all the parts are. CSV files, Excel files, all of these types of things are very structured. If you're working on data that has a clear structure to it, it's going to be much easier to do your tasks. If you're working on CSV files, Excel files, those types of things, and you're trying to do tasks that deal with their current structure or transform them into new structures that are relatively straightforward like visualization is not going to be too hard. Let me give you an example of this. I've got some data right here. This is some information that I've extracted from Vanderbilt's annual report. I've taken this data and I put it into a CSV file. It's very well-structured. I go and insert the CSV file into ChatGPT or Code Interpreter, and really quickly it can end up generating a visualization with almost no effort on my part, because this is a really easy data set to work with because it's very structured. Now, let's contrast that with an example where I took the same data but I didn't start from a CSV file, I started from the PDF of Vanderbilt's report. I had to go and extract the data first. This is what we see in the report. There's all these different pages of data and the thing that I'm looking for is buried on one of the pages. If you look at the text, it's structured maybe to a human, but it's not very structured to a Python program or Code Interpreter or something else. It's not in a great format once you extract it from the PDF. If you can read the PDF and look at it visually, it's structured, but if you actually look at the underlying text and the structure of the text itself, it's not, and it's not very clear what everything is and this thing is hard to read and look through. Basically, what I had to do is I had to take all of this unstructured data and I had to give it a lot of hints about what I could see visually. I told it look, the original data was a series of tables and headers before each. That was something that is easy to see visually. But if you looked at the underlying text it was hard to see. Each table had five columns, so I'm having to do all this work to help it rediscover the structure. If you have unstructured data like this, you're going to have to do a bunch of work to discover the structure. It takes a lot of back-and-forth with it before finally, now I've got it in a structured format. This is what it looks like when it's structured. Now we have a table with clearly delineated parts. We've helped ChatGPT or Code Interpreter to rediscover the parts. But if you look at all this text up here, it was not easily structured, it was not easy to extract and analyze. Important point number one, if you're working with structured data, your task is going to be so much easier in Code Interpreter. If you're working with unstructured data, task is going to be a lot harder and you're going to have to do more work. Just know that upfront. Now, second important point is how much unstructured text you are working with. How big of a document are you trying to analyze? This also is true for how big of a thing you're trying to create. I'll talk about later. But for right now, let's just focus on the documents or the data sets that we're working with. Unstructured versus structured makes a big difference and how easy it is to work with on Code Interpreter. Second one is how big the thing is that you're trying to work with, particularly for unstructured data. Now, for structured data, you can get away with much bigger data sets for most tasks. But for unstructured data where you want to have it read, and this is what I want you to think about is if you're thinking, I'm going to need Code Interpreter to read this and understand it in order to perform the operation well, it can only read and understand so much at one time. If you have a document that is bigger than Code Interpreter can read at one time, and how do you know how much Code Interpreter can read at one time, well, the metric I'm going to give you for this is if you can go and cut and paste all of the data from Code Interpreter. Let me just download this file right here and I'm going to show you an example of this. I've taken the IRS 1040 form. I'm going to go and I'm going to send a message. I basically had it extract this whole file into text. Now I'm going to cut and paste it into a chat message. It's going to tell us, hey, it looks like you've pasted the IRS 1040 for 2022. This document, what I did here is I took that PDF file and I extracted it into a text file, and the text I got out of it, I was able to just directly and copy into a chat message. If you could have taken that document and just copy and pasted everything that was in it into a single chat message and had it say, that's fine, that is what you would call a small, easy to work with document. If you've got a bunch of unstructured text that is of that size, no problem. Now, this is the Vanderbilt financial report that's like 40 pages long. It doesn't fit in a single text message or a single chat message. In this example, I've cut and pasted it. I've left this in place with the error message so that you can see it because it takes a little bit to do all this. Now I've copied and pasted 40 pages worth of material into Code Interpreter. What does it say? It says the message you submitted was too long. Please reload the conversation and submit something shorter. If you are in that situation, you are not going to be able to just tell Code Interpreter, read the document and have it read and analyze the whole thing at once. It's going to have to break it down into individual pieces and read and analyze individual pieces at a time. The moment you begin doing that, it becomes trickier and more difficult to perform the operations. Now, you can absolutely do it and I'm going to teach you all the techniques for doing that. But right now, particularly before you've learned all those techniques, try to start with things that are structured and start with documents that are smaller, and you could literally cut and paste it directly into a Code Interpreter message, because those are the ones that are going to be the easiest to work with getting started.
2026-07-09T04:29:15.777Z — transcript_dom — Working with Small Documents | Coursera — 7258 chars
I want to help you gauge the difficulty of a task that you're about to start with Code Interpreter. Now, the reason for this is when you get started, you're going to have all ideas of things that you can go and try. But I want you to have a way of understanding how difficult each of your ideas are going to be to accomplish. If you understand two basic things that we're going to talk about, you'll be much better off in terms of gauging the difficulty of accomplishing a particular task. Those two things are, if you want to know how difficult a particular task is going to be, we want to go and look at the document or data that we're going to work with. The first question we want to ask is how structured or unstructured is that data? Structured data is something like a table. If you've got a bunch of tables that you're trying to read through and it's all in Excel format, that's a structured format. This is an example here where I'm uploading a CSV file, that is a structured format. It's like a table. It's some very clearly marked format where you know what all the parts are. CSV files, Excel files, all of these types of things are very structured. If you're working on data that has a clear structure to it, it's going to be much easier to do your tasks. If you're working on CSV files, Excel files, those types of things, and you're trying to do tasks that deal with their current structure or transform them into new structures that are relatively straightforward like visualization is not going to be too hard. Let me give you an example of this. I've got some data right here. This is some information that I've extracted from Vanderbilt's annual report. I've taken this data and I put it into a CSV file. It's very well-structured. I go and insert the CSV file into ChatGPT or Code Interpreter, and really quickly it can end up generating a visualization with almost no effort on my part, because this is a really easy data set to work with because it's very structured. Now, let's contrast that with an example where I took the same data but I didn't start from a CSV file, I started from the PDF of Vanderbilt's report. I had to go and extract the data first. This is what we see in the report. There's all these different pages of data and the thing that I'm looking for is buried on one of the pages. If you look at the text, it's structured maybe to a human, but it's not very structured to a Python program or Code Interpreter or something else. It's not in a great format once you extract it from the PDF. If you can read the PDF and look at it visually, it's structured, but if you actually look at the underlying text and the structure of the text itself, it's not, and it's not very clear what everything is and this thing is hard to read and look through. Basically, what I had to do is I had to take all of this unstructured data and I had to give it a lot of hints about what I could see visually. I told it look, the original data was a series of tables and headers before each. That was something that is easy to see visually. But if you looked at the underlying text it was hard to see. Each table had five columns, so I'm having to do all this work to help it rediscover the structure. If you have unstructured data like this, you're going to have to do a bunch of work to discover the structure. It takes a lot of back-and-forth with it before finally, now I've got it in a structured format. This is what it looks like when it's structured. Now we have a table with clearly delineated parts. We've helped ChatGPT or Code Interpreter to rediscover the parts. But if you look at all this text up here, it was not easily structured, it was not easy to extract and analyze. Important point number one, if you're working with structured data, your task is going to be so much easier in Code Interpreter. If you're working with unstructured data, task is going to be a lot harder and you're going to have to do more work. Just know that upfront. Now, second important point is how much unstructured text you are working with. How big of a document are you trying to analyze? This also is true for how big of a thing you're trying to create. I'll talk about later. But for right now, let's just focus on the documents or the data sets that we're working with. Unstructured versus structured makes a big difference and how easy it is to work with on Code Interpreter. Second one is how big the thing is that you're trying to work with, particularly for unstructured data. Now, for structured data, you can get away with much bigger data sets for most tasks. But for unstructured data where you want to have it read, and this is what I want you to think about is if you're thinking, I'm going to need Code Interpreter to read this and understand it in order to perform the operation well, it can only read and understand so much at one time. If you have a document that is bigger than Code Interpreter can read at one time, and how do you know how much Code Interpreter can read at one time, well, the metric I'm going to give you for this is if you can go and cut and paste all of the data from Code Interpreter. Let me just download this file right here and I'm going to show you an example of this. I've taken the IRS 1040 form. I'm going to go and I'm going to send a message. I basically had it extract this whole file into text. Now I'm going to cut and paste it into a chat message. It's going to tell us, hey, it looks like you've pasted the IRS 1040 for 2022. This document, what I did here is I took that PDF file and I extracted it into a text file, and the text I got out of it, I was able to just directly and copy into a chat message. If you could have taken that document and just copy and pasted everything that was in it into a single chat message and had it say, that's fine, that is what you would call a small, easy to work with document. If you've got a bunch of unstructured text that is of that size, no problem. Now, this is the Vanderbilt financial report that's like 40 pages long. It doesn't fit in a single text message or a single chat message. In this example, I've cut and pasted it. I've left this in place with the error message so that you can see it because it takes a little bit to do all this. Now I've copied and pasted 40 pages worth of material into Code Interpreter. What does it say? It says the message you submitted was too long. Please reload the conversation and submit something shorter. If you are in that situation, you are not going to be able to just tell Code Interpreter, read the document and have it read and analyze the whole thing at once. It's going to have to break it down into individual pieces and read and analyze individual pieces at a time. The moment you begin doing that, it becomes trickier and more difficult to perform the operations. Now, you can absolutely do it and I'm going to teach you all the techniques for doing that. But right now, particularly before you've learned all those techniques, try to start with things that are structured and start with documents that are smaller, and you could literally cut and paste it directly into a Code Interpreter message, because those are the ones that are going to be the easiest to work with getting started.
2026-07-09T03:24:05.514Z — transcript_dom — Turning Conversations into Software Utilities | Coursera — 10018 chars
This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or computer scientist. But you can use it in conjunction with somebody else who is really good at those things if you aren't a programmer or computer scientist. If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. Now what is this technique? It's a very exciting one from the perspective of software development. One of the challenges that we have is we have all these little tools that we would like to have to help us out. All day long when I'm working and thinking it'd be really nice to have a piece of software that did this and simplified this process for me. Sometimes as a software engineer, I'll go and take the time to actually write the software to do that. The Code Interpreter creates an intriguing new possibility where we can actually turn a conversation with Code Interpreter into software. Now, if you are not a programmer, if you can't read the code, you should not go all the way through with this because you have to be able to look at the code and know if it's going to work correctly, if it's safe to run on your computer. It could delete all the files for all you know, if you can't read the code. We 100% know that large language models can make mistakes, and you need to pay attention to the code that comes out of what I'm going to show you, and if you can't read it and understand it, you should not proceed. You will need to go find a programmer to help the rest of the way. But a programmer at some point, a human software developer, needs to be involved in the analysis for what I'm going to show you. However, it's really exciting. Let's take a look at what we've got. This is an original conversation that I had where I took a movie and I extracted 10 different frames from the movie, I then went and display the images. I looked at it, I'm having this whole conversation, I resize the images and I did a bunch of other things. I turned each image into grayscale, I increase the contrast by 30%, and I turned the images into an animated GIF. I also created a PowerPoint presentation with one image per slide. Now I thought, wouldn't it be great if I could take this whole process so that I can repeat it? Now, I've repeated exactly this type of process for creating tools to help me with the creation of these videos and cataloging them. For example, automatically going and taking my videos and measuring how long they are, creating CSV files for them, doing other interesting things that then help me along. But any conversation that you go and have with Code Interpreter, you can turn into a piece of software in most cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present in the conversation. Then the last step in this thing, I created the PowerPoint and then I also created a CSV to catalog all this stuff. Here's what I'm going to do. This is the real magic now. Again, if you are not a programmer you need to find a programmer if you're going to take this approach. If you're a programmer, you're going to be able to rapidly accelerate the pace that you can create your own personal tools, because any conversation you have with Code Interpreter that yields a good result, you'll be able to kickstart a piece of software for yourself. Here's what I'm going to say. Turn this process into a Python program that I can download and run on my computer and provide the paths to the documents as command line argument, zip up the program for me to download. Now note you could have also gone and said create some GUI application or something else, anything that it has the tools to create, but I'm just going really simple, I'm just saying turn it into a Python program. You can also tell it to replace calls to GPT-4 with calls to OpenAI's API for GPT-4, the completion API. But I'm not going to do that here because I don't need it. What does it do? It says, sure, I can help with that. It's going to create a script that extracts all the frames from the video, resizes the images, converts the images to grayscale, creates a PowerPoint presentation of the images, catalogs them in a CSV file, and makes an animated GIF from the images. It then gives us the command that we're going to need to install all the Python packages for this piece of software, and then finally, it gives us the zip file, which I have actually gone and downloaded. I'm going to show you what this thing looks like now. Here is the application that it has built for me. I'm going to now run it on a new movie that I did not do before, and this thing is going to run for a second. It takes a few seconds to do all of the work that's in here. Then we should be able to see that it's produced a number of different outputs. It created the extracted frames, so if we go and looked at that, we have all the extracted frames from the video, we can go, and we see also that we have this frames presentation, which is all of the individual frames put into a PowerPoint presentation. We have the resized frames, we have the catalog image in CSV. Now, note something. I said it's really important to have a programmer look at this, and the reason is, is because it's not perfect. One of the things that it was supposed to do was to create an animated GIF, and it didn't do that. Now, as a developer, I can easily go and look at the program before I run it, which I did, and I saw that there was nothing problematic in it, I read the code. Then two, I could go and then say, it doesn't have that animated GIF or whatever it is and I could modify it and work with it to get it there. Now the key thing about this is it turned the conversation into a piece of software, and that flow of that conversation dictated the requirements for the software, and the user was interactively developing it. Now if we go back and look at the conversation, as I'm actually going through and running through it, I'm actually going to see incrementally building what I want the conversation to look like. I'm essentially doing the process of trying and building it incrementally and testing it out. Here I'm seeing an intermediate output, I'm then getting to look at the intermediate resized images and check that it's doing what I want. The key thing is it's producing Python code along the way, to do all this stuff. When it gets to the end down here, and I'm telling it to go and create the final Python program, really all it has to do is stitch together all the code that it's already created and modularize it a little bit. Add places where all these paths and things can be taken into the program as command line parameters rather than starting from scratch. Now, if you're not a programmer, how do you use this capability? One, you could kick-start a conversation. You can download it then, and then you could take it to a programmer. You could go hire a freelance developer or some development shop, to then review that, modify it if it doesn't work exactly like you want. But think of how much farther down the path you are. You can show them, here's my requirements, here's the conversation I had where it did exactly what I wanted, here's the initial Python program that it produced for me. Now, go and check that it's going to line up with what I just did. Here's what I want it to do, and here's what it produced. Do a quick audit of it, read through the code, test it inside a container, make sure it looks safe and reasonable, run it on some test cases for me and debug it and run it on my original test case, and then if it looks good, give it back to me. It's a different type of style of software development. It's going to create a new paradigm that's really exciting for creating these smaller tools. We can probably do that much less expensively than when we start from scratch and when we're trying to collect all the requirements, and we're trying to get everybody on the same page about what it's supposed to do and how it's supposed to work. Now we're actually having the end-user build up the process and the flow of what they wanted to do and interact with it through Code Interpreter and generate the initial starting point for the software before taking it to the programmer. The conversation itself becomes a set of requirements, and the software becomes the initial starting point for the developer, so hopefully they don't have to do a whole lot to get it into a final form of a usable tool. Now, if you're a software developer like me, this is awesome because you can take the conversation, you can output Python and you're way ahead of the game. Now in this case, I would have to go and work with a little bit to fix that part that it wasn't giving me the animated GIF I wanted and look for it and make sure there's no other bugs. I'd test it, do all the normal things I would do if I got a piece of software or if I had somebody else write a piece of software for me. But it's a really exciting capability that I think is worth talking about. Now again, I want to warn you, if you're not a programmer, you should not download these things and run them blindly. If you are a programmer, you should not blindly trust the software that comes out of it. You should download it, you should read all the code first, make sure you're comfortable with what it's doing, and you should test it in some safe environment like inside of a container or something else if you're at all concerned about what it's going to be doing. Now, in most cases, these things are going to be fairly straightforward, smaller bits of software, fairly easy to audit. I've created a lot of great tools for myself using this process. It's something really exciting that I think is a new style of software development that's going to be enabled because of Code Interpreter.
