microsoft/generative-ai-for-beginnersPublic

21 Lessons, Get Started Building with Generative AI

AI summary: A comprehensive 21-lesson course teaching the fundamentals of building Generative AI applications.

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Jupyter NotebookMITCreated Jun 19, 2023Last push 1d ago+2.2K stars this week+2.2K this month

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since Oct 22, 2023
050K100KOct 2023Sep 2024Aug 2025Aug 2026
116.9K stars as of Aug 7, 2026, tracked back to Oct 22, 2023. Historical curve reconstructed from public GitHub event archives, calibrated to the current total.

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628 commits in the last yearLessMore

Signals and awards

derived from tracked data
  • Landmark project

    116,911 stars

  • Very active

    628 commits in 52 weeks

  • Community-driven

    ~154 contributors

  • Well documented

    High community health score

  • Permissive license

    MIT

  • Continuous integration

    Automated checks passing

  • Repeat trending

    5 trending appearances

  • Top 10% tracked

    Rank 52 of 1058

What generative-ai-for-beginners does

Generative AI for Beginners is an extensive, open-source curriculum developed by Microsoft to teach the core concepts of building applications with LLMs. The course comprises 21 structured lessons that cover everything from basic prompt engineering to complex RAG (Retrieval-Augmented Generation) architectures. It provides practical, hands-on examples and code snippets to demonstrate how to integrate AI models into real-world software. The repository is heavily localized, offering automated, up-to-date translations in numerous languages via GitHub Actions. It serves as a foundational learning path for developers transitioning into the rapidly evolving field of generative AI.

This course is aimed at software developers, technical students, and IT professionals who are new to generative AI and want to learn how to build practical LLM-powered applications. It is ideal for those seeking a structured, comprehensive learning path.

  • Comprehensive curriculum: Offers 21 detailed lessons covering the full spectrum of generative AI application development.
  • Hands-on examples: Provides practical code samples and exercises to reinforce theoretical concepts.
  • Multi-language support: Features extensive localization, keeping translations up-to-date automatically via GitHub Actions.
  • Broad topic coverage: Explores everything from basic prompt engineering to advanced architectural patterns like RAG.
  • Beginner friendly: Designed specifically for developers with no prior experience in machine learning or AI.

Where teams use it

Self-Guided Learning

Individual developers can follow the curriculum sequentially to build a solid foundation in generative AI concepts.

Corporate Training

Organizations can utilize the materials to upskill their engineering teams on integrating LLMs into corporate applications.

University Curriculum Supplement

Educators can incorporate the lessons and exercises into computer science courses focused on modern AI.

Global Accessibility

Non-English speaking developers can access the advanced technical concepts through the extensive, automated translations.

Getting started: git clone https://github.com/microsoft/generative-ai-for-beginners.git

README

main branch

Generative AI For Beginners

21 Lessons teaching everything you need to know to start building Generative AI applications

GitHub license GitHub contributors GitHub issues GitHub pull-requests PRs Welcome

GitHub watchers GitHub forks GitHub stars

Microsoft Foundry Discord

🌐 Multi-Language Support

Supported via GitHub Action (Automated & Always Up-to-Date)

Arabic | Bengali | Bulgarian | Burmese (Myanmar) | Chinese (Simplified) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Macau) | Chinese (Traditional, Taiwan) | Croatian | Czech | Danish | Dutch | Estonian | Finnish | French | German | Greek | Hebrew | Hindi | Hungarian | Indonesian | Italian | Japanese | Kannada | Khmer | Korean | Lithuanian | Malay | Malayalam | Marathi | Nepali | Nigerian Pidgin | Norwegian | Persian (Farsi) | Polish | Portuguese (Brazil) | Portuguese (Portugal) | Punjabi (Gurmukhi) | Romanian | Russian | Serbian (Cyrillic) | Slovak | Slovenian | Spanish | Swahili | Swedish | Tagalog (Filipino) | Tamil | Telugu | Thai | Turkish | Ukrainian | Urdu | Vietnamese

Prefer to Clone Locally?

