GraphRAG vs. RAG: why a knowledge graph finds more

Where classic RAG stops

RAG splits documents into chunks, makes them findable via vector search, and passes hits to a language model. That works well for look-up questions. It fails when the answer emerges from relationships: "Which proposals did we write for hospital groups in the last two years, and who worked on them?" Text chunks do not know that proposal A belongs to customer B in industry C.

What GraphRAG does differently

GraphRAG additionally builds a knowledge graph: entities and their relationships are stored explicitly. A question first navigates the graph to the relevant nodes, then fetches the underlying passages - answers come with sources. The difference shows in aggregations, multi-hop questions, and freshness: a changed fact is updated at one node instead of a hundred text copies.

Honest advice: GraphRAG is not always necessary. For an FAQ over a product manual, classic RAG is cheaper and faster to set up. The graph pays off once company knowledge is spread across systems, mailboxes, and heads - proposals and tenders, onboarding, expert search. That is exactly what we built KI MEMORY for: data-sovereign, on-premise capable.