The limits of vector-only retrieval
Vector search is excellent when a question can be answered from semantically similar chunks. It starts to degrade when the answer depends on relationships between entities across multiple documents.
Enterprise questions often require:
- multi-hop reasoning across teams, systems, and policies
- conflict resolution between current and historical statements
- traversal over explicit dependency chains
Pure vector retrieval treats context as nearby embeddings, not as linked facts.
What GraphRAG adds
GraphRAG layers a graph structure over your content so retrieval can combine:
- semantic similarity from embeddings
- explicit topology from graph edges
- constraint-aware traversal (entity, time, source, lineage)
This hybrid approach improves precision on compositional questions because the model receives context that is both semantically relevant and structurally connected.
Typical enterprise win
When users ask questions like "Which policy exceptions approved last quarter impacted customer onboarding latency?" the answer needs linked entities and chronology, not just similar passages.
Graph-backed retrieval produces more faithful evidence chains and lowers hallucination risk by grounding generations in traceable nodes and edges.
Practical adoption pattern
Start by graphing only high-value domains (policies, products, controls, incidents), then expand coverage based on query logs. You do not need a perfect enterprise graph on day one to see benefit.