Building RAG and MCP Servers with Claude

Large language models are incredibly powerful, but they are often limited by what they do not know—namely, your private data and real-time external tools. Connecting Claude to these resources securely and reliably is the key to building production-ready AI applications. This text-only course guides you through the foundational concepts of Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP). You will learn how to design, write, and deploy custom MCP servers that allow Claude to safely read databases, fetch live web data, and execute local tools. What you'll learn: Understand the core architecture of Retrieval-Augmented Generation (RAG) and how it enhances LLM accuracy; Configure Model Context Protocol (MCP) servers to establish secure communication between Claude and external data sources; Implement vector databases and semantic search strategies to retrieve highly relevant context for user queries; Create custom tool definitions and function-calling schemas that Claude can execute dynamically; Apply modern error-handling and security best practices to prevent prompt injection and data leaks; Practice building structured data pipelines that clean, chunk, and embed documents for AI retrieval. You will start with the fundamental terminology of vector embeddings and context windows before progressing to step-by-step code implementations of custom MCP servers and data ingestion pipelines. This course is designed for software developers and AI enthusiasts who want to build connected AI systems, with no prior experience with Claude's API or MCP required. Start reading today to unlock the full potential of context-aware AI applications.