GitHub - microsoft/Generative-AI-for-beginners-java: Learn Generative AI fundamentals through Java programming.

Time Commitment: The entire workshop can be completed online without local setup. The environment setup takes 2 minutes, with exploring the samples requiring 1-3 hours depending on exploration depth.

Quick Start

Fork this repository to your GitHub account
Click Code → Codespaces tab → ... → New with options...
Use the defaults – this will select the Development container created for this course
Click Create codespace
Wait ~2 minutes for the environment to be ready
Jump straight to The first example

Multi-Language Support

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

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-java.git
cd Generative-AI-for-beginners-java
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-java.git
cd Generative-AI-for-beginners-java
git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"
This gives you everything you need to complete the course with a much faster download.

Course Structure & Learning Path

Chapter 1: Introduction to Generative AI

Core Concepts: Understanding Large Language Models, tokens, embeddings, and AI capabilities
Java AI Ecosystem: Overview of Spring AI and OpenAI SDKs
Model Context Protocol: Introduction to MCP and its role in AI agent communication
Practical Applications: Real-world scenarios including chatbots and content generation
→ Start Chapter 1

Chapter 2: Development Environment Setup

Multi-Provider Configuration: Set up GitHub Models, Azure OpenAI, and OpenAI Java SDK integrations
Spring Boot + Spring AI: Best practices for enterprise AI application development
GitHub Models: Free AI model access for prototyping and learning (no credit card required)
Development Tools: Docker containers, VS Code, and GitHub Codespaces configuration
→ Start Chapter 2

Chapter 3: Core Generative AI Techniques

Prompt Engineering: Techniques for optimal AI model responses
Embeddings & Vector Operations: Implement semantic search and similarity matching
Retrieval-Augmented Generation (RAG): Combine AI with your own data sources
Function Calling: Extend AI capabilities with custom tools and plugins
→ Start Chapter 3

Chapter 4: Practical Applications & Projects

Pet Story Generator (petstory/): Creative content generation with GitHub Models
Foundry Local Demo (foundrylocal/): Local AI model integration with OpenAI Java SDK
MCP Calculator Service (calculator/): Basic Model Context Protocol implementation with Spring AI
→ Start Chapter 4

Chapter 5: Responsible AI Development

GitHub Models Safety: Test built-in content filtering and safety mechanisms (hard blocks and soft refusals)
Responsible AI Demo: Hands-on example showing how modern AI safety systems work in practice
Best Practices: Essential guidelines for ethical AI development and deployment
→ Start Chapter 5

Additional Resources

LangChain

Azure / Edge / MCP / Agents

Generative AI Series

Core Learning

Copilot Series

Getting Help

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