Learn to build integrated AI systems through this comprehensive hands-on crash course covering Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP) fundamentals. Master RAG techniques to connect AI models with your own data sources for accurate, context-aware responses, starting with basic keyword search methods like TF-IDF and BM25 before advancing to semantic search using embedding models and vector databases. Explore document chunking strategies, vector similarity calculations, and production-level considerations including caching, monitoring, and microservices architecture on Kubernetes. Discover when to use prompt engineering, fine-tuning, or RAG for different AI applications, and understand the mathematical foundations of embeddings and vector operations. Progress to Model Context Protocol implementation to coordinate communication and actions across multiple software components, learning MCP architecture with clients and servers, JSON-RPC protocol specifications, and the role of AI agents in action-oriented systems. Build custom MCP servers using the Python SDK, develop resources, tools, and prompts, and create MCP clients with proper roots, sampling, and elicitation techniques. Complete eight hands-on labs covering basic search limitations, semantic search implementation, vector database operations, document chunking optimization, end-to-end RAG pipeline construction, AI assistant environment setup, MCP server connections, and custom MCP component development. Gain practical experience with popular tools including Chroma and Pinecone vector databases, various indexing algorithms like HNSW and IVF, and production deployment strategies for sophisticated multi-part AI applications.

Syllabus

- Course Overview: Building Integrated AI Systems
- The Simplest Explanation of RAG
- Real-world Use Case: Internal Policy Chatbot
- Understanding Retrieval, Augmenting, and Generation
- When to use Prompt Engineering, Fine-Tuning, or RAG
- Solving Voice and Style with Fine-Tuning
- Why RAG is Best for Dynamic Factual Information
- Keyword Search Techniques: TF-IDF and BM25
- Hands-on Lab 1: Basic Search and Keyword Limitations
- Introduction to Semantic Search and Meaning
- Embedding Models: Parameter Size and Local vs. API Models
- How Embeddings Convert Text into Mathematical Vectors
- Vector Similarity and the Dot Product
- Hands-on Lab 2: Implementing Semantic Search with Embedding Models
- Scaling with Vector Databases: Chroma and Pinecone
- Indexing Algorithms: HNSW, IVF, and LSH
- Hands-on Lab 3: Initializing and Querying a Vector Database
- The Precision Problem: Why Document Chunking is Essential
- Chunking Strategies: Fixed-size, Overlap, and Boundary Rules
- Hands-on Lab 4: Document Chunking and Optimized Retrieval
- Bringing it All Together: The RAG Pipeline
- Hands-on Lab 5: Building a Complete End-to-End RAG Pipeline
- Production Concerns: Caching, Monitoring, and Error Handling
- Implementation Strategies for Query, Embedding, and LLM Caching
- Essential Metrics for Tracking RAG Performance
- Production Architecture: Microservices on Kubernetes
- Introduction to Model Context Protocol MCP
- The Role of AI Agents in Action-Oriented Systems
- Why We Need Standardized Tools for Third-Party Interactions
- MCP Architecture: Clients, Servers, and Local vs. Remote Hosting
- Hands-on Lab 6: Setting up the AI Assistant Environment
- Core MCP Components: Resources, Tools, and Prompts
- Understanding the MCP Specification and JSON-RPC Protocol
- Hands-on Lab 7: Connecting to and Using an Existing MCP Server
- Building a Custom MCP Server with the Python SDK
- Testing with the MCP Inspector
- Hands-on Lab 8: Developing Resources, Tools, and Prompts for MCP
- Building an MCP Client: Roots, Sampling, and Elicitation