Time Commitment: Di complete workshop fit finish online without local setup. E go take 2 minutes to setup di environment, and to explore di samples fit take 1-3 hours depending on how deep you wan explore.

Quick Start

1. Fork dis repository go your GitHub account
2. Click Code → Codespaces tab → ... → New with options...
3. Use di defaults – dis one go select di Development container wey dem create for dis course
4. Click Create codespace
5. Wait ~2 minutes make di environment ready
6. Jump straight go Chapter 2: Provision Azure AI Foundry

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Prefer to Clone Locally?

Dis repository get over 50 language translations wey go make di download size big. To clone without di translations, use sparse checkout:

Bash / macOS / Linux:

CMD (Windows):

Dis one go give you everything wey you need to complete di course with faster download.

• Core Concepts: Understand 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 how e dey help AI agents communication
• Practical Applications: Real-world tins like chatbots and content generation
• → Start Chapter 1

• Azure AI Foundry: Provision model deployments as code with Bicep and Azure Developer CLI (azd)
• Spring Boot + Spring AI: Best practices for enterprise AI application development
• Keyless Authentication: Connect secure with Microsoft Entra ID — no API keys to manage
• Development Tools: Docker containers, VS Code, and GitHub Codespaces configuration
• → Start Chapter 2

• Prompt Engineering: Techniques for best 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 abilities with custom tools and plugins
• → Start Chapter 3

• Pet Story Generator (petstory/): Creative content generation with Azure AI Foundry
• 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

• Azure AI Foundry Content Safety: Test built-in content filtering and safety mechanisms (hard blocks and soft refusals)
• Responsible AI Demo: Hands-on example wey show how modern AI safety systems dey work
• Best Practices: Important rules for ethical AI development and deployment
• → Start Chapter 5

If you get stuck or get any questions about how to build AI apps. Join other learners and people wey sabi well-well for development inside discussion about MCP. Na good community wey dey support, questions dey welcome and knowledge dey share freely.

If you get product feedback or errors wey show when you dey build, visit:

Disclaimer: Dis document don translate wit AI translation service Co-op Translator. Even tho we dey try make am correct, abeg make you know say automated translation fit get errors or mistakes. Di original document for dia own language na im be di correct source. For important info, make person wey sabi human translation do am. We no go responsible for any misunderstanding or wrong understanding wey fit happen because of dis translation.