Why Vector Search Fails on Hard Questions—And How GraphRAG Fixes It

Vector RAG systems excel at finding similar passages but struggle with corpus-wide questions, returning results based on keyword overlap rather than representative analysis. GraphRAG solves this gap by building knowledge graphs and hierarchical community summaries, enabling true holistic reasoning—though at approximately 1000x the computational cost. Understanding when this significant investment pays off is critical for building cost-effective AI search systems.