Graph-RAG vs. vector RAG: when hybrid retrieval actually wins

Vector RAG answers “what’s semantically similar to this query.” Graph-RAG answers “what’s connected to this query.” Most production systems need both, but it’s worth being precise about which problem you actually have before reaching for the more complex architecture.

Where vector RAG falls short

Vector similarity search works well for direct semantic matches, but struggles with:

• Multi-hop questions (“who reports to the person who approved this contract?”)
• Aggregation queries across many documents
• Questions about relationships, not just content

What Graph-RAG adds

Graph-RAG layers a knowledge graph on top of retrieval, so the system can traverse explicit relationships between entities instead of relying purely on embedding proximity.

When it’s worth the complexity

In practice, I’ve found hybrid retrieval pays off when:

1. The domain has genuine multi-hop relationships (org charts, contract chains, dependency graphs)
2. Vector-only retrieval is measurably missing relevant context in evaluation
3. You can maintain the graph — it’s not free, and a stale graph is worse than no graph

If none of those apply, plain vector RAG with good chunking will usually get you further, faster.

Closing thought

Treat Graph-RAG as an escalation, not a default. Start with vector RAG, measure where it fails, and add graph structure only where the failure mode is genuinely relational.