2026-07-09T01:43:07.788Z — transcript_dom — Working with Small Documents | Coursera — 7258 chars
I want to help you gauge the difficulty of a task that you're about to start with Code Interpreter. Now, the reason for this is when you get started, you're going to have all ideas of things that you can go and try. But I want you to have a way of understanding how difficult each of your ideas are going to be to accomplish. If you understand two basic things that we're going to talk about, you'll be much better off in terms of gauging the difficulty of accomplishing a particular task. Those two things are, if you want to know how difficult a particular task is going to be, we want to go and look at the document or data that we're going to work with. The first question we want to ask is how structured or unstructured is that data? Structured data is something like a table. If you've got a bunch of tables that you're trying to read through and it's all in Excel format, that's a structured format. This is an example here where I'm uploading a CSV file, that is a structured format. It's like a table. It's some very clearly marked format where you know what all the parts are. CSV files, Excel files, all of these types of things are very structured. If you're working on data that has a clear structure to it, it's going to be much easier to do your tasks. If you're working on CSV files, Excel files, those types of things, and you're trying to do tasks that deal with their current structure or transform them into new structures that are relatively straightforward like visualization is not going to be too hard. Let me give you an example of this. I've got some data right here. This is some information that I've extracted from Vanderbilt's annual report. I've taken this data and I put it into a CSV file. It's very well-structured. I go and insert the CSV file into ChatGPT or Code Interpreter, and really quickly it can end up generating a visualization with almost no effort on my part, because this is a really easy data set to work with because it's very structured. Now, let's contrast that with an example where I took the same data but I didn't start from a CSV file, I started from the PDF of Vanderbilt's report. I had to go and extract the data first. This is what we see in the report. There's all these different pages of data and the thing that I'm looking for is buried on one of the pages. If you look at the text, it's structured maybe to a human, but it's not very structured to a Python program or Code Interpreter or something else. It's not in a great format once you extract it from the PDF. If you can read the PDF and look at it visually, it's structured, but if you actually look at the underlying text and the structure of the text itself, it's not, and it's not very clear what everything is and this thing is hard to read and look through. Basically, what I had to do is I had to take all of this unstructured data and I had to give it a lot of hints about what I could see visually. I told it look, the original data was a series of tables and headers before each. That was something that is easy to see visually. But if you looked at the underlying text it was hard to see. Each table had five columns, so I'm having to do all this work to help it rediscover the structure. If you have unstructured data like this, you're going to have to do a bunch of work to discover the structure. It takes a lot of back-and-forth with it before finally, now I've got it in a structured format. This is what it looks like when it's structured. Now we have a table with clearly delineated parts. We've helped ChatGPT or Code Interpreter to rediscover the parts. But if you look at all this text up here, it was not easily structured, it was not easy to extract and analyze. Important point number one, if you're working with structured data, your task is going to be so much easier in Code Interpreter. If you're working with unstructured data, task is going to be a lot harder and you're going to have to do more work. Just know that upfront. Now, second important point is how much unstructured text you are working with. How big of a document are you trying to analyze? This also is true for how big of a thing you're trying to create. I'll talk about later. But for right now, let's just focus on the documents or the data sets that we're working with. Unstructured versus structured makes a big difference and how easy it is to work with on Code Interpreter. Second one is how big the thing is that you're trying to work with, particularly for unstructured data. Now, for structured data, you can get away with much bigger data sets for most tasks. But for unstructured data where you want to have it read, and this is what I want you to think about is if you're thinking, I'm going to need Code Interpreter to read this and understand it in order to perform the operation well, it can only read and understand so much at one time. If you have a document that is bigger than Code Interpreter can read at one time, and how do you know how much Code Interpreter can read at one time, well, the metric I'm going to give you for this is if you can go and cut and paste all of the data from Code Interpreter. Let me just download this file right here and I'm going to show you an example of this. I've taken the IRS 1040 form. I'm going to go and I'm going to send a message. I basically had it extract this whole file into text. Now I'm going to cut and paste it into a chat message. It's going to tell us, hey, it looks like you've pasted the IRS 1040 for 2022. This document, what I did here is I took that PDF file and I extracted it into a text file, and the text I got out of it, I was able to just directly and copy into a chat message. If you could have taken that document and just copy and pasted everything that was in it into a single chat message and had it say, that's fine, that is what you would call a small, easy to work with document. If you've got a bunch of unstructured text that is of that size, no problem. Now, this is the Vanderbilt financial report that's like 40 pages long. It doesn't fit in a single text message or a single chat message. In this example, I've cut and pasted it. I've left this in place with the error message so that you can see it because it takes a little bit to do all this. Now I've copied and pasted 40 pages worth of material into Code Interpreter. What does it say? It says the message you submitted was too long. Please reload the conversation and submit something shorter. If you are in that situation, you are not going to be able to just tell Code Interpreter, read the document and have it read and analyze the whole thing at once. It's going to have to break it down into individual pieces and read and analyze individual pieces at a time. The moment you begin doing that, it becomes trickier and more difficult to perform the operations. Now, you can absolutely do it and I'm going to teach you all the techniques for doing that. But right now, particularly before you've learned all those techniques, try to start with things that are structured and start with documents that are smaller, and you could literally cut and paste it directly into a Code Interpreter message, because those are the ones that are going to be the easiest to work with getting started.
2026-07-09T01:41:57.770Z — transcript_dom — Working with Small Documents | Coursera — 7258 chars
I want to help you gauge the difficulty of a task that you're about to start with Code Interpreter. Now, the reason for this is when you get started, you're going to have all ideas of things that you can go and try. But I want you to have a way of understanding how difficult each of your ideas are going to be to accomplish. If you understand two basic things that we're going to talk about, you'll be much better off in terms of gauging the difficulty of accomplishing a particular task. Those two things are, if you want to know how difficult a particular task is going to be, we want to go and look at the document or data that we're going to work with. The first question we want to ask is how structured or unstructured is that data? Structured data is something like a table. If you've got a bunch of tables that you're trying to read through and it's all in Excel format, that's a structured format. This is an example here where I'm uploading a CSV file, that is a structured format. It's like a table. It's some very clearly marked format where you know what all the parts are. CSV files, Excel files, all of these types of things are very structured. If you're working on data that has a clear structure to it, it's going to be much easier to do your tasks. If you're working on CSV files, Excel files, those types of things, and you're trying to do tasks that deal with their current structure or transform them into new structures that are relatively straightforward like visualization is not going to be too hard. Let me give you an example of this. I've got some data right here. This is some information that I've extracted from Vanderbilt's annual report. I've taken this data and I put it into a CSV file. It's very well-structured. I go and insert the CSV file into ChatGPT or Code Interpreter, and really quickly it can end up generating a visualization with almost no effort on my part, because this is a really easy data set to work with because it's very structured. Now, let's contrast that with an example where I took the same data but I didn't start from a CSV file, I started from the PDF of Vanderbilt's report. I had to go and extract the data first. This is what we see in the report. There's all these different pages of data and the thing that I'm looking for is buried on one of the pages. If you look at the text, it's structured maybe to a human, but it's not very structured to a Python program or Code Interpreter or something else. It's not in a great format once you extract it from the PDF. If you can read the PDF and look at it visually, it's structured, but if you actually look at the underlying text and the structure of the text itself, it's not, and it's not very clear what everything is and this thing is hard to read and look through. Basically, what I had to do is I had to take all of this unstructured data and I had to give it a lot of hints about what I could see visually. I told it look, the original data was a series of tables and headers before each. That was something that is easy to see visually. But if you looked at the underlying text it was hard to see. Each table had five columns, so I'm having to do all this work to help it rediscover the structure. If you have unstructured data like this, you're going to have to do a bunch of work to discover the structure. It takes a lot of back-and-forth with it before finally, now I've got it in a structured format. This is what it looks like when it's structured. Now we have a table with clearly delineated parts. We've helped ChatGPT or Code Interpreter to rediscover the parts. But if you look at all this text up here, it was not easily structured, it was not easy to extract and analyze. Important point number one, if you're working with structured data, your task is going to be so much easier in Code Interpreter. If you're working with unstructured data, task is going to be a lot harder and you're going to have to do more work. Just know that upfront. Now, second important point is how much unstructured text you are working with. How big of a document are you trying to analyze? This also is true for how big of a thing you're trying to create. I'll talk about later. But for right now, let's just focus on the documents or the data sets that we're working with. Unstructured versus structured makes a big difference and how easy it is to work with on Code Interpreter. Second one is how big the thing is that you're trying to work with, particularly for unstructured data. Now, for structured data, you can get away with much bigger data sets for most tasks. But for unstructured data where you want to have it read, and this is what I want you to think about is if you're thinking, I'm going to need Code Interpreter to read this and understand it in order to perform the operation well, it can only read and understand so much at one time. If you have a document that is bigger than Code Interpreter can read at one time, and how do you know how much Code Interpreter can read at one time, well, the metric I'm going to give you for this is if you can go and cut and paste all of the data from Code Interpreter. Let me just download this file right here and I'm going to show you an example of this. I've taken the IRS 1040 form. I'm going to go and I'm going to send a message. I basically had it extract this whole file into text. Now I'm going to cut and paste it into a chat message. It's going to tell us, hey, it looks like you've pasted the IRS 1040 for 2022. This document, what I did here is I took that PDF file and I extracted it into a text file, and the text I got out of it, I was able to just directly and copy into a chat message. If you could have taken that document and just copy and pasted everything that was in it into a single chat message and had it say, that's fine, that is what you would call a small, easy to work with document. If you've got a bunch of unstructured text that is of that size, no problem. Now, this is the Vanderbilt financial report that's like 40 pages long. It doesn't fit in a single text message or a single chat message. In this example, I've cut and pasted it. I've left this in place with the error message so that you can see it because it takes a little bit to do all this. Now I've copied and pasted 40 pages worth of material into Code Interpreter. What does it say? It says the message you submitted was too long. Please reload the conversation and submit something shorter. If you are in that situation, you are not going to be able to just tell Code Interpreter, read the document and have it read and analyze the whole thing at once. It's going to have to break it down into individual pieces and read and analyze individual pieces at a time. The moment you begin doing that, it becomes trickier and more difficult to perform the operations. Now, you can absolutely do it and I'm going to teach you all the techniques for doing that. But right now, particularly before you've learned all those techniques, try to start with things that are structured and start with documents that are smaller, and you could literally cut and paste it directly into a Code Interpreter message, because those are the ones that are going to be the easiest to work with getting started.