This repository includes 50+ language translations which significantly increases the download size. To clone without translations, use sparse checkout:

Bash / macOS / Linux:

git clone --filter=blob:none --sparse https://github.com/microsoft/generative-ai-for-beginners.git
cd generative-ai-for-beginners
git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'

CMD (Windows):

git clone --filter=blob:none --sparse https://github.com/microsoft/generative-ai-for-beginners.git
cd generative-ai-for-beginners
git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"

This gives you everything you need to complete the course with a much faster download.

Generative AI for Beginners (Version 3) - A Course

Learn the fundamentals of building Generative AI applications with our 21-lesson comprehensive course by Microsoft Cloud Advocates.

🌱 Getting Started

This course has 21 lessons. Each lesson covers its own topic so start wherever you like!

Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both Python and TypeScript when possible.

For .NET Developers checkout Generative AI for Beginners (.NET Edition)!

Each lesson also includes a "Keep Learning" section with additional learning tools.

What You Need

To run the code of this course, you can use either:

We have created a Course Setup lesson to help you with setting up your development environment.

Don't forget to star (🌟) this repo to find it easier later.

🧠 Ready to Deploy?

If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both Python and TypeScript.

🗣️ Meet Other Learners, Get Support

Join our official Microsoft Foundry Discord server to meet and network with other learners taking this course and get support.

Ask questions or share product feedback in our Microsoft Foundry Developer Forum on Github.

🚀 Building a Startup?

Visit Microsoft for Startups to find out how to get started building with Azure credits today.

🙏 Want to help?

Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request

📂 Each lesson includes:

  • A short video introduction to the topic
  • A written lesson located in the README
  • Python and TypeScript code samples supporting Azure OpenAI and OpenAI API
  • Links to extra resources to continue your learning

🗃️ Lessons

# Lesson Link Description Video Extra Learning
00 Course Setup Learn: How to Setup Your Development Environment Video Coming Soon Learn More
01 Introduction to Generative AI and LLMs Learn: Understanding what Generative AI is and how Large Language Models (LLMs) work. Video Learn More
02 Exploring and comparing different LLMs Learn: How to select the right model for your use case Video Learn More
03 Using Generative AI Responsibly Learn: How to build Generative AI Applications responsibly Video Learn More
04 Understanding Prompt Engineering Fundamentals Learn: Hands-on Prompt Engineering Best Practices Video Learn More
05 Creating Advanced Prompts Learn: How to apply prompt engineering techniques that improve the outcome of your prompts. Video Learn More
06 Building Text Generation Applications Build: A text generation app using Azure OpenAI / OpenAI API Video Learn More
07 Building Chat Applications Build: Techniques for efficiently building and integrating chat applications. Video Learn More
08 Building Search Apps Vector Databases Build: A search application that uses Embeddings to search for data. Video Learn More
09 Building Image Generation Applications Build: An image generation application Video Learn More
10 Building Low Code AI Applications Build: A Generative AI application using Low Code tools Video Learn More
11 Integrating External Applications with Function Calling Build: What is function calling and its use cases for applications Video Learn More
12 Designing UX for AI Applications Learn: How to apply UX design principles when developing Generative AI Applications Video Learn More
13 Securing Your Generative AI Applications Learn: The threats and risks to AI systems and methods to secure these systems. Video Learn More
14 The Generative AI Application Lifecycle Learn: The tools and metrics to manage the LLM Lifecycle and LLMOps Video Learn More
15 Retrieval Augmented Generation (RAG) and Vector Databases Build: An application using a RAG Framework to retrieve embeddings from a Vector Databases Video Learn More
16 Open Source Models and Hugging Face Build: An application using open source models available on Hugging Face Video Learn More
17 AI Agents Build: An application using an AI Agent Framework Video Learn More
18 Fine-Tuning LLMs Learn: The what, why and how of fine-tuning LLMs Video Learn More
19 Building with SLMs Learn: The benefits of building with Small Language Models Video Coming Soon Learn More
20 Building with Mistral Models Learn: The features and differences of the Mistral Family Models Video Coming Soon Learn More
21 Building with Meta Models Learn: The features and differences of the Meta Family Models Video Coming Soon Learn More

