Is your LLM lying to you?

Vector-only RAG systems hallucinate because semantic similarity doesn't equal relevance — they can't traverse relationships between data sources. GraphRAG solves this by combining vector search with knowledge graph traversal, enabling AI to answer complex multi-system queries (e.g., which customers are affected if a transformer goes offline). Independent research shows 3x higher response quality and 28.6% faster issue resolution compared to vector-only approaches. Knowledge graphs add explainability, traceability, and access controls that pure vector architectures lack, making AI applications production-ready.