Building Production RAG Systems with PostgreSQL: Complete Implementation Guide

Building Production RAG Systems with PostgreSQL: Complete Implementation Guide Most RAG (Retrieval-Augmented Generation) systems fail in production for predictable technical reasons: poor retrieval quality, lack of source attribution, and inability to handle real-world query variations. This guide shows you how to build a production-ready RAG system that actually works. Why Most RAG Systems Fail Production RAG failures stem from three core technical problems: 1. Pure Vector Search Limitations Vector similarity doesn't always match human relevance. A query for "API rate limits" might retrieve "request throttling guidelines" when users want exact information about "1000 requests/hour." 2. No Source Attribution Users don't trust answers they can't verify. Without clear source citations, even correct answers feel unreliable. 3. Single Search Strategy Relying only on vector search or only on keyword search misses important results. Real questions require both semantic understanding and exac