🌟 Special thanks

Special thanks to John Aziz for creating all of the GitHub Actions and workflows

Bernhard Merkle for making key contributions to each lesson to improve the learner and code experience.

🎒 Other Courses

Our team produces other courses! Check out:

LangChain

LangChain4j for Beginners LangChain.js for Beginners LangChain for Beginners

Azure / Edge / MCP / Agents

AZD for Beginners Edge AI for Beginners MCP for Beginners AI Agents for Beginners


Generative AI Series

Generative AI for Beginners Generative AI (.NET) Generative AI (Java) Generative AI (JavaScript)


Core Learning

ML for Beginners Data Science for Beginners AI for Beginners Cybersecurity for Beginners Web Dev for Beginners IoT for Beginners XR Development for Beginners


Copilot Series

Copilot for AI Paired Programming Copilot for C#/.NET Copilot Adventure

Getting Help

If you get stuck or have any questions about building AI apps. Join fellow learners and experienced developers in discussions about MCP. It's a supportive community where questions are welcome and knowledge is shared freely.

Microsoft Foundry Discord

If you have product feedback or errors while building visit:

Microsoft Foundry Developer Forum

View on GitHub

Recent activity

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When work happens

weekday and hour
SunMonTueWedThuFriSat036912151821Sun 0:00 — 8 commitsSun 1:00 — 5 commitsSun 2:00 — 0 commitsSun 3:00 — 2 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 0 commitsSun 7:00 — 3 commitsSun 8:00 — 6 commitsSun 9:00 — 4 commitsSun 10:00 — 5 commitsSun 11:00 — 9 commitsSun 12:00 — 5 commitsSun 13:00 — 2 commitsSun 14:00 — 2 commitsSun 15:00 — 5 commitsSun 16:00 — 8 commitsSun 17:00 — 7 commitsSun 18:00 — 19 commitsSun 19:00 — 11 commitsSun 20:00 — 3 commitsSun 21:00 — 5 commitsSun 22:00 — 8 commitsSun 23:00 — 2 commitsMon 0:00 — 3 commitsMon 1:00 — 3 commitsMon 2:00 — 1 commitsMon 3:00 — 47 commitsMon 4:00 — 8 commitsMon 5:00 — 4 commitsMon 6:00 — 0 commitsMon 7:00 — 2 commitsMon 8:00 — 11 commitsMon 9:00 — 19 commitsMon 10:00 — 14 commitsMon 11:00 — 17 commitsMon 12:00 — 25 commitsMon 13:00 — 18 commitsMon 14:00 — 39 commitsMon 15:00 — 60 commitsMon 16:00 — 37 commitsMon 17:00 — 36 commitsMon 18:00 — 11 commitsMon 19:00 — 3 commitsMon 20:00 — 7 commitsMon 21:00 — 7 commitsMon 22:00 — 8 commitsMon 23:00 — 9 commitsTue 0:00 — 12 commitsTue 1:00 — 5 commitsTue 2:00 — 1 commitsTue 3:00 — 2 commitsTue 4:00 — 2 commitsTue 5:00 — 1 commitsTue 6:00 — 5 commitsTue 7:00 — 2 commitsTue 8:00 — 5 