2026-07-09T01:03:16.487Z — transcript_dom — Zip Files for Automation | Coursera — 7601 chars
We can always go and upload individual files and download individual files to code interpreter, and we can certainly get by that way. But one of the wonderful things about code interpreter and the way that it works is that it can actually take zip files, unzip them, work on multiple files at once, and automate entire processes for us, and then zip up the results and give us back multiple results at once, which is way more convenient. It also allows us to do interesting things, like when you have a zip file, it can actually have a folder hierarchy inside of it. Or we can give additional things or tools or other possibilities, bits of python code, anything we want to do to code interpreter. But I'm going to give you a simple example of why an archive is so helpful. Let's imagine that you have a series of images, and you want to go and apply a transformation to them. You want to make them much more stylized, apply some filter to them. You've taken all these images, okay? Now, you want to stylize them. So, I'm going to upload an archive full of images to code interpreter. But this could be an archive of any of your files. It could be an archive full of Excel files that you want to combine. It might be an archive full of Excel files, and you want a specific visualization built for every single individual Excel file. Or maybe you want to search across those Excel files and filter them for a subset of rows and then create new Excel files, whatever automation you want to do. So whenever you start thinking about I have multiple files and I need to automate some process across them, you want to start thinking about uploading a zip file to code interpreter. Zip everything up that you're going to perform the operation on, describe the operation and tell it to perform it on the files that are within the zip file. Simple automation, super effective. So, I'm going to upload this archive full of images, and I'm going to say, please make these images much more stylized by dramatically increasing the contrast and saturation. So, simple pattern. Whenever you need to automate something on a set of files, zip them up into an archive and upload the zip. It's basically the archive interaction pattern. So now, what it's going to do is it's going to say, first thing, let's unzip the archive to access the images. It then pulls out each of the individual JPEGs, which were JPEGs that I'd pulled out of a movie, it then goes through and access provides each of them. Now, I could stop and I could download them one by one at this point, but that seems like a lot of work for me, particularly if I've got 100 files or 200 files. So, instead what I'm going to do is I'm just going to say zip up the files so that I can download them. So, we can use zip to automate the processing of multiple files so that we don't have to upload 100 different files. We can just zip them up and put them up there, but also to save us the time of how are we going to get all these files back down. We can zip them up in order to download them. So now, I can just go and click on download the enhanced images, and I now have all the images. The other thing that's really useful and I mentioned this, is that a zip file can have a folder structure to it. If you just upload individual files, you can't create meaning or organization with the folders. Now, you can do this both on the things you upload. For example, you could upload an archive that had different folders and you could say, take all of the files in this folder and all of the files in this folder and do something with them. And so because you have folders, it then allows you to refer to them to talk about certain file sets and already have them organized. So zip files, they don't just give you the ability to upload multiple things at once, but they also give you the ability to organize what the input you're giving it. You can organize the files into different directory structures, different categories, these types of things. And so it's a useful tool for organizing the input that you're giving it and then being able to refer and talk about different sets of files or groupings or to help it figure out what it needs. So for example, if you're trying to do a process and you have all the files related to cats in a folder, and then you go and ask a question related to cats, it's going to be much more likely that it's going to be able to figure out which files are relevant and automatically go and find them. So one way that having a zip file can help is it provides an index for code interpreter to go and find the right things. So in this example, this is one I showed before, I'm going to upload a zip file. I'm going to ask it to read each file and summarize what's in it, propose a folder structure for those files, propose better names for each file. This is another kind of really important aspect of this. It can rename files as well. And then when you have it all done, zip it up, or show it to me in this case. So it goes through, it reads all the files. And this is just a fantastic tool for file organization. You have a ton of files, zip them up, throw it in there. If you haven't kept the organization up to date with what's in those files, and you'd like to figure out a better way to do it, well, here's the AI driven file organization. It's an amazing little utility. It summarizes all the files, it proposes a file organization or hierarchy for all of them. And then I go and I have to work with it a little bit, because it couldn't quite figure out what some of the files are, but I tell it to go and try a different method. And this is a useful thing you'll want to know, is like if it doesn't quite work out, just tell it. Try another way, try to do it again, but come up with an alternate method. So, that this is a pattern of interaction, if it won't do it, say try an alternate method. Don't give up, try an alternate method and often it will find another way to do it. It goes through, and it proposes a new folder structure. We have course descriptions, receipts, reports, and policies, and it turns out that's actually an excellent organization of these files. And then I just tell it looks great. Create a zip file so I can download it. Now, the key thing of this zip file is it maintains the folder structure. So now, the folders are organized inside or the files are organized inside of those folders. So when I get it back, I'm not having to figure out how to do it. Now, notice one thing it didn't do for me. I forgot to get it to make sure that it went through and renamed the files. It didn't actually do that. And I think that's probably my fault for not confirming and saying, hey, the structure looks good, now rename everything, that was my fault there. I just said, go ahead and zip it up. And it did that, and now here's the zip file. So this is a fantastic thing to know about. Anytime you need to automate work on multiple files, anytime you need to automate the creation of directory structures and hierarchy, anytime it would be helpful to you for code interpreter to have a directory structure that organizes the files so it can more efficiently either find things or so that you can more efficiently tell it where things are or talk about the sets of files, zip files are a fantastic way to do that. So, whenever you need automation or whenever you need to output multiple things, or whenever you need structure in the thing that you're getting back in terms of the file organization hierarchy or the file organization hierarchy on the input, fantastic way to do it is with zip files.
2026-07-09T00:57:31.161Z — transcript_dom — Zip Files for Automation | Coursera — 7601 chars
We can always go and upload individual files and download individual files to code interpreter, and we can certainly get by that way. But one of the wonderful things about code interpreter and the way that it works is that it can actually take zip files, unzip them, work on multiple files at once, and automate entire processes for us, and then zip up the results and give us back multiple results at once, which is way more convenient. It also allows us to do interesting things, like when you have a zip file, it can actually have a folder hierarchy inside of it. Or we can give additional things or tools or other possibilities, bits of python code, anything we want to do to code interpreter. But I'm going to give you a simple example of why an archive is so helpful. Let's imagine that you have a series of images, and you want to go and apply a transformation to them. You want to make them much more stylized, apply some filter to them. You've taken all these images, okay? Now, you want to stylize them. So, I'm going to upload an archive full of images to code interpreter. But this could be an archive of any of your files. It could be an archive full of Excel files that you want to combine. It might be an archive full of Excel files, and you want a specific visualization built for every single individual Excel file. Or maybe you want to search across those Excel files and filter them for a subset of rows and then create new Excel files, whatever automation you want to do. So whenever you start thinking about I have multiple files and I need to automate some process across them, you want to start thinking about uploading a zip file to code interpreter. Zip everything up that you're going to perform the operation on, describe the operation and tell it to perform it on the files that are within the zip file. Simple automation, super effective. So, I'm going to upload this archive full of images, and I'm going to say, please make these images much more stylized by dramatically increasing the contrast and saturation. So, simple pattern. Whenever you need to automate something on a set of files, zip them up into an archive and upload the zip. It's basically the archive interaction pattern. So now, what it's going to do is it's going to say, first thing, let's unzip the archive to access the images. It then pulls out each of the individual JPEGs, which were JPEGs that I'd pulled out of a movie, it then goes through and access provides each of them. Now, I could stop and I could download them one by one at this point, but that seems like a lot of work for me, particularly if I've got 100 files or 200 files. So, instead what I'm going to do is I'm just going to say zip up the files so that I can download them. So, we can use zip to automate the processing of multiple files so that we don't have to upload 100 different files. We can just zip them up and put them up there, but also to save us the time of how are we going to get all these files back down. We can zip them up in order to download them. So now, I can just go and click on download the enhanced images, and I now have all the images. The other thing that's really useful and I mentioned this, is that a zip file can have a folder structure to it. If you just upload individual files, you can't create meaning or organization with the folders. Now, you can do this both on the things you upload. For example, you could upload an archive that had different folders and you could say, take all of the files in this folder and all of the files in this folder and do something with them. And so because you have folders, it then allows you to refer to them to talk about certain file sets and already have them organized. So zip files, they don't just give you the ability to upload multiple things at once, but they also give you the ability to organize what the input you're giving it. You can organize the files into different directory structures, different categories, these types of things. And so it's a useful tool for organizing the input that you're giving it and then being able to refer and talk about different sets of files or groupings or to help it figure out what it needs. So for example, if you're trying to do a process and you have all the files related to cats in a folder, and then you go and ask a question related to cats, it's going to be much more likely that it's going to be able to figure out which files are relevant and automatically go and find them. So one way that having a zip file can help is it provides an index for code interpreter to go and find the right things. So in this example, this is one I showed before, I'm going to upload a zip file. I'm going to ask it to read each file and summarize what's in it, propose a folder structure for those files, propose better names for each file. This is another kind of really important aspect of this. It can rename files as well. And then when you have it all done, zip it up, or show it to me in this case. So it goes through, it reads all the files. And this is just a fantastic tool for file organization. You have a ton of files, zip them up, throw it in there. If you haven't kept the organization up to date with what's in those files, and you'd like to figure out a better way to do it, well, here's the AI driven file organization. It's an amazing little utility. It summarizes all the files, it proposes a file organization or hierarchy for all of them. And then I go and I have to work with it a little bit, because it couldn't quite figure out what some of the files are, but I tell it to go and try a different method. And this is a useful thing you'll want to know, is like if it doesn't quite work out, just tell it. Try another way, try to do it again, but come up with an alternate method. So, that this is a pattern of interaction, if it won't do it, say try an alternate method. Don't give up, try an alternate method and often it will find another way to do it. It goes through, and it proposes a new folder structure. We have course descriptions, receipts, reports, and policies, and it turns out that's actually an excellent organization of these files. And then I just tell it looks great. Create a zip file so I can download it. Now, the key thing of this zip file is it maintains the folder structure. So now, the folders are organized inside or the files are organized inside of those folders. So when I get it back, I'm not having to figure out how to do it. Now, notice one thing it didn't do for me. I forgot to get it to make sure that it went through and renamed the files. It didn't actually do that. And I think that's probably my fault for not confirming and saying, hey, the structure looks good, now rename everything, that was my fault there. I just said, go ahead and zip it up. And it did that, and now here's the zip file. So this is a fantastic thing to know about. Anytime you need to automate work on multiple files, anytime you need to automate the creation of directory structures and hierarchy, anytime it would be helpful to you for code interpreter to have a directory structure that organizes the files so it can more efficiently either find things or so that you can more efficiently tell it where things are or talk about the sets of files, zip files are a fantastic way to do that. So, whenever you need automation or whenever you need to output multiple things, or whenever you need structure in the thing that you're getting back in terms of the file organization hierarchy or the file organization hierarchy on the input, fantastic way to do it is with zip files.
