Why RAG Is Failing at Complex Questions (And How Knowledge Graphs Fix It)

Traditional RAG systems fail at complex multi-hop questions because they treat knowledge bases as disconnected text chunks, relying on vector similarity search that cannot follow logical inference chains across multiple documents. Microsoft's research found standard RAG captures only 22-32% of comprehensive answers on such queries. GraphRAG addresses this by using knowledge graphs that represent entities and relationships as nodes and edges, turning multi-hop queries into graph traversal problems.