commitsTue 9:00 — 5 commitsTue 10:00 — 9 commitsTue 11:00 — 9 commitsTue 12:00 — 16 commitsTue 13:00 — 5 commitsTue 14:00 — 7 commitsTue 15:00 — 24 commitsTue 16:00 — 15 commitsTue 17:00 — 13 commitsTue 18:00 — 3 commitsTue 19:00 — 22 commitsTue 20:00 — 7 commitsTue 21:00 — 4 commitsTue 22:00 — 5 commitsTue 23:00 — 5 commitsWed 0:00 — 9 commitsWed 1:00 — 3 commitsWed 2:00 — 4 commitsWed 3:00 — 6 commitsWed 4:00 — 0 commitsWed 5:00 — 0 commitsWed 6:00 — 1 commitsWed 7:00 — 4 commitsWed 8:00 — 3 commitsWed 9:00 — 13 commitsWed 10:00 — 5 commitsWed 11:00 — 15 commitsWed 12:00 — 9 commitsWed 13:00 — 32 commitsWed 14:00 — 11 commitsWed 15:00 — 2 commitsWed 16:00 — 8 commitsWed 17:00 — 16 commitsWed 18:00 — 15 commitsWed 19:00 — 13 commitsWed 20:00 — 14 commitsWed 21:00 — 17 commitsWed 22:00 — 12 commitsWed 23:00 — 10 commitsThu 0:00 — 17 commitsThu 1:00 — 4 commitsThu 2:00 — 1 commitsThu 3:00 — 19 commitsThu 4:00 — 7 commitsThu 5:00 — 3 commitsThu 6:00 — 0 commitsThu 7:00 — 4 commitsThu 8:00 — 3 commitsThu 9:00 — 6 commitsThu 10:00 — 10 commitsThu 11:00 — 17 commitsThu 12:00 — 21 commitsThu 13:00 — 7 commitsThu 14:00 — 15 commitsThu 15:00 — 30 commitsThu 16:00 — 20 commitsThu 17:00 — 9 commitsThu 18:00 — 11 commitsThu 19:00 — 4 commitsThu 20:00 — 5 commitsThu 21:00 — 23 commitsThu 22:00 — 10 commitsThu 23:00 — 9 commitsFri 0:00 — 22 commitsFri 1:00 — 5 commitsFri 2:00 — 13 commitsFri 3:00 — 3 commitsFri 4:00 — 3 commitsFri 5:00 — 3 commitsFri 6:00 — 26 commitsFri 7:00 — 21 commitsFri 8:00 — 26 commitsFri 9:00 — 23 commitsFri 10:00 — 21 commitsFri 11:00 — 27 commitsFri 12:00 — 15 commitsFri 13:00 — 15 commitsFri 14:00 — 20 commitsFri 15:00 — 29 commitsFri 16:00 — 17 commitsFri 17:00 — 10 commitsFri 18:00 — 4 commitsFri 19:00 — 9 commitsFri 20:00 — 10 commitsFri 21:00 — 7 commitsFri 22:00 — 33 commitsFri 23:00 — 7 commitsSat 0:00 — 21 commitsSat 1:00 — 21 commitsSat 2:00 — 9 commitsSat 3:00 — 4 commitsSat 4:00 — 8 commitsSat 5:00 — 8 commitsSat 6:00 — 4 commitsSat 7:00 — 9 commitsSat 8:00 — 7 commitsSat 9:00 — 4 commitsSat 10:00 — 3 commitsSat 11:00 — 3 commitsSat 12:00 — 8 commitsSat 13:00 — 2 commitsSat 14:00 — 1 commitsSat 15:00 — 1 commitsSat 16:00 — 8 commitsSat 17:00 — 1 commitsSat 18:00 — 8 commitsSat 19:00 — 1 commitsSat 20:00 — 1 commitsSat 21:00 — 4 commitsSat 22:00 — 6 commitsSat 23:00 — 2 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Aug 6, 2026daily#5+783
Aug 5, 2026daily#5+783
Aug 4, 2026daily#6+775
Aug 3, 2026daily#6+588
Aug 2, 2026daily#4+108
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