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GraphRAG
1 posts about GraphRAG. Every summary links to the original.
Why AI Agents Get the Right Facts but the Wrong Answer—and How GraphRAG Helps
The post examines why an agent can cite accurate return-policy evidence yet reject an eligible customer when decisive facts are distributed across systems. Its washing-machine scenario combines a product classification, an extended-policy rule, membership tier, purchase amount, and purchase date to show that retrieving enough evidence differs from retrieving relevant evidence. GraphRAG addresses the gap by extracting entities and relationships into a knowledge graph, then using multi-hop retrieval to follow connections from WM-500 to the applicable policy and supporting records. The implementation also uses explicit pointers from graph entities to structured data, while separating the TBox blueprint from ABox instances and requiring business validation of the schema. The diagnostic framework distinguishes missing retrieved context, omitted business rules, and broken record mappings, while noting that these examples are not benchmark results and that correct evidence can still be used incorrectly.
Scott Nyberg
