Building Production RAG Systems: A Practical Guide

Retrieval-Augmented Generation (RAG) has become the standard architecture for building LLM applications that need access to external knowledge. But moving from a demo to production requires careful consideration of several components. ## The RAG Stack 1. **Document Ingestion** — Chunking strategies, embedding selection, metadata extraction 2. **Vector Storage** — Choosing between ChromaDB, Pinecone, Qdrant, or Weaviate 3. **Retrieval** — Dense vs sparse, hybrid search, re-ranking 4. **Generation** — Prompt engineering, context window management, answer synthesis ## Chunking Strategies The chunking strategy dramatically affects retrieval quality. My approach from building AyurMind: - **Semantic chunking**: Split at natural boundaries (paragraphs, sections) - **Overlap**: 10-15% overlap between chunks for context preservation - **Metadata enrichment**: Tag chunks with source, section, and position ## Evaluation Use metrics like hit rate, MRR, and NDCG to evaluate retrieval quality before optimizing generation.