Stop graphing everything: When GraphRAG actually beats vector RAG

The article argues that standard vector RAG works well for direct lookup questions but struggles when answers require connecting facts across documents or summarizing themes across a corpus. GraphRAG can improve results by building knowledge graphs of entities and relationships, though the gains depend heavily on question type and come with added overhead.

The key trend is a shift from simple semantic search toward retrieval systems that model structure, relationships, and context. Graph-based retrieval is likely most valuable in enterprise domains with dense, interconnected knowledge, but teams should avoid adopting it broadly unless their use cases justify the added complexity.