RAG Systems with LangChain: From Foundations to Advanced Patterns

Large language models are powerful, but they often lack access to your specific, private, or real-time data. Retrieval-Augmented Generation (RAG) bridges this gap, allowing you to build AI applications that answer questions accurately using your own documents. This text-only course guides you through the entire lifecycle of building production-ready RAG applications. You will transition from understanding core concepts to implementing advanced retrieval strategies and evaluating system performance using industry-standard patterns. What you'll learn: - Understand the foundational architecture of Retrieval-Augmented Generation and how it enhances LLM capabilities. - Configure document loaders, text splitters, and embedding models to prepare your data for retrieval. - Manage vector databases to store and query high-dimensional document representations efficiently. - Implement advanced retrieval techniques, including query rewriting, re-ranking, and hybrid search. - Apply modern evaluation methodologies to measure the accuracy, relevance, and safety of your RAG pipeline. - Practice building agentic RAG workflows that dynamically decide when and how to retrieve information. You will start with key terminology and foundational architectures before diving into hands-on code snippets. The course progresses systematically from basic document ingestion to sophisticated retrieval strategies and system evaluation. This course is designed for software developers, data enthusiasts, and AI beginners who want to build data-connected applications. No prior experience with LangChain or vector databases is required, though a basic understanding of Python is helpful. Start reading today to build smarter, data-aware AI applications with confidence.