Graph RAG

Graph RAG combines two useful methods. Ordinary RAG finds relevant text passages; a knowledge graph supplies the relationships between the people, products, policies, events or cases involved. Together they help an assistant answer questions that require both evidence in documents and a reliable view of how the underlying facts connect.

This is not automatically better than ordinary RAG. It earns its complexity when the business question crosses multiple linked records, when entity ambiguity is costly, or when a reviewer must understand why a particular source was selected. For a straightforward policy question, well-curated document RAG may be simpler, cheaper and sufficient.

Concrete example: a leadership team asks which customer commitments could be affected by a new supplier restriction. Graph RAG identifies the supplier, connected products, contracts, customers and accountable owners; it then retrieves the specific clauses and current records needed to support an answer. The final response should still cite the passages and flag any missing or stale evidence.

The management test is clear: use Graph RAG only if connected context changes a real decision, and measure whether it improves retrieval quality, explanation and review enough to justify the extra data stewardship.