Build Your First Production RAG Pipeline — Free 8-Hour Course on freeCodeCamp

TL;DR: This free 8-hour freeCodeCamp course takes you from "chatbot with docs" to a deployed, observable, secure RAG system using LangChain and Supabase pgvector.

What you'll be able to do after this: build a RAG pipeline that survives contact with real users — with visible retrieval, token budgeting, and a security layer, deployed on real infrastructure.

• Embed and query documents end-to-end with LangChain + pgvector (Supabase), with chunking strategies and similarity search you can tune to your data
• Debug in production — add an observability dashboard showing exactly which retrieved chunks answered (or didn't answer) each query
• Go beyond basic RAG — hybrid search, contextual retrieval, late chunking, GraphRAG for linked-data scenarios, and a multimodal pipeline for vision-based documents

The course is taught by Paulo Dichone and runs 8 hours on freeCodeCamp's YouTube channel — a structured progression, not a clip collection. It's designed for the gap between "I built a chatbot that answers questions about my PDF" and "I have something I can ship to real users."

RAG is the most practical entry point into custom model behavior: no fine-tuning, no GPU, just your data and a vector store. If you've hit a wall after a 20-minute tutorial and wondered why retrieval wasn't working in practice, this is what comes next.

Sources: Production RAG with LangChain & Vector Databases — freeCodeCamp, Course on YouTube