Retrieval-augmented generation gave LLMs a memory: fetch relevant chunks, hand them to the model, get a grounded answer. It works well — until the question needs more than one fact, sitting in more than one place, connected in a way a similarity search can’t see.
Where traditional RAG breaks
- Multi-hop questions — the answer requires connecting facts across documents.
- Lost context — chunking severs the relationships that made the text meaningful.
- Near-duplicates — vector search returns five versions of the same passage and misses the one that matters.
- No global view — it can find a paragraph but can’t reason over the whole corpus.
How Graph RAG works
Graph RAG builds a knowledge graph from your corpus first — entities and the relationships between them — then retrieves over that structure. Instead of “here are some similar chunks,” the model gets “here is the connected subgraph that answers this,” which is a far better thing to reason from.
When to reach for it
- Complex, regulated, or highly interconnected enterprise data.
- Questions that span many documents and require reasoning, not lookup.
- Cases where you must explain why an answer is what it is.
“Vector search finds text that looks similar. Graph RAG finds facts that are actually connected. For enterprise questions, that difference is the answer.”
KnackLabs
