RAG Patterns for Production: Beyond the Tutorial

Retrieval-Augmented Generation (RAG) is the most practical way to give LLMs access to your private data. But the gap between a demo and a production system is enormous.

Production RAG requires solving: 1. Intelligent chunking (not just splitting by tokens) 2. Hybrid search (semantic + keyword) 3. Re-ranking retrieved documents 4. Evaluation and monitoring 5. Handling stale or conflicting information

We'll build a production-grade RAG pipeline using LangChain, a vector database, and Claude as the generation model.