2026-07-09T00:53:20.344Z — transcript_dom — Turning Conversations into Software Utilities | Coursera — 10018 chars
This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or computer scientist. But you can use it in conjunction with somebody else who is really good at those things if you aren't a programmer or computer scientist. If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. Now what is this technique? It's a very exciting one from the perspective of software development. One of the challenges that we have is we have all these little tools that we would like to have to help us out. All day long when I'm working and thinking it'd be really nice to have a piece of software that did this and simplified this process for me. Sometimes as a software engineer, I'll go and take the time to actually write the software to do that. The Code Interpreter creates an intriguing new possibility where we can actually turn a conversation with Code Interpreter into software. Now, if you are not a programmer, if you can't read the code, you should not go all the way through with this because you have to be able to look at the code and know if it's going to work correctly, if it's safe to run on your computer. It could delete all the files for all you know, if you can't read the code. We 100% know that large language models can make mistakes, and you need to pay attention to the code that comes out of what I'm going to show you, and if you can't read it and understand it, you should not proceed. You will need to go find a programmer to help the rest of the way. But a programmer at some point, a human software developer, needs to be involved in the analysis for what I'm going to show you. However, it's really exciting. Let's take a look at what we've got. This is an original conversation that I had where I took a movie and I extracted 10 different frames from the movie, I then went and display the images. I looked at it, I'm having this whole conversation, I resize the images and I did a bunch of other things. I turned each image into grayscale, I increase the contrast by 30%, and I turned the images into an animated GIF. I also created a PowerPoint presentation with one image per slide. Now I thought, wouldn't it be great if I could take this whole process so that I can repeat it? Now, I've repeated exactly this type of process for creating tools to help me with the creation of these videos and cataloging them. For example, automatically going and taking my videos and measuring how long they are, creating CSV files for them, doing other interesting things that then help me along. But any conversation that you go and have with Code Interpreter, you can turn into a piece of software in most cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present in the conversation. Then the last step in this thing, I created the PowerPoint and then I also created a CSV to catalog all this stuff. Here's what I'm going to do. This is the real magic now. Again, if you are not a programmer you need to find a programmer if you're going to take this approach. If you're a programmer, you're going to be able to rapidly accelerate the pace that you can create your own personal tools, because any conversation you have with Code Interpreter that yields a good result, you'll be able to kickstart a piece of software for yourself. Here's what I'm going to say. Turn this process into a Python program that I can download and run on my computer and provide the paths to the documents as command line argument, zip up the program for me to download. Now note you could have also gone and said create some GUI application or something else, anything that it has the tools to create, but I'm just going really simple, I'm just saying turn it into a Python program. You can also tell it to replace calls to GPT-4 with calls to OpenAI's API for GPT-4, the completion API. But I'm not going to do that here because I don't need it. What does it do? It says, sure, I can help with that. It's going to create a script that extracts all the frames from the video, resizes the images, converts the images to grayscale, creates a PowerPoint presentation of the images, catalogs them in a CSV file, and makes an animated GIF from the images. It then gives us the command that we're going to need to install all the Python packages for this piece of software, and then finally, it gives us the zip file, which I have actually gone and downloaded. I'm going to show you what this thing looks like now. Here is the application that it has built for me. I'm going to now run it on a new movie that I did not do before, and this thing is going to run for a second. It takes a few seconds to do all of the work that's in here. Then we should be able to see that it's produced a number of different outputs. It created the extracted frames, so if we go and looked at that, we have all the extracted frames from the video, we can go, and we see also that we have this frames presentation, which is all of the individual frames put into a PowerPoint presentation. We have the resized frames, we have the catalog image in CSV. Now, note something. I said it's really important to have a programmer look at this, and the reason is, is because it's not perfect. One of the things that it was supposed to do was to create an animated GIF, and it didn't do that. Now, as a developer, I can easily go and look at the program before I run it, which I did, and I saw that there was nothing problematic in it, I read the code. Then two, I could go and then say, it doesn't have that animated GIF or whatever it is and I could modify it and work with it to get it there. Now the key thing about this is it turned the conversation into a piece of software, and that flow of that conversation dictated the requirements for the software, and the user was interactively developing it. Now if we go back and look at the conversation, as I'm actually going through and running through it, I'm actually going to see incrementally building what I want the conversation to look like. I'm essentially doing the process of trying and building it incrementally and testing it out. Here I'm seeing an intermediate output, I'm then getting to look at the intermediate resized images and check that it's doing what I want. The key thing is it's producing Python code along the way, to do all this stuff. When it gets to the end down here, and I'm telling it to go and create the final Python program, really all it has to do is stitch together all the code that it's already created and modularize it a little bit. Add places where all these paths and things can be taken into the program as command line parameters rather than starting from scratch. Now, if you're not a programmer, how do you use this capability? One, you could kick-start a conversation. You can download it then, and then you could take it to a programmer. You could go hire a freelance developer or some development shop, to then review that, modify it if it doesn't work exactly like you want. But think of how much farther down the path you are. You can show them, here's my requirements, here's the conversation I had where it did exactly what I wanted, here's the initial Python program that it produced for me. Now, go and check that it's going to line up with what I just did. Here's what I want it to do, and here's what it produced. Do a quick audit of it, read through the code, test it inside a container, make sure it looks safe and reasonable, run it on some test cases for me and debug it and run it on my original test case, and then if it looks good, give it back to me. It's a different type of style of software development. It's going to create a new paradigm that's really exciting for creating these smaller tools. We can probably do that much less expensively than when we start from scratch and when we're trying to collect all the requirements, and we're trying to get everybody on the same page about what it's supposed to do and how it's supposed to work. Now we're actually having the end-user build up the process and the flow of what they wanted to do and interact with it through Code Interpreter and generate the initial starting point for the software before taking it to the programmer. The conversation itself becomes a set of requirements, and the software becomes the initial starting point for the developer, so hopefully they don't have to do a whole lot to get it into a final form of a usable tool. Now, if you're a software developer like me, this is awesome because you can take the conversation, you can output Python and you're way ahead of the game. Now in this case, I would have to go and work with a little bit to fix that part that it wasn't giving me the animated GIF I wanted and look for it and make sure there's no other bugs. I'd test it, do all the normal things I would do if I got a piece of software or if I had somebody else write a piece of software for me. But it's a really exciting capability that I think is worth talking about. Now again, I want to warn you, if you're not a programmer, you should not download these things and run them blindly. If you are a programmer, you should not blindly trust the software that comes out of it. You should download it, you should read all the code first, make sure you're comfortable with what it's doing, and you should test it in some safe environment like inside of a container or something else if you're at all concerned about what it's going to be doing. Now, in most cases, these things are going to be fairly straightforward, smaller bits of software, fairly easy to audit. I've created a lot of great tools for myself using this process. It's something really exciting that I think is a new style of software development that's going to be enabled because of Code Interpreter.
2026-07-09T00:52:53.439Z — transcript_dom — Turning Conversations into Software Utilities | Coursera — 10018 chars
This next capability I'm going to talk about is really one that you can only take from start to finish if you know how to program or you're a software engineer or computer scientist. But you can use it in conjunction with somebody else who is really good at those things if you aren't a programmer or computer scientist. If you're an organization that has a bunch of programmers or you have a friend that it's a programmer, this is a way that you could do that. Now what is this technique? It's a very exciting one from the perspective of software development. One of the challenges that we have is we have all these little tools that we would like to have to help us out. All day long when I'm working and thinking it'd be really nice to have a piece of software that did this and simplified this process for me. Sometimes as a software engineer, I'll go and take the time to actually write the software to do that. The Code Interpreter creates an intriguing new possibility where we can actually turn a conversation with Code Interpreter into software. Now, if you are not a programmer, if you can't read the code, you should not go all the way through with this because you have to be able to look at the code and know if it's going to work correctly, if it's safe to run on your computer. It could delete all the files for all you know, if you can't read the code. We 100% know that large language models can make mistakes, and you need to pay attention to the code that comes out of what I'm going to show you, and if you can't read it and understand it, you should not proceed. You will need to go find a programmer to help the rest of the way. But a programmer at some point, a human software developer, needs to be involved in the analysis for what I'm going to show you. However, it's really exciting. Let's take a look at what we've got. This is an original conversation that I had where I took a movie and I extracted 10 different frames from the movie, I then went and display the images. I looked at it, I'm having this whole conversation, I resize the images and I did a bunch of other things. I turned each image into grayscale, I increase the contrast by 30%, and I turned the images into an animated GIF. I also created a PowerPoint presentation with one image per slide. Now I thought, wouldn't it be great if I could take this whole process so that I can repeat it? Now, I've repeated exactly this type of process for creating tools to help me with the creation of these videos and cataloging them. For example, automatically going and taking my videos and measuring how long they are, creating CSV files for them, doing other interesting things that then help me along. But any conversation that you go and have with Code Interpreter, you can turn into a piece of software in most cases that you can then run completely separately from Code Interpreter and you can even have it call out to OpenAI's APIs to replace the part where GPT-4 was present in the conversation. Then the last step in this thing, I created the PowerPoint and then I also created a CSV to catalog all this stuff. Here's what I'm going to do. This is the real magic now. Again, if you are not a programmer you need to find a programmer if you're going to take this approach. If you're a programmer, you're going to be able to rapidly accelerate the pace that you can create your own personal tools, because any conversation you have with Code Interpreter that yields a good result, you'll be able to kickstart a piece of software for yourself. Here's what I'm going to say. Turn this process into a Python program that I can download and run on my computer and provide the paths to the documents as command line argument, zip up the program for me to download. Now note you could have also gone and said create some GUI application or something else, anything that it has the tools to create, but I'm just going really simple, I'm just saying turn it into a Python program. You can also tell it to replace calls to GPT-4 with calls to OpenAI's API for GPT-4, the completion API. But I'm not going to do that here because I don't need it. What does it do? It says, sure, I can help with that. It's going to create a script that extracts all the frames from the video, resizes the images, converts the images to grayscale, creates a PowerPoint presentation of the images, catalogs them in a CSV file, and makes an animated GIF from the images. It then gives us the command that we're going to need to install all the Python packages for this piece of software, and then finally, it gives us the zip file, which I have actually gone and downloaded. I'm going to show you what this thing looks like now. Here is the application that it has built for me. I'm going to now run it on a new movie that I did not do before, and this thing is going to run for a second. It takes a few seconds to do all of the work that's in here. Then we should be able to see that it's produced a number of different outputs. It created the extracted frames, so if we go and looked at that, we have all the extracted frames from the video, we can go, and we see also that we have this frames presentation, which is all of the individual frames put into a PowerPoint presentation. We have the resized frames, we have the catalog image in CSV. Now, note something. I said it's really important to have a programmer look at this, and the reason is, is because it's not perfect. One of the things that it was supposed to do was to create an animated GIF, and it didn't do that. Now, as a developer, I can easily go and look at the program before I run it, which I did, and I saw that there was nothing problematic in it, I read the code. Then two, I could go and then say, it doesn't have that animated GIF or whatever it is and I could modify it and work with it to get it there. Now the key thing about this is it turned the conversation into a piece of software, and that flow of that conversation dictated the requirements for the software, and the user was interactively developing it. Now if we go back and look at the conversation, as I'm actually going through and running through it, I'm actually going to see incrementally building what I want the conversation to look like. I'm essentially doing the process of trying and building it incrementally and testing it out. Here I'm seeing an intermediate output, I'm then getting to look at the intermediate resized images and check that it's doing what I want. The key thing is it's producing Python code along the way, to do all this stuff. When it gets to the end down here, and I'm telling it to go and create the final Python program, really all it has to do is stitch together all the code that it's already created and modularize it a little bit. Add places where all these paths and things can be taken into the program as command line parameters rather than starting from scratch. Now, if you're not a programmer, how do you use this capability? One, you could kick-start a conversation. You can download it then, and then you could take it to a programmer. You could go hire a freelance developer or some development shop, to then review that, modify it if it doesn't work exactly like you want. But think of how much farther down the path you are. You can show them, here's my requirements, here's the conversation I had where it did exactly what I wanted, here's the initial Python program that it produced for me. Now, go and check that it's going to line up with what I just did. Here's what I want it to do, and here's what it produced. Do a quick audit of it, read through the code, test it inside a container, make sure it looks safe and reasonable, run it on some test cases for me and debug it and run it on my original test case, and then if it looks good, give it back to me. It's a different type of style of software development. It's going to create a new paradigm that's really exciting for creating these smaller tools. We can probably do that much less expensively than when we start from scratch and when we're trying to collect all the requirements, and we're trying to get everybody on the same page about what it's supposed to do and how it's supposed to work. Now we're actually having the end-user build up the process and the flow of what they wanted to do and interact with it through Code Interpreter and generate the initial starting point for the software before taking it to the programmer. The conversation itself becomes a set of requirements, and the software becomes the initial starting point for the developer, so hopefully they don't have to do a whole lot to get it into a final form of a usable tool. Now, if you're a software developer like me, this is awesome because you can take the conversation, you can output Python and you're way ahead of the game. Now in this case, I would have to go and work with a little bit to fix that part that it wasn't giving me the animated GIF I wanted and look for it and make sure there's no other bugs. I'd test it, do all the normal things I would do if I got a piece of software or if I had somebody else write a piece of software for me. But it's a really exciting capability that I think is worth talking about. Now again, I want to warn you, if you're not a programmer, you should not download these things and run them blindly. If you are a programmer, you should not blindly trust the software that comes out of it. You should download it, you should read all the code first, make sure you're comfortable with what it's doing, and you should test it in some safe environment like inside of a container or something else if you're at all concerned about what it's going to be doing. Now, in most cases, these things are going to be fairly straightforward, smaller bits of software, fairly easy to audit. I've created a lot of great tools for myself using this process. It's something really exciting that I think is a new style of software development that's going to be enabled because of Code Interpreter.
