GraphRAG vs Naive RAG: Enterprise Architecture Guide
Answer-first: Naive RAG works well for simple keyword queries on isolated documents. For complex, global questions spanning multiple entities, GraphRAG is superior as it builds a knowledge graph using LLMs. Enterprise implementations require combining change data capture (CDC) with vector search to keep graphs synchronized. What You’ll Learn That AI Won’t Tell You Schema design for knowledge graphs that speed up global enterprise RAG. Syncing GraphRAG knowledge bases in real-time using PostgreSQL WAL events. Most RAG (Retrieval-Augmented Generation) implementations look the same: chunk documents, embed them into vectors, store them in a vector database, retrieve by cosine similarity, and inject the top-K chunks into the LLM context. This works for simple document Q&A. It fails systematically for enterprise knowledge bases where the answer to a question depends not on a single document chunk, but on the relationships between dozens of interconnected entities. ...