GraphRAG vs Naive RAG: Enterprise Architecture Guide
Answer-first: Compare Naive RAG with GraphRAG for enterprise AI pipelines: knowledge graphs, LlamaIndex, chunking, streaming CDC, and security controls for dynamic data. 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. ...