2026-07-09T00:08:04.974Z — transcript_dom — Reactive vs. Deliberative Agents in the Real World | Coursera — 3366 chars
Picture this. You walk into a room and the light turns on automatically. Now picture asking your smart assistant to plan a movie night. Both systems are reacting, but very differently. One's fast and simple. The other thinks before it acts. That's the difference between reactive and deliberative agents. Reactive agents respond immediately to changes in their environment. There's no deep reasoning. They sense and act. Think of a thermostat adjusting heat, a car's collision detection system, a robot vacuum avoiding a chair. They are reliable, fast and often operate in real-time. But they don't plan or learn. They're great when the response is obvious and the goal is speed. Deliberative agents take input, reason and then decide on a course of action. This can involve evaluating options, forecasting outcomes or balancing priorities. Let's go back to that movie night example. An agent like Amazon Alexa might check your calendar, suggest available times, dim the lights, queue up your streaming app. These agents need internal memory, decision trees, maybe even a task manager and they are ideal when the task has multiple steps or variables. So it's not about which is better. It's about choosing the right tool for the right task. By the end of this video, you will be able to differentiate between reactive, deliberative and hybrid agent behaviors. Identify real-world examples of when each agent type is most effective. Understand the trade-offs between speed, planning and adaptability in agent design. Recognize how hybrid agents combine fast reactions with strategic planning. Apply these concepts to select the right agent behavior model for specific use cases. Some agents combine both approaches. A hybrid agent can react in real-time but also make long-term decisions. Think of a self-driving car. It reacts instantly to a pedestrian but also follows a route, reroutes around traffic and estimates arrival time. That's reactive plus deliberative working together. Whether it's turning on a light or planning your week, AI agents work in different ways to get things done. And as a designer, you will need to know when to keep it simple and when to let your agent think. Before we move on, take a moment to reflect and answer the quick question on your screen. In this video, you learn. Reactive agents act instantly without thinking. Great for simple, fast tasks. Deliberative agents reason before acting. Ideal for multi-step decisions. Hybrid agents combine quick reactions with planned actions. Each agent type suits different task needs. Speed was a strategy. Designers must choose the right type based on the task complexity. Agents aren't just programmed responders. They are decision makers shaped by how they perceive and process the world. Projects have succeeded or stalled based on whether developers or product managers chose a reactive setup for speed or deliberative one for complexity or a hybrid for balance. Take a moment to think in your own work or experience. Have you seen systems that acted too quickly or too slowly because they weren't built with the right kind of agent? You've seen how agents can respond instantly or plan deliberately. But how do these ideas play out in real tools and platforms? In the next case study, we'll compare two popular agent approaches, Rasa and AutoGPT and explore when each one makes sense.
2026-06-27T10:01:43.926Z — reading_dom — Hands-on Lab: Windows Update | Coursera — 2220 chars
Module 1 Common Security Threats and Risks Module 2 Security Best Practices Module 3 Safe Browsing Practices Module 4 Final Exam and Project Hands-on Lab: Windows Update Course Introduction Video . Duration: 3 minutes 3 min Course Overview Reading . Duration: 10 minutes 10 min Confidentiality, Integrity, and Availability Video . Duration: 5 minutes 5 min Security and Information Privacy Video . Duration: 8 minutes 8 min Activity: Exploring Information Privacy Ungraded Plugin . Duration: 5 minutes 5 min Intellectual Property and Types of Confidential Information Reading . Duration: 5 minutes 5 min Microsoft Windows Server Lab Environment Video . Duration: 2 minutes 2 min Threats and Breaches Video . Duration: 7 minutes 7 min Hands-on Lab: Windows Update Ungraded App Item . Duration: 45 minutes 45 min Threat Types Video . Duration: 8 minutes 8 min Phishing, Social Engineering, and Other Attacks Video . Duration: 6 minutes 6 min Activity: Identifying an Attack Ungraded Plugin . Duration: 15 minutes 15 min Physical Security for Computing Devices Reading . Duration: 5 minutes 5 min Case Study Reading . Duration: 5 minutes 5 min Activity: Identifying Malware Threats Ungraded Plugin . Duration: 5 minutes 5 min Windows Defender Antivirus Reading . Duration: 3 minutes 3 min Hands-on Lab: Windows Defender Antivirus Ungraded App Item . Duration: 20 minutes 20 min Just for Fun - Hands-on Lab: Hacker Typer Reading . Duration: 10 minutes 10 min Summary & Highlights Reading . Duration: 1 minute 1 min Practice Quiz: Common Security Threats and Risks Practice Assignment . Duration: 10 minutes 10 min Graded Quiz: Common Security Threats and Risks Graded Assignment . Duration: 30 minutes 30 min We learn most effectively through active, hands-on experiences. This lab offers a cloud-based Microsoft Windows Server environment equipped with all the necessary software to successfully complete the tasks. In case you need to view the lab instructions click HERE to open in a new tab. If you want to know more about Microsoft Windows Server Lab Environment, click here . Check the I agree to use this app responsibly box and click the Launch App button to access your cloud workspace.
2026-06-27T08:20:07.445Z — reading_dom — Threats and Breaches | Coursera — 2220 chars
Module 1 Common Security Threats and Risks Module 2 Security Best Practices Module 3 Safe Browsing Practices Module 4 Final Exam and Project Hands-on Lab: Windows Update Course Introduction Video . Duration: 3 minutes 3 min Course Overview Reading . Duration: 10 minutes 10 min Confidentiality, Integrity, and Availability Video . Duration: 5 minutes 5 min Security and Information Privacy Video . Duration: 8 minutes 8 min Activity: Exploring Information Privacy Ungraded Plugin . Duration: 5 minutes 5 min Intellectual Property and Types of Confidential Information Reading . Duration: 5 minutes 5 min Microsoft Windows Server Lab Environment Video . Duration: 2 minutes 2 min Threats and Breaches Video . Duration: 7 minutes 7 min Hands-on Lab: Windows Update Ungraded App Item . Duration: 45 minutes 45 min Threat Types Video . Duration: 8 minutes 8 min Phishing, Social Engineering, and Other Attacks Video . Duration: 6 minutes 6 min Activity: Identifying an Attack Ungraded Plugin . Duration: 15 minutes 15 min Physical Security for Computing Devices Reading . Duration: 5 minutes 5 min Case Study Reading . Duration: 5 minutes 5 min Activity: Identifying Malware Threats Ungraded Plugin . Duration: 5 minutes 5 min Windows Defender Antivirus Reading . Duration: 3 minutes 3 min Hands-on Lab: Windows Defender Antivirus Ungraded App Item . Duration: 20 minutes 20 min Just for Fun - Hands-on Lab: Hacker Typer Reading . Duration: 10 minutes 10 min Summary & Highlights Reading . Duration: 1 minute 1 min Practice Quiz: Common Security Threats and Risks Practice Assignment . Duration: 10 minutes 10 min Graded Quiz: Common Security Threats and Risks Graded Assignment . Duration: 30 minutes 30 min We learn most effectively through active, hands-on experiences. This lab offers a cloud-based Microsoft Windows Server environment equipped with all the necessary software to successfully complete the tasks. In case you need to view the lab instructions click HERE to open in a new tab. If you want to know more about Microsoft Windows Server Lab Environment, click here . Check the I agree to use this app responsibly box and click the Launch App button to access your cloud workspace.
2026-06-27T08:05:06.132Z — subtitle_track — Threats and Breaches | Coursera — 7912 chars
Willkommen bei „Bedrohungen und Sicherheitslücken“. Nachdem Sie sich dieses Video angesehen haben, können Sie die verschiedenen Arten von Sicherheitsbedrohungen identifizieren, Beispiele für Sicherheitsbedrohungen auflisten und den Unterschied zwischen einem Wurm und einem Trojaner erklären. Schwache Sicherheitsrichtlinien können zu physischen Bedrohungen, Manipulationen oder zum Diebstahl von Hardware führen. Nur vertrauenswürdiges, autorisiertes Personal sollte physischen Zugang zu Informationssystemen haben, und zwar nur für die spezifischen Systeme, für die es verantwortlich ist. Es ist viel einfacher, Daten direkt von einem Laptop oder Server zu stehlen, als sich remote in ein komplexes Netzwerk zu hacken. Um die Hardware vor physischen Bedrohungen, Manipulationen und Diebstahl zu schützen, sperren Sie sie in einem sicheren Bereich mit Kartenlesern an den Türen ab, um den Zugang zu beschränken. Verwenden Sie eine robuste Überwachung innerhalb und außerhalb der Räumlichkeiten und sorgen Sie dafür, dass diese gewartet , aktualisiert und getestet werden. Bei Stromausfällen, Bränden und Naturkatastrophen wie Erdbeben, Überschwemmungen, Tornados, Wirbelstürmen und elektrischen Stürmen kann es zu Hardwareausfällen oder -zerstörungen kommen. Umweltbedingungen wie Feuchtigkeit und Schimmel bergen ebenfalls Risiken. Schützen Sie Ihre Geräte mit einer gut gewarteten Infrastruktur, die Brandbekämpfungssysteme, Notstromversorgung und ein ordnungsgemäß funktionierendes HLK-System umfasst, um Feuchtigkeit und Schimmel zu vermeiden. Letztlich funktioniert keine dieser Strategien ohne einen detaillierten Plan, was zu tun ist, wenn eine Katastrophe eintritt oder ein System beschädigt wird. Regelmäßige Planung, Wartung und Backups sowie Durchläufe simulierter Katastrophen- und Angriffsszenarien helfen dabei, den Prozess zu verfeinern und Sicherheitslücken zu identifizieren. Ungepatchte Systeme, falsch konfigurierte Firewalls, schwache Cybersicherheit und schwache physische Sicherheit sind nur einige der Arten, auf denen Datenbedrohungen auftreten. Datenlecks sind die versehentliche Offenlegung vertraulicher oder sensibler Daten durch eine Sicherheitslücke. Datenlecks liegen vor, wenn ein Datenleck vorsätzlich von Cyberkriminellen verursacht wird. Diese treten auf, wenn Social Engineering- oder Phishing-Angriffe Mitarbeiter dazu verleiten, vertrauliche Anmeldeinformationen oder Informationen preiszugeben. Bei Datendumps werfen Cyberkriminelle gestohlene Daten ins Dark Web, um Geld zu verdienen. Ein Datendump kann PII, PHI, Bankkontonummern, PINs, Sozialversicherungsnummern und mehr enthalten. Andere Cyberkriminelle kaufen und verwenden Datendumps für Dinge wie Identitätsdiebstahl und Passwortangriffe. Beim Müllcontainertauchen durchsucht man buchstäblich einen Müllcontainer, um etwas Wertvolles zu finden. Der Papierkorb eines Unternehmens kann Listen mit Kundennamen, Telefonnummern, Kontaktinformationen, Geschäftsplänen, Produktdesigns oder einen Zugangscode auf einem Haftnotiz enthalten. Technologieunternehmen verlangen das Schreddern von Dokumenten und die Zerstörung von Geräten als normalen Geschäftsablauf, da diese aus dem Papierkorb gestohlen werden können, um Daten zu sammeln, die für Identitätsdiebstahl und Datenschutzverletzungen verwendet werden können. Oder die Daten könnten an Hacker oder Wettbewerber eines Unternehmens verkauft werden. Insider-Bedrohungen sind Bedrohungen, die von innerhalb einer Organisation ausgehen. Ein Mitarbeiter ist eine Insider-Bedrohung, wenn er wertvolle oder vertrauliche Informationen verkauft, das Unternehmen schädigt oder in Verlegenheit bringt oder versehentlich wertvolle Zugangsdaten oder Daten preisgibt. Insider-Bedrohungen können auch von Hackern verursacht werden, die Insider rekrutieren, indem sie Mitarbeiter erpressen oder Geld oder eine andere Belohnung versprechen. Zu den Softwarebedrohungen gehören Diebstahl, Exploits und Malware. Software- oder Lizenzdiebstahl ist das unbefugte Kopieren oder Verwenden urheberrechtlich geschützter Software. Dazu gehören Raubkopien von Software und gefälschte Aktivierungscodes. Exploits sind Codeteile, die Sicherheitslücken in Hardware oder Software nutzen, um in ein System einzudringen. Mit Malware infizierte Websites verwenden Exploits, um automatisch Malware auf ein System herunterzuladen. Dies wird als Drive-by-Download bezeichnet. Malware ist ein allgemeiner Begriff für Software, die Computersysteme gefährden soll. Malware kann zu Systemverlangsamungen, seltsamen Anfragen, Fehlleitungen des Browsers und Popup-Werbung führen. Es kann auch Daten stehlen, alles aufzeichnen, was Sie mit oder in der Nähe Ihres Geräts tun, Ihre Kontakte mit infizierten Links spammen und Ihren Computer mit einem Netzwerk entführter Computer verbinden, die ferngesteuert werden (bekannt als Botnetz). Malware kann von Anhängen, fragwürdigen Websites, Dateidownloads, infizierten USB-Laufwerken oder Links in E-Mails, Anzeigen, sozialen Medien, Torrents und sogar Textnachrichten stammen. Phishing- und Remote Desktop Protocol-Angriffe (oder RDP-Angriffe) sind die beliebtesten Angriffsvektoren für Ransomware, da sie zu einer höheren Erfolgsquote führen. Um Malware zu vermeiden, halten Sie die Software auf dem neuesten Stand, öffnen Sie keine seltsamen Anhänge oder Links, sichern Sie Ihre Daten, verwenden Sie starke Antivirensoftware und verwenden Sie starke, häufig aktualisierte Passwörter. Zu den Malware-Typen gehören Viren, Würmer, Trojaner, Exploits, Spyware, Adware und Ransomware. Computerviren sind Programme, die darauf ausgelegt sind, sich von Host zu Host zu verbreiten, genau wie echte Viren. Eine infizierte App oder Datei muss von einem Benutzer gestartet werden, damit ein Virus aktiviert werden kann. Viren können eine Webcam einschalten, Tastatureingaben und Seitenbesuche aufzeichnen, Daten stehlen, Dateien beschädigen und E-Mail-Konten kapern. Schauen wir uns einige verschiedene Typen an: Programmviren sind Codeteile, die sich selbst in ein anderes Programm einfügen. Makroviren wirken sich über die Makros aus, die sie zur Automatisierung von Aufgaben verwenden, auf Microsoft Office-Dateien. Stealth-Viren kopieren sich an verschiedene Orte, um Antiviren-Scans zu vermeiden. Polymorphe Viren ändern ihre Eigenschaften, um die Cybersicherheitsabwehr zu umgehen. Und 97% aller Schadprogramme verwenden polymorphe Viren. Würmer sind Viren, die sich von selbst entwickeln, nachdem sie Systemschwächen identifiziert haben. Sie sind nicht auf Apps oder Dateien angewiesen. Im Gegensatz zu Viren können Würmer ferngesteuert werden. Trojaner verleiten Sie dazu, legitim anmutende Software zu installieren, die schädliche Malware enthält. Spyware sammelt persönliche Daten, Anmeldeinformationen, Kreditkarteninformationen und Online-Aktivitäten und kann mithilfe der Kamera oder des Mikrofons eines Geräts aufzeichnen. Adware ist in Online-Anzeigen codierte Software, die Ihre persönlichen Daten, Webseitenbesuche und Tastatureingaben aufzeichnet, um Ihnen personalisierte Werbung zu senden. Sowohl Adware als auch Spyware können legitim oder bösartig sein. Und schließlich sperrt Ransomware ein System, verschlüsselt seine Dateien und zeigt eine Lösegeldforderung an. Um den Verschlüsselungscode zu erhalten, müssen Sie das Lösegeld zahlen. Oder Sie können den Zugriff wiedererlangen, indem Sie eine vollständige Systemwiederherstellung aus einem Backup durchführen. In diesem Video haben Sie gelernt, dass Hardware vor physischer Beschädigung, Manipulation und Diebstahl geschützt werden muss. Hardware ist anfällig für Naturkatastrophen, Brände, Schimmel und Stromausfälle. Insider-Bedrohungen können ein Mitarbeiter oder ein Hacker sein, der nach einem Insider sucht. Zu den Softwarebedrohungen gehören Diebstahl, Exploits und Malware. Zu Malware gehören Viren, Spyware, Adware und Ransomware, und 97% aller Schadprogramme enthalten polymorphe Viren.
