Building explainable GraphRAG: why knowledge graphs beat vector search for auditable answers
camerongreen95 · reddit · 2026-09-17
The author hit an explainability wall building RAG on GPT models for a financial advisor use case: systems retrieve a chunk and answer confidently, but offer no reasoning trace—just a similarity score—a real problem for finance, legal, and compliance work requiring auditability.
The fix: use a knowledge graph as the retrieval layer's source of truth instead of vector search alone. Entities become nodes, relationships become edges, and queries like "who are Company X's indirect suppliers" become graph traversals that can structurally connect facts scattered across documents.
A Sep 19 workshop led by GraphAware chief scientist Dr. Alessandro Negro goes deeper: progressive knowledge graph building, multi-step verified entity/relationship extraction, agentic retrieval combining vector search with graph navigation, and text-to-Cypher querying on real financial filings.
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