What Is GraphRAG?

Retrieval & Data Also known as: graph RAG, graph-based RAG

GraphRAG is a form of retrieval-augmented generation that pulls context from a knowledge graph, a structured network of entities and the relationships between them, rather than from plain text chunks alone. Because it can follow the connections between facts, it answers questions whose evidence is spread across many documents better than standard RAG.

How GraphRAG works

Standard RAG splits documents into chunks, stores them as embeddings, and retrieves the chunks most similar to a question. That works well when the answer sits in one place, but it struggles when the answer depends on how separate facts connect.

GraphRAG adds a knowledge graph to the retrieval step. Information is organized as entities, such as people, companies, or products, and the relationships between them. When a question comes in, the system can walk those relationships to gather connected facts, then hand that structured context to the language model. The result is stronger on questions that require joining information from several sources.

Why GraphRAG matters for AI

GraphRAG matters because a lot of valuable business questions are relationship questions. Which clients are affected if one supplier fails, how a policy change ripples across departments, how a set of contracts relate to each other. These need the connections between facts, not just isolated passages.

Building the knowledge graph is more work than chunking text, so GraphRAG is not the right tool for every case. Where the questions are genuinely connected, it pays off. At Custom AI Studio, structured retrieval like this is part of how we make an AI system reason over a company's own knowledge rather than answering in isolation.

Frequently asked questions.

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What is the difference between GraphRAG and standard RAG?

Standard RAG retrieves text chunks by similarity, while GraphRAG retrieves from a knowledge graph that captures how facts relate. GraphRAG handles multi-step and relationship questions better, at the cost of building and maintaining the graph.

When should you use GraphRAG?

When answers depend on connections across many documents or entities, rather than sitting in a single passage. For simple lookups where the answer is in one place, standard RAG is usually simpler and enough.

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