2026-06-27T06:57:08.346Z — subtitle_track — Security and Information Privacy | Coursera — 8299 chars
Willkommen bei „Sicherheit und Datenschutz“. Nachdem Sie sich dieses Video angesehen haben, werden Sie in der Lage sein, geistiges Eigentum zu definieren, zu erklären, wie Daten in Informationen umgewandelt werden können, und die verschiedenen Arten vertraulicher Informationen aufzulisten. Ein Informationsgut sind Informationen oder Daten, die von Wert sind. Beispiele hierfür sind Patientenakten, Kundeninformationen und geistiges Eigentum. Informationsbestände können physisch, auf Papier , Festplatten oder anderen Medien oder elektronisch in Datenbanken und Dateien vorhanden sein. Bei der Datenanalyse werden Rohdaten wie Werte oder Fakten verwendet, um aussagekräftige Informationen zu erstellen. Daten sind Rohwerte und Fakten, die normalerweise von automatisierten Systemen gesammelt werden. Zum Beispiel Seitenbesuche, Linkklicks, monatliche Verkäufe. Informationen sind eine Zusammenfassung der Rohdaten. Zum Beispiel positive oder negative Ergebnisse, die nach einer bestimmten Änderung auftreten. Erkenntnisse sind Schlussfolgerungen, die auf den Ergebnissen der Informationsanalyse basieren. Aussagekräftige Geschäftsentscheidungen basieren auf Erkenntnissen. Wenn sich beispielsweise nach der Änderung der Ladenöffnungszeiten ein positiver Trend einstellt, wäre es die richtige Geschäftsentscheidung, diese neuen Öffnungszeiten beizubehalten. Geistiges Eigentum (oder IP) bezieht sich auf geistige Schöpfungen und ist im Allgemeinen nicht greifbar. Es ist oft urheberrechtlich, markenrechtlich und patentrechtlich geschützt. Industriedesigns , Geschäftsgeheimnisse und Forschungsentdeckungen sind Beispiele für geistiges Eigentum. Sogar ein Teil des Wissens der Mitarbeiter gilt als geistiges Eigentum. Unternehmen verwenden ein rechtsverbindliches Dokument, eine sogenannte Geheimhaltungsvereinbarung (oder NDA), um die Weitergabe vertraulicher Informationen zu verhindern. Digitale Produkte sind immaterielle Vermögenswerte, die ein Unternehmen besitzt. Beispiele hierfür sind Software, Online-Musik, Online-Kurse, E-Books oder Hörbücher sowie Webelemente wie WordPress- oder Shopify-Themen. Ein Unternehmen muss digitale Produkte vor Piraterie und Reverse-Engineering schützen. Quellcodes, Lizenzen und Aktivierungsschlüssel müssen auch vor Hackern und Insiderbedrohungen geschützt werden. Bei Digital Rights Management (DRM) handelt es sich um Code, der direkt zu Dateien hinzugefügt wird und so verhindert, dass digitale Inhalte kopiert oder raubkopiert werden. Es gibt jedoch Tools, mit denen DRM-Code entfernt werden kann. Der Digital Millennium Copyright Act (DMCA) macht es illegal, Kopierschutzmaßnahmen zu umgehen oder Technologien zu entwickeln, die helfen, Kopierschutzmaßnahmen zu umgehen. Datengestützte Geschäftsentscheidungen helfen Unternehmen, auf reale Ereignisse zu reagieren. Vertriebs- und Marketingdaten helfen beispielsweise dabei, Trends und Kundeninteressen zu erkennen. Und Produktions- und Fulfillment-Daten helfen dabei, Produktivitätsprobleme in Bereichen wie Fertigung, Abrechnungssystemen, Transport und mehr zu identifizieren. Die richtigen Informationen zu erhalten, ist der Schlüssel zu datengestützten Geschäftsentscheidungen. Datenerfassung ist das Sammeln von Daten aus mehreren Quellen und deren sichere Speicherung in relationalen Datenbanken oder, häufiger, halbstrukturierten Data Warehouses. Daten können erfasst werden durch: Serverprotokolle, aus denen hervorgeht, wo Kunden surfen, IoT-Sensoren in Haushaltsgeräten und Geschäftstechnologie, Kunden- und Mitarbeiterbefragungen oder Bewertungssysteme. Bei der Datenkorrelation werden Rohdatenpunkte analysiert, um Verbindungen oder Verknüpfungen zu finden. Netflix verwendet beispielsweise Tools, die Suchanfragen, Aufrufe und Bewertungen vergleichen, um vorhersagen zu können, welche Filme und Serien auf seiner Plattform erfolgreich sein werden. KI- und maschinelle Lernalgorithmen automatisieren Teile der Analyse. Aussagekräftige Berichterstattung ist die Präsentation analysierter Informationen auf eine Weise, die den Benutzern hilft, sie weiter zu analysieren und zu interpretieren. Berichtstools verwenden erfasste und korrelierte Daten, um Diagramme, Stichwortsuche und Grafiken bereitzustellen, mit denen Unternehmen Geschäftseinblicke gewinnen können. Vertrauliche Informationen sind Informationen, die geheim gehalten werden müssen. Die Mitarbeiter werden darin geschult, vertrauliche Informationen zu erkennen und damit umzugehen, damit sie sicher bleiben. Unternehmen ordnen Informationen und Dateien danach, wie sensibel sie jeweils sind. Jedes Unternehmen ordnet seine Informationen unterschiedlich ein, aber es gibt vier Haupttypen vertraulicher Informationen, die allgemein geschützt werden sollten: Personenbezogene Daten (PII) sind alle Informationen, die zur Identifizierung einer Person verwendet werden können, z. B. behördliche Ausweisnummern, Geburtsdaten, Adressen und Telefonnummern. Vertrauliche Unternehmensinformationen sind alle Informationen, die zur Führung eines Unternehmens verwendet werden, z. B. geistiges Eigentum, Produktdesigns, Verfahren, Pläne, Mitarbeiterdaten und Finanzdaten. Vertrauliche Kundeninformationen sind Informationen, die Kunden oder Partner Unternehmen zur Verfügung stellen, darunter PII und auch Dinge wie Kaufhistorien, Kreditkarteninformationen. Geschützte Gesundheitsinformationen (PHI) sind alle Informationen, die während der Diagnose oder Behandlung zur Krankenakte einer Person hinzugefügt werden und zu deren Identifizierung verwendet werden können, wie PII, Krankengeschichte, Verschreibungslisten, Fotos und mehr. Beispiele für unachtsame Datenübergabe sind Dinge wie die Eingabe der Kreditkarteninformationen eines Kunden in eine unverschlüsselte Datenbank, das unbeaufsichtigte Verlassen der Krankenakte eines Patienten an der Rezeption oder die Tatsache, dass ein Arbeitsfreund sich Ihr Passwort ausleiht, um Dateien herunterzuladen, weil er sein Passwort vergessen hat. Der richtige Umgang mit Vertraulichkeit bedeutet, den Zugriff auf diejenigen zu beschränken, die die Informationen benötigen, nicht autorisierte Zugriffe oder Kopien zuzulassen, Informationen sicher mit Verschlüsselung, Firewalls, Berechtigungen und mehr zu speichern, alle nicht mehr benötigten Dateikopien zu vernichten — sie nicht einfach zu verwerfen, vor der Verarbeitung oder Speicherung von Informationen eine ausdrückliche Zustimmung einzuholen, einschließlich einer Offenlegung darüber, wie lange sie aufbewahrt werden, sicherzustellen, dass Mitarbeiter sichere Passwörter erstellen, die sie nicht aufschreiben oder weitergeben, und dass sie diese Passwörter ändern regelmäßig (z. B. einmal pro Quartal) werden PII manchmal mit PCI und SPI verwechselt. Hier sind die Unterschiede zwischen ihnen. Persönlich identifizierbare Informationen (PII) sind Informationen, die eine Person identifizieren. Persönliche Kundeninformationen (PCI) sind Informationen, die einen Kunden identifizieren und beschreiben. Sie enthalten viele der gleichen Datentypen wie PII. Wie Name, Adresse, Kontaktinformationen, Kontoanmeldung und demografische Daten. Es kann auch beschreibende Daten wie Alter, Geschlecht, Berufsbezeichnung und Familienstand enthalten. Sensible personenbezogene Daten (SPI) sind Informationen, die zwar nicht identifizierbar sind, aber Schaden anrichten können, wenn sie veröffentlicht werden. Wie Sie sehen, sind sich PII, PCI und SPI sehr ähnlich. In den meisten Fällen, in denen es sich nicht um Rechtsfragen handelt, werden diese Begriffe häufig synonym verwendet. In diesem Video haben Sie gelernt, dass Rohdaten, wenn sie verfeinert werden, zu Informationen werden. Geistiges Eigentum (oder IP) umfasst Designs, Geschäftsgeheimnisse, Forschungsergebnisse und sogar das Wissen von Mitarbeitern. Digitale Produkte sind immaterielle Vermögenswerte, die einem Unternehmen gehören, wie Software, E-Books oder Webelemente. Datengestützte Geschäftsentscheidungen basieren auf der Erfassung von Daten, deren Korrelation und deren anschließender Verwendung zur Erstellung aussagekräftiger Berichte. Zu den Datenklassen, die Unternehmen und Organisationen schützen müssen, gehören personenbezogene Daten (oder PII), vertrauliche Unternehmensinformationen, vertrauliche Kundeninformationen und geschützte Gesundheitsinformationen (PHI).
2026-06-18T13:13:34.648Z — transcript_dom — Security and Information Privacy | Coursera — 1762 chars
Information is a summary of the raw data. For example, positive or negative results that happen after some specific change. And, insights are conclusions based on the results of information analysis. Meaningful business decisions are based on insights. For example, If a positive trend occurs after store hours are changed, the right business decision would be to maintain those new hours. Intellectual property (or IP) refers to creations of the mind and generally are not tangible. It's often protected by copyright, trademark, and patent law. Industrial designs, trade secrets, and research discoveries are all examples of IP. Even some employee knowledge is considered intellectual property. Companies use a legally binding document called a Non-Disclosure Agreement (or an NDA) to prevent the sharing of sensitive information. Digital products are non-tangible assets a company owns. Examples include software, online music, online courses, e-Books or audiobooks, and web elements like WordPress or Shopify themes. A company must protect digital products from piracy and reverse-engineering. Source codes, licenses, and activation keys also need protection from hackers and insider threats. Here are the differences between them. Personally Identifiable Information (PII) is information that identifies a person. Personal Customer Information (PCI) is information that identifies and describes a customer. It includes much of the same types of data as PII. Like name, address, contact information, account login, and demographics. It can also include descriptive data like age, gender, job title, and marital status. Sensitive Personal Information (SPI) is information that does not identify but can cause harm if made public.
