The Death of Naive RAG and the Shift to GraphRAG

The first version of almost every enterprise knowledge assistant looks the same: split documents into chunks, embed them, store the vectors, retrieve the top few by cosine similarity, and paste them into a prompt. On a pilot corpus of a few hundred well-written documents, this works well enough to impress a steering committee. On the real corpus — thousands of linked contracts, tickets, policies, specifications, and emails — it hits a ceiling that better prompts and larger models do not lift. This topic explains why that ceiling exists and how an ontology-backed knowledge graph moves it.…