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

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

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

• 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

• 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

• 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

• 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

• 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

• MCP For Beginners
• AI Agents For Beginners
• Generative AI for Beginners using .NET
• Generative AI for Beginners using JavaScript
• Generative AI for Beginners
• ML for Beginners
• Data Science for Beginners
• AI for Beginners
• Cybersecurity for Beginners
• Web Dev for Beginners
• IoT for Beginners
• XR Development for Beginners
• Mastering GitHub Copilot for AI Paired Programming
• Mastering GitHub Copilot for C#/.NET Developers
• Choose Your Own Copilot Adventure
• RAG Chat App with Azure AI Services