GraphRAG is retrieval-augmented generation where the retrieval layer queries a knowledge graph not, or not only, a vector database.
Instead of returning similar text chunks, it returns typed entities and the relationships between them. The LLM then answers from structured, citable facts rather than statistical guesses about which paragraph looked relevant.
RAG in one paragraph, then what GraphRAG adds
Retrieval-augmented generation grounds an LLM answer in retrieved evidence. The standard recipe: chunk your documents, embed them with a model, store the embeddings in a vector database. At query time, embed the user's question, find the most similar chunks, pass them to the LLM as context.
This works beautifully for "summarize this document" and "find me a policy that says X."
It falls apart the moment the answer requires composing facts from multiple entities or systems.
A knowledge graph stores your business as typed entities customers, contracts, products, incidents connected by typed relationships. When a user asks a question, the retriever can walk the graph (customer → contract → clause → obligation) to compose the actual answer, then hand the LLM the resulting facts with pointers to the source systems they came from.
Three concrete failures GraphRAG fixes
Hallucination on multi-hop questions.
"Which of our Platinum plan customers raised P1 tickets about the feature we deprecated last quarter?"
No single document contains that answer. Vector search returns chunks that look related and the LLM fabricates the join. GraphRAG returns the exact list, with citations to the source system for each row.
Fabricated citations in high-stakes domains.
Legal and clinical assistants built on pure vector RAG keep inventing cases and papers. When retrieval returns a typed entity with a real identifier, there is nothing to invent the citation is the node.
Answers that go stale silently.
Vector chunks do not know that a policy was superseded. A graph can model supersession as an edge and exclude the retired version by traversal, rather than hoping the stale chunk ranks lower.
The five layers of a GraphRAG system
| Layer | What it does |
|---|---|
| Ontology | Defines the entity types and relationship types your domain actually has |
| Ingestion | Extracts entities and relationships from source systems and documents |
| Graph store | Holds the typed nodes and edges; Neo4j is the common choice |
| Hybrid retrieval | Combines graph traversal with vector search over node text |
| Evaluation | Measures whether retrieved context actually supports the answer |
The ontology is the part teams underestimate. It is a modeling exercise about your business, not a database schema task, and getting it wrong is the most expensive mistake in the stack.
The evaluation layer is the part teams skip. Without it you cannot tell a retrieval regression from a model regression.
When to use GraphRAG and when not to
Use it when:
Answers require composing facts across multiple entities or systems
You need counting, aggregation, or absence ("which accounts have no owner?")
Citations must point to systems of record, not paragraphs
Permissions are relationship-shaped and cannot be expressed as one filter
Content gets superseded and stale answers are harmful
Do not use it when:
Your corpus is a document pile and the questions are "summarize" or "find the policy that says X"
Access control is effectively open, or expressible as a single filter
Nothing in your source data gets reversed or superseded later
If all three of those hold, plain vector search is sufficient and considerably cheaper to run. Internal enterprise search over operational data almost never satisfies all three at once which is why the question keeps coming up.
For the full decision framework, see GraphRAG vs vector RAG.
We build enterprise GraphRAG systems on Neo4j. If you are deciding whether your stack needs one, talk to our team.
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