Production-Ready RAG: Connecting Custom Documents to Generative AI

RAG has emerged as the critical pattern for connecting large language models with proprietary document collections, enabling organizations to build AI applications grounded in their own data. This comprehensive guide bridges the gap between RAG theory and production deployment, providing developers with architecture, code patterns, and debugging strategies to implement it reliably. With detailed integration of LangChain, pgvector, and Gemini, it offers a blueprint for scaling personalized, knowledge-aware AI systems.