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RAG that retrieves over a knowledge graph of entities and relationships instead of (or alongside) flat text chunks.
Plain RAG retrieves isolated chunks, so it struggles with questions that need connecting facts scattered across many documents — 'which engineers worked on projects that later failed?' GraphRAG first builds a graph of entities and how they relate, then traverses that structure to assemble an answer, often summarizing whole communities of related nodes. It shines on multi-hop and 'whole corpus' questions where the answer is in the relationships, not any single passage. The cost is the upfront extraction pipeline to turn messy text into a clean graph.
Plainly
Think of GraphRAG as a simple recipe for doing the work better. RAG that retrieves over a knowledge graph of entities and relationships instead of (or alongside) flat text chunks.
In practice
Use it when you need a repeatable method instead of guessing from vibes. In practice, define the owner, input, output, and failure mode before you rely on it.