2026-06-18T13:13:26.137Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T13:13:02.921Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T13:13:01.259Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T13:12:59.931Z — reading_dom — Introduction to Cybersecurity Essentials - Home - Week week | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T13:12:54.997Z — transcript_dom — Course Introduction | Coursera — 232 chars
And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and we’re excited that you’re here. Let’s get started! :
2026-06-18T13:12:31.428Z — transcript_dom — Course Introduction | Coursera — 230 chars
And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and we’re excited that you’re here. Let’s get started!
2026-06-18T13:12:22.069Z — transcript_dom — Security and Information Privacy | Coursera — 1762 chars
Information is a summary of the raw data. For example, positive or negative results that happen after some specific change. And, insights are conclusions based on the results of information analysis. Meaningful business decisions are based on insights. For example, If a positive trend occurs after store hours are changed, the right business decision would be to maintain those new hours. Intellectual property (or IP) refers to creations of the mind and generally are not tangible. It's often protected by copyright, trademark, and patent law. Industrial designs, trade secrets, and research discoveries are all examples of IP. Even some employee knowledge is considered intellectual property. Companies use a legally binding document called a Non-Disclosure Agreement (or an NDA) to prevent the sharing of sensitive information. Digital products are non-tangible assets a company owns. Examples include software, online music, online courses, e-Books or audiobooks, and web elements like WordPress or Shopify themes. A company must protect digital products from piracy and reverse-engineering. Source codes, licenses, and activation keys also need protection from hackers and insider threats. Here are the differences between them. Personally Identifiable Information (PII) is information that identifies a person. Personal Customer Information (PCI) is information that identifies and describes a customer. It includes much of the same types of data as PII. Like name, address, contact information, account login, and demographics. It can also include descriptive data like age, gender, job title, and marital status. Sensitive Personal Information (SPI) is information that does not identify but can cause harm if made public.
2026-06-18T12:49:10.543Z — transcript_dom — Security and Information Privacy | Coursera — 1762 chars
Information is a summary of the raw data. For example, positive or negative results that happen after some specific change. And, insights are conclusions based on the results of information analysis. Meaningful business decisions are based on insights. For example, If a positive trend occurs after store hours are changed, the right business decision would be to maintain those new hours. Intellectual property (or IP) refers to creations of the mind and generally are not tangible. It's often protected by copyright, trademark, and patent law. Industrial designs, trade secrets, and research discoveries are all examples of IP. Even some employee knowledge is considered intellectual property. Companies use a legally binding document called a Non-Disclosure Agreement (or an NDA) to prevent the sharing of sensitive information. Digital products are non-tangible assets a company owns. Examples include software, online music, online courses, e-Books or audiobooks, and web elements like WordPress or Shopify themes. A company must protect digital products from piracy and reverse-engineering. Source codes, licenses, and activation keys also need protection from hackers and insider threats. Here are the differences between them. Personally Identifiable Information (PII) is information that identifies a person. Personal Customer Information (PCI) is information that identifies and describes a customer. It includes much of the same types of data as PII. Like name, address, contact information, account login, and demographics. It can also include descriptive data like age, gender, job title, and marital status. Sensitive Personal Information (SPI) is information that does not identify but can cause harm if made public.
2026-06-18T12:48:59.452Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T12:48:35.655Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T12:48:35.260Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T12:48:33.251Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T12:48:26.986Z — transcript_dom — Course Introduction | Coursera — 232 chars
And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and we’re excited that you’re here. Let’s get started! :
2026-06-18T12:48:01.268Z — transcript_dom — Course Introduction | Coursera — 232 chars
And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and we’re excited that you’re here. Let’s get started! :
2026-06-18T12:47:51.580Z — transcript_dom — Security and Information Privacy | Coursera — 1762 chars
Information is a summary of the raw data. For example, positive or negative results that happen after some specific change. And, insights are conclusions based on the results of information analysis. Meaningful business decisions are based on insights. For example, If a positive trend occurs after store hours are changed, the right business decision would be to maintain those new hours. Intellectual property (or IP) refers to creations of the mind and generally are not tangible. It's often protected by copyright, trademark, and patent law. Industrial designs, trade secrets, and research discoveries are all examples of IP. Even some employee knowledge is considered intellectual property. Companies use a legally binding document called a Non-Disclosure Agreement (or an NDA) to prevent the sharing of sensitive information. Digital products are non-tangible assets a company owns. Examples include software, online music, online courses, e-Books or audiobooks, and web elements like WordPress or Shopify themes. A company must protect digital products from piracy and reverse-engineering. Source codes, licenses, and activation keys also need protection from hackers and insider threats. Here are the differences between them. Personally Identifiable Information (PII) is information that identifies a person. Personal Customer Information (PCI) is information that identifies and describes a customer. It includes much of the same types of data as PII. Like name, address, contact information, account login, and demographics. It can also include descriptive data like age, gender, job title, and marital status. Sensitive Personal Information (SPI) is information that does not identify but can cause harm if made public.
2026-06-18T12:41:49.270Z — transcript_dom — Security and Information Privacy | Coursera — 1762 chars
Information is a summary of the raw data. For example, positive or negative results that happen after some specific change. And, insights are conclusions based on the results of information analysis. Meaningful business decisions are based on insights. For example, If a positive trend occurs after store hours are changed, the right business decision would be to maintain those new hours. Intellectual property (or IP) refers to creations of the mind and generally are not tangible. It's often protected by copyright, trademark, and patent law. Industrial designs, trade secrets, and research discoveries are all examples of IP. Even some employee knowledge is considered intellectual property. Companies use a legally binding document called a Non-Disclosure Agreement (or an NDA) to prevent the sharing of sensitive information. Digital products are non-tangible assets a company owns. Examples include software, online music, online courses, e-Books or audiobooks, and web elements like WordPress or Shopify themes. A company must protect digital products from piracy and reverse-engineering. Source codes, licenses, and activation keys also need protection from hackers and insider threats. Here are the differences between them. Personally Identifiable Information (PII) is information that identifies a person. Personal Customer Information (PCI) is information that identifies and describes a customer. It includes much of the same types of data as PII. Like name, address, contact information, account login, and demographics. It can also include descriptive data like age, gender, job title, and marital status. Sensitive Personal Information (SPI) is information that does not identify but can cause harm if made public.
2026-06-18T12:41:39.654Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T12:41:15.883Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T12:41:15.262Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T12:41:13.511Z — reading_dom — Introduction to Cybersecurity Essentials - Home - Week week | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T12:41:07.231Z — transcript_dom — Course Introduction | Coursera — 232 chars
And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and we’re excited that you’re here. Let’s get started! :
2026-06-18T12:40:41.742Z — transcript_dom — Course Introduction | Coursera — 232 chars
And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and we’re excited that you’re here. Let’s get started! :
2026-06-18T12:40:32.481Z — transcript_dom — Security and Information Privacy | Coursera — 1762 chars
Information is a summary of the raw data. For example, positive or negative results that happen after some specific change. And, insights are conclusions based on the results of information analysis. Meaningful business decisions are based on insights. For example, If a positive trend occurs after store hours are changed, the right business decision would be to maintain those new hours. Intellectual property (or IP) refers to creations of the mind and generally are not tangible. It's often protected by copyright, trademark, and patent law. Industrial designs, trade secrets, and research discoveries are all examples of IP. Even some employee knowledge is considered intellectual property. Companies use a legally binding document called a Non-Disclosure Agreement (or an NDA) to prevent the sharing of sensitive information. Digital products are non-tangible assets a company owns. Examples include software, online music, online courses, e-Books or audiobooks, and web elements like WordPress or Shopify themes. A company must protect digital products from piracy and reverse-engineering. Source codes, licenses, and activation keys also need protection from hackers and insider threats. Here are the differences between them. Personally Identifiable Information (PII) is information that identifies a person. Personal Customer Information (PCI) is information that identifies and describes a customer. It includes much of the same types of data as PII. Like name, address, contact information, account login, and demographics. It can also include descriptive data like age, gender, job title, and marital status. Sensitive Personal Information (SPI) is information that does not identify but can cause harm if made public.
2026-06-18T12:39:02.167Z — transcript_dom — Security and Information Privacy | Coursera — 1762 chars
Information is a summary of the raw data. For example, positive or negative results that happen after some specific change. And, insights are conclusions based on the results of information analysis. Meaningful business decisions are based on insights. For example, If a positive trend occurs after store hours are changed, the right business decision would be to maintain those new hours. Intellectual property (or IP) refers to creations of the mind and generally are not tangible. It's often protected by copyright, trademark, and patent law. Industrial designs, trade secrets, and research discoveries are all examples of IP. Even some employee knowledge is considered intellectual property. Companies use a legally binding document called a Non-Disclosure Agreement (or an NDA) to prevent the sharing of sensitive information. Digital products are non-tangible assets a company owns. Examples include software, online music, online courses, e-Books or audiobooks, and web elements like WordPress or Shopify themes. A company must protect digital products from piracy and reverse-engineering. Source codes, licenses, and activation keys also need protection from hackers and insider threats. Here are the differences between them. Personally Identifiable Information (PII) is information that identifies a person. Personal Customer Information (PCI) is information that identifies and describes a customer. It includes much of the same types of data as PII. Like name, address, contact information, account login, and demographics. It can also include descriptive data like age, gender, job title, and marital status. Sensitive Personal Information (SPI) is information that does not identify but can cause harm if made public.
2026-06-18T12:39:01.293Z — transcript_dom — Security and Information Privacy | Coursera — 1762 chars
Information is a summary of the raw data. For example, positive or negative results that happen after some specific change. And, insights are conclusions based on the results of information analysis. Meaningful business decisions are based on insights. For example, If a positive trend occurs after store hours are changed, the right business decision would be to maintain those new hours. Intellectual property (or IP) refers to creations of the mind and generally are not tangible. It's often protected by copyright, trademark, and patent law. Industrial designs, trade secrets, and research discoveries are all examples of IP. Even some employee knowledge is considered intellectual property. Companies use a legally binding document called a Non-Disclosure Agreement (or an NDA) to prevent the sharing of sensitive information. Digital products are non-tangible assets a company owns. Examples include software, online music, online courses, e-Books or audiobooks, and web elements like WordPress or Shopify themes. A company must protect digital products from piracy and reverse-engineering. Source codes, licenses, and activation keys also need protection from hackers and insider threats. Here are the differences between them. Personally Identifiable Information (PII) is information that identifies a person. Personal Customer Information (PCI) is information that identifies and describes a customer. It includes much of the same types of data as PII. Like name, address, contact information, account login, and demographics. It can also include descriptive data like age, gender, job title, and marital status. Sensitive Personal Information (SPI) is information that does not identify but can cause harm if made public.
2026-06-18T12:38:58.679Z — transcript_dom — Introduction to Cybersecurity Essentials - Home - Week week | Coursera — 1377 chars
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2026-06-18T12:38:55.591Z — transcript_dom — Course Introduction | Coursera — 232 chars
And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and we’re excited that you’re here. Let’s get started! :
2026-06-18T12:38:33.251Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T12:38:32.398Z — reading_dom — Course Overview | Coursera — 4430 chars
Course Overview Welcome to the Cybersecurity Essentials for Everyone course. This course has been thoughtfully designed to introduce you to the core principles and practices of cybersecurity—skills that are increasingly vital in today’s digital world. Whether you're an aspiring IT support professional, a beginner exploring career opportunities in cybersecurity, or simply someone looking to understand how to stay safe online, this course will provide you with a solid foundation. Throughout the course, you'll gain practical knowledge of key cybersecurity concepts, tools, and techniques used to protect data, systems, and networks from common threats. You'll explore a range of cyber threats, including malware, ransomware, phishing, and data breaches, and learn how to counter them with security strategies such as password management, device hardening, and safe browsing practices. Additionally, you’ll develop the ability to troubleshoot common security issues in a Windows Server environment. To support your learning, each module includes hands-on labs that simulate real-world scenarios. These culminate in a final practical project that allows you to demonstrate your newly acquired skills in a tangible way. No prior experience in cybersecurity is needed. This course is designed for beginners and serves as an excellent starting point for anyone looking to pursue further study or a career in the field. After completing this course, you will be able to: Articulate the fundamentals of cybersecurity and explain why they matter in today’s digital landscape. Identify and describe common cyber threats such as malware, phishing, and ransomware, along with strategies used to mitigate them. Implement key security best practices, including strong password management, software updates, and data encryption. Apply safe browsing techniques and configure browser settings to enhance online safety. Diagnose and troubleshoot security issues in a Windows Server environment. Analyze real-world data breach incidents to understand their causes and consequences. This course is structured into four comprehensive modules. To maximize your learning experience, we recommend setting aside regular, dedicated time for each module’s videos, readings, and hands-on activities. Begin your cybersecurity journey by understanding the foundational concepts. This module covers the distinctions between data, information, and insights and introduces prevalent security threats such as ransomware, malware, data theft, and unauthorized access. You’ll also explore the concept of layered defense strategies and how to identify attack vectors to maintain data integrity and confidentiality. In this module, you'll delve into essential security practices at both individual and organizational levels. Topics include creating and managing secure passwords, using password managers, and understanding the AAA framework (Authentication, Authorization, and Accounting). You'll also learn about securing devices, avoiding malicious applications, applying encryption techniques, and managing email safely. Here, the focus shifts to web safety. You'll learn how to reduce risks while browsing the internet by recognizing threats in public browsing environments, social media platforms, and insecure applications. The module also covers browser configuration techniques, including managing cookies, plug-ins, cache, and extensions, along with strategies for identifying trustworthy websites and avoiding scams. Module 4: Final Module – Case Studies and Hands-On Practice The final module brings everything together. You’ll analyze high-profile data breach case studies to understand their underlying causes. A comprehensive quiz will help reinforce your learning. You will also complete three hands-on IT support tasks—troubleshooting Windows updates, clearing browser cache, and configuring the Windows Defender Firewall. Screenshots of your completed tasks will be submitted for AI-based evaluation. The course is accessible via any modern web browser on desktop, tablet, or smartphone. You’ll work with browser safety tools and Windows Server troubleshooting utilities and submit assignments for AI-assisted evaluation, ensuring a practical, real-world learning experience. Congratulations on taking the first step toward building your cybersecurity expertise. We’re excited to support you on this journey. Let’s get started!
