Production RAG with LangChain & Vector Databases: Full Course

Getting RAG to Work in a Notebook Is Easy. Keeping It Alive in Production Is the Real Test.

Most RAG tutorials stop right after the first successful answer. This course, at more than seven and a half hours, starts there and keeps going into the territory where real systems succeed or fail: debugging, cost, scale and security.

The foundation (0:00 to about 1:35)

You begin with a full RAG overview, set up your development environment and learn how document loaders and the indexing pipeline work. Then comes a deep dive into embedding dimensions, followed by hands-on work creating a vector database with Chroma, running similarity search with scores and building a basic RAG system.

Making it better (about 1:35 to 3:20)

This is where the course separates itself. You learn how to debug RAG systems, use hybrid search, manage token budgets and add observability with LangSmith. A full section on RAG optimization shows how to improve retrieval quality with evidence rather than guesswork.

Going to production (about 3:20 to 6:05)

Scaling RAG systems, understanding the real costs of vector search and choosing production hosting come first. You then set up Supabase and PGVector, learn the three pillars of production visibility and build a full production project with a security layer, a LangGraph agent, a FastAPI API and a LangSmith dashboard. The security layer is tested and reviewed with a checklist.

Advanced RAG topics (about 6:05 to the end)

The final stretch looks at where RAG is heading: long context models vs RAG, contextual retrieval, late vs early chunking, agentic RAG with self-correcting retrieval, GraphRAG for multi-hop reasoning, and multimodal RAG with ColPali. It closes with a summary of the current state of advanced RAG and how the field has evolved.

Honest note: This is a long, intermediate-level course. Basic Python and some familiarity with LLMs will help you get the most out of it.

Who should watch? AI engineers, backend developers, data scientists and anyone building LLM apps who needs RAG that works beyond the demo. 🚀