Knowledge graphs don't replace RAG — they solve a different problem
Vector-search-based RAG and aren't ; they solve different problems. finds text that is similar, while a finds text that is connected — and those are not the same.
Take a private equity example: asking 'who do we know that understands the logistics software space?' Standard RAG retrieves documents mentioning 'logistics software' ranked by — some call , , a CRM note. But the real answer is often scattered across four separate systems: a 2021 call note mentions a founder, a CRM record links that founder to a company, an email shows a partner met that founder at a conference, and a deal memo shows the firm passed on something similar before.
Standard RAG can't follow that thread because it only finds nearby documents and hopes the model stitches them together. Microsoft's own GraphRAG paper identified exactly this limitation in ordinary retrieval.
Key points
- (RAG) finds similar documents; find connected information — a different job entirely
- When the answer is scattered across multiple documents or systems, standard RAG can't trace that connecting thread
- Microsoft's GraphRAG paper confirms ordinary retrieval struggles with this kind of connection-based question
- A isn't a RAG replacement — it complements RAG for the specific problem RAG wasn't built to solve