2026-06-18T12:38:13.413Z — transcript_dom — Course Introduction | Coursera — 232 chars
And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and we’re excited that you’re here. Let’s get started! :
2026-06-18T12:37:53.233Z — transcript_dom — Confidentiality, Integrity, and Availability | Coursera — 2072 chars
Welcome to “Confidentiality, Integrity, and Availability” After watching this video, you will be able to explain what the CIA Triad is, list concerns related to the CIA Triad, and define common regulatory standards and penalties. A comprehensive security program must contain confidentiality, integrity, and availability. These are known as the CIA Triad. Confidentiality means that data is protected from unauthorized access. Integrity means that data is protected from unauthorized changes. And, availability means you have access to your data whenever you need it. When confidential data is exposed beyond the intended audience, it causes risk. :54 Confidential information is kept secret to prevent identity theft, compromised accounts and systems, legal concerns, damage to reputation, and other severe consequences. To determine if data should be confidential, ask: Who is authorized? Do confidentiality regulations apply? Are there conditions for when data can be accessed? What would the impact of disclosure be? Is the data valuable? Cybercriminals are always after sensitive information or personal data. To keep confidential data secure, control data access and use security tools like encryption and multifactor authentication (MFA). So, data is one of the most valuable assets a company can have, but it is not static. It can be transferred to other systems, altered, and updated multiple times. Data integrity guarantees that data is accurate, complete, and consistent. It covers data in storage, during processing, and in transit. Without data integrity, loss, corruption, or compromise can cause significant damage and financial loss for both businesses and customers. The two main types of data integrity are physical and logical. To preserve data integrity, security plans must prevent unauthorized access and changes. Regulations like HIPAA and GDPR help to keep data safe, secure, accurate, and private. And, non-compliance or repeated violations of privacy regulations can result in hefty fines and penalties. :
2026-06-18T12:33:56.546Z — transcript_dom — Confidentiality, Integrity, and Availability | Coursera — 2072 chars
Welcome to “Confidentiality, Integrity, and Availability” After watching this video, you will be able to explain what the CIA Triad is, list concerns related to the CIA Triad, and define common regulatory standards and penalties. A comprehensive security program must contain confidentiality, integrity, and availability. These are known as the CIA Triad. Confidentiality means that data is protected from unauthorized access. Integrity means that data is protected from unauthorized changes. And, availability means you have access to your data whenever you need it. When confidential data is exposed beyond the intended audience, it causes risk. :54 Confidential information is kept secret to prevent identity theft, compromised accounts and systems, legal concerns, damage to reputation, and other severe consequences. To determine if data should be confidential, ask: Who is authorized? Do confidentiality regulations apply? Are there conditions for when data can be accessed? What would the impact of disclosure be? Is the data valuable? Cybercriminals are always after sensitive information or personal data. To keep confidential data secure, control data access and use security tools like encryption and multifactor authentication (MFA). So, data is one of the most valuable assets a company can have, but it is not static. It can be transferred to other systems, altered, and updated multiple times. Data integrity guarantees that data is accurate, complete, and consistent. It covers data in storage, during processing, and in transit. Without data integrity, loss, corruption, or compromise can cause significant damage and financial loss for both businesses and customers. The two main types of data integrity are physical and logical. To preserve data integrity, security plans must prevent unauthorized access and changes. Regulations like HIPAA and GDPR help to keep data safe, secure, accurate, and private. And, non-compliance or repeated violations of privacy regulations can result in hefty fines and penalties. :
2026-06-18T12:33:52.171Z — transcript_dom — Confidentiality, Integrity, and Availability | Coursera — 2072 chars
Welcome to “Confidentiality, Integrity, and Availability” After watching this video, you will be able to explain what the CIA Triad is, list concerns related to the CIA Triad, and define common regulatory standards and penalties. A comprehensive security program must contain confidentiality, integrity, and availability. These are known as the CIA Triad. Confidentiality means that data is protected from unauthorized access. Integrity means that data is protected from unauthorized changes. And, availability means you have access to your data whenever you need it. When confidential data is exposed beyond the intended audience, it causes risk. :54 Confidential information is kept secret to prevent identity theft, compromised accounts and systems, legal concerns, damage to reputation, and other severe consequences. To determine if data should be confidential, ask: Who is authorized? Do confidentiality regulations apply? Are there conditions for when data can be accessed? What would the impact of disclosure be? Is the data valuable? Cybercriminals are always after sensitive information or personal data. To keep confidential data secure, control data access and use security tools like encryption and multifactor authentication (MFA). So, data is one of the most valuable assets a company can have, but it is not static. It can be transferred to other systems, altered, and updated multiple times. Data integrity guarantees that data is accurate, complete, and consistent. It covers data in storage, during processing, and in transit. Without data integrity, loss, corruption, or compromise can cause significant damage and financial loss for both businesses and customers. The two main types of data integrity are physical and logical. To preserve data integrity, security plans must prevent unauthorized access and changes. Regulations like HIPAA and GDPR help to keep data safe, secure, accurate, and private. And, non-compliance or repeated violations of privacy regulations can result in hefty fines and penalties. :
2026-06-18T12:31:16.945Z — transcript_dom — Confidentiality, Integrity, and Availability | Coursera — 2072 chars
Welcome to “Confidentiality, Integrity, and Availability” After watching this video, you will be able to explain what the CIA Triad is, list concerns related to the CIA Triad, and define common regulatory standards and penalties. A comprehensive security program must contain confidentiality, integrity, and availability. These are known as the CIA Triad. Confidentiality means that data is protected from unauthorized access. Integrity means that data is protected from unauthorized changes. And, availability means you have access to your data whenever you need it. When confidential data is exposed beyond the intended audience, it causes risk. :54 Confidential information is kept secret to prevent identity theft, compromised accounts and systems, legal concerns, damage to reputation, and other severe consequences. To determine if data should be confidential, ask: Who is authorized? Do confidentiality regulations apply? Are there conditions for when data can be accessed? What would the impact of disclosure be? Is the data valuable? Cybercriminals are always after sensitive information or personal data. To keep confidential data secure, control data access and use security tools like encryption and multifactor authentication (MFA). So, data is one of the most valuable assets a company can have, but it is not static. It can be transferred to other systems, altered, and updated multiple times. Data integrity guarantees that data is accurate, complete, and consistent. It covers data in storage, during processing, and in transit. Without data integrity, loss, corruption, or compromise can cause significant damage and financial loss for both businesses and customers. The two main types of data integrity are physical and logical. To preserve data integrity, security plans must prevent unauthorized access and changes. Regulations like HIPAA and GDPR help to keep data safe, secure, accurate, and private. And, non-compliance or repeated violations of privacy regulations can result in hefty fines and penalties. :
2026-06-18T12:31:12.803Z — transcript_dom — Confidentiality, Integrity, and Availability | Coursera — 2072 chars
Welcome to “Confidentiality, Integrity, and Availability” After watching this video, you will be able to explain what the CIA Triad is, list concerns related to the CIA Triad, and define common regulatory standards and penalties. A comprehensive security program must contain confidentiality, integrity, and availability. These are known as the CIA Triad. Confidentiality means that data is protected from unauthorized access. Integrity means that data is protected from unauthorized changes. And, availability means you have access to your data whenever you need it. When confidential data is exposed beyond the intended audience, it causes risk. :54 Confidential information is kept secret to prevent identity theft, compromised accounts and systems, legal concerns, damage to reputation, and other severe consequences. To determine if data should be confidential, ask: Who is authorized? Do confidentiality regulations apply? Are there conditions for when data can be accessed? What would the impact of disclosure be? Is the data valuable? Cybercriminals are always after sensitive information or personal data. To keep confidential data secure, control data access and use security tools like encryption and multifactor authentication (MFA). So, data is one of the most valuable assets a company can have, but it is not static. It can be transferred to other systems, altered, and updated multiple times. Data integrity guarantees that data is accurate, complete, and consistent. It covers data in storage, during processing, and in transit. Without data integrity, loss, corruption, or compromise can cause significant damage and financial loss for both businesses and customers. The two main types of data integrity are physical and logical. To preserve data integrity, security plans must prevent unauthorized access and changes. Regulations like HIPAA and GDPR help to keep data safe, secure, accurate, and private. And, non-compliance or repeated violations of privacy regulations can result in hefty fines and penalties. :
2026-06-18T12:29:15.354Z — transcript_dom — Course Introduction | Coursera — 232 chars
And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and we’re excited that you’re here. Let’s get started! :
2026-06-18T12:23:15.375Z — transcript_dom — Course Introduction | Coursera — 232 chars
And graded assessments prove what you’ve learned, leading to a shareable badge and certificate that you can show prospective employers. We’re here to support your success, and we’re excited that you’re here. Let’s get started! :
2026-06-18T02:35:32.979Z — transcript_dom — Introduction to LangChain | Coursera — 435 chars
Benefits include modularity, extensibility, decomposition capabilities, and easy integration with vector databases. Several practical applications include deciphering complex legal documents, extracting key statistics from reports, customer support, and automating routine writing tasks. LangChain can be used with other data types by using external libraries and models. : Added to Selection. Press [CTRL + S] to save as a note
2026-06-18T02:24:57.241Z — transcript_dom — Introduction to LangChain | Coursera — 378 chars
Benefits include modularity, extensibility, decomposition capabilities, and easy integration with vector databases. Several practical applications include deciphering complex legal documents, extracting key statistics from reports, customer support, and automating routine writing tasks. LangChain can be used with other data types by using external libraries and models.
2026-06-18T02:20:14.112Z — transcript_dom — Introduction to In-Context Learning | Coursera — 2953 chars
Welcome to Introduction to In-context Learning. After watching this video, you'll be able to describe in-context learning. You will also be able to explain the fundamentals of prompt engineering. In-context learning is a specific method of prompt engineering where demonstrations of the task are provided to the model as a part of the prompt in natural language. However, in-context learning doesn’t require additional training. A new task is learned from a small set of examples presented within the context or prompt at inference time. Let's understand some advantages and disadvantages of in-context learning. :46 It eliminates the need for continual fine-tuning, allowing the model to adapt and learn within its context. Here is an example of a prompt given to GPT 3.5. The wind is This simple prompt leads to a poetic response. Blowing gently through the trees, whispering secrets and stories to anyone who cares to listen. You can see how an open ended prompt can guide the LLM to create imaginative and detailed responses, highlighting its ability to generate creative and engaging content. Let's break down the components that make up a well structured prompt. Instructions tell the LLM what needs to be done. For example, classify the following customer review into neutral, negative, or positive sentiment. This is straightforward and directs the LLMs action. Context helps the LLM understand the scenario or the background in which it operates. Here it's indicated that this review is part of feedback for a recently launched product. This can help the LLM weigh the sentiment analysis in light of the products novelty. Input data is the actual data the LLM will process. In the prompt, it's the customer review. The product arrived late but the quality exceeded my expectations. The LLM uses this data to perform the task specified by the instructions. The output indicator is the part of the prompt where the LLM's response is expected. It's a clear marker that tells the AI where to deliver its analysis. In this example, sentiment indicates waiting for the LLM to append its classification. Let's recap. In this video, you learned that in-context learning is a method of prompt engineering where task demonstrations are provided to the model as a part of the prompt. 5:2 Prompts are inputs given to an LLM to guide it towards performing a specific task. They consist of instructions and context. Prompt engineering is a process where you design and refine the prompts to get relevant and accurate responses from AI. Prompt engineering has several advantages. It boosts the effectiveness and accuracy of LLMs. It ensures relevant responses. It facilitates meeting user expectations. It eliminates the need for continual fine tuning. A prompt consists of four key elements, instructions, context, input data, and output indicator. : Added to Selection. Press [CTRL + S] to save as a note
2026-06-18T02:12:36.154Z — transcript_dom — Introduction to In-Context Learning | Coursera — 145 chars
1.25 Introduction to In-Context Learning Warning: 3 XP • Applied Generative AI Development and Strategy Save note Dive deeper on this topic Files