Learn the fundamentals of building Generative AI applications with our 18-lesson comprehensive course by Microsoft Cloud Advocates.
This course has 18 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.
Each lesson also includes a "Keep Learning" section with additional learning tools.
What You Need
- Access to the Azure OpenAI Service OR OpenAI API - Only required to complete coding lessons
- Basic knowledge of Python or TypeScript is helpful - *For absolute beginners check out these Python and TypeScript courses.
- A GitHub account to fork this entire repo to your own GitHub account
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.
If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both Python and TypeScript.
Join our official AI Discord server to meet and network with other learners taking this course and get support.
Sign up for Microsoft for Startups Founders Hub to receive free OpenAI credits and up to $150k towards Azure credits to access OpenAI models through Azure OpenAI Services.
Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request
- 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
| Lesson Link | Description | Additional Learning | |
|---|---|---|---|
| 00 | Course Setup | Learn: How to Setup Your Development Environment | Learn More |
| 01 | Introduction to Generative AI and LLMs | Learn: Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More |
| 02 | Exploring and comparing different LLMs | Learn: How to select the right model for your use case | Learn More |
| 03 | Using Generative AI Responsibly | Learn: How to build Generative AI Applications responsibly | Learn More |
| 04 | Understanding Prompt Engineering Fundamentals | Learn: Hands-on Prompt Engineering Best Practices | Learn More |
| 05 | Creating Advanced Prompts | Learn: How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More |
| 06 | Building Text Generation Applications | Build: A text generation app using Azure OpenAI | Learn More |
| 07 | Building Chat Applications | Build: Techniques for efficiently building and integrating chat applications. | Learn More |
| 08 | Building Search Apps Vector Databases | Build: A search application that uses Embeddings to search for data. | Learn More |
| 09 | Building Image Generation Applications | Build: A image generation application | Learn More |
| 10 | Building Low Code AI Applications | Build: A Generative AI application using Low Code tools | Learn More |
| 11 | Integrating External Applications with Function Calling | Build: What is function calling and its use cases for applications | Learn More |
| 12 | Designing UX for AI Applications | Learn: How to apply UX design principles when developing Generative AI Applications | Learn More |
| 13 | Securing Your Generative AI Applications | Learn: The threats and risks to AI systems and methods to secure these systems. | Learn More |
| 14 | The Generative AI Application Lifecycle | Learn: The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More |
| 15 | Retrieval Augmented Generation (RAG) and Vector Databases | Build: An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More |
| 16 | Open Source Models and Hugging Face | Build: An application using open source models available on Hugging Face | Learn More |
| 17 | AI Agents | Build: An application using an AI Agent Framework | Learn More |
| 18 | Fine-Tuning LLMs | Learn: The what, why and how of fine-tuning LLMs | Learn More |
Special thanks to John Aziz for creating all of the GitHub Actions and workflows
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