.Leveraging Graph RAG to Fix LLM Hallucinations Where Facts Can't Be Wrong
Most RAG systems rely purely on vector similarity search: documents are embedded, queries are embedded, and the closest matches are retrieved. That approach has a fundamental limit — vector similarity doesn't understand relationships. For questions like compliance requirements across jurisdictions, vector-only systems can't reason about which regulations apply where, how requirements relate to business processes, or whether sources conflict. Graph RAG addresses this by building knowledge graphs that capture entity relationships explicitly, with tooling options including Microsoft GraphRAG, LightRAG and Neo4j — each with different trade-offs. Its real advantage is traceability: every fact has a path through the graph, enabling audit trails and explainability for regulated industries.