Explainer · August 18, 2026 · 8 min read
What is GraphRAG? A plain English explainer.
GraphRAG defined without the jargon: for engineers deciding what to build, and executives deciding what to fund.
The short answer
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, so the LLM answers from structured, cite able facts instead of statistical guesses.
RAG in one paragraph, then what GraphRAG adds
Retrieval augmented generation (RAG) 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, and at query time embed the user's question, find the most similar chunks, and pass them to the LLM as context. Works beautifully for "summarize this document" and "find me a policy that says X". Falls apart the moment the answer requires composing facts from multiple entities or systems.
GraphRAG adds a knowledge graph to the retrieval layer. 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.
Why it matters (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?" is a four hop traversal (Customer → Plan → Ticket → Feature) with a date filter. Vector RAG cannot compose that from independent chunks. GraphRAG returns the exact list, with citations to the source systems for each row.
- Fabricated citations. Legal and clinical assistants that ship with a pure LLM keep making up cases and papers. GraphRAG constrains the answer to nodes that exist in the graph: the model literally cannot cite a paper that isn't there.
- Stale personal context. "What did I order last week?" is not a similarity question: it's a graph query about your account. Vector RAG has no notion of "your". GraphRAG walks your account edges and answers with your data, freshly.
The five layers of a GraphRAG system
- Sources: your structured systems (Postgres, Snowflake, Salesforce, ERP) and unstructured content (docs, tickets, transcripts).
- Ingestion & entity extraction: structured data maps directly to graph edges; unstructured text gets entities extracted and linked back to the canonical graph.
- Knowledge graph + vector index: both indexed by the same entity IDs, so the retriever can hop between them.
- Hybrid retriever: plans a graph query (often LLM generated Cypher/SPARQL), optionally expands with vector similarity for supporting text.
- Grounding & generation: retrieved facts are shaped into a prompt that constrains the LLM to answer only from what was returned, with inline citations.
We covered this architecture and the delivery phases in more depth on the GraphRAG implementation page.
When to use GraphRAG (and when not to)
Use GraphRAG when
- • Your top user questions require multi hop reasoning across entities
- • Fabricated citations or hallucinations are a business or compliance risk
- • You need answers that reference specific customer, contract, patient, or asset data
- • Regulators or auditors need to see provenance for every fact
Don't bother with GraphRAG (yet) when
- • Your corpus is a large pile of homogeneous documents with no meaningful relationships
- • Similarity search over unstructured text is genuinely all you need
- • You're still validating the underlying use case: start simple, add the graph when vector alone hits a wall