GraphRAG vs naïve RAG

Weighing an expensive, powerful relational index against a lightweight, cheap semantic match — and choosing correctly for your query mix.

Why it matters

GraphRAG is not a strict upgrade over naïve RAG — it's a different tool with real operational cost: an extraction pipeline, a graph database, community detection, and a more complex query planner. Reaching for it by default is a common and expensive mistake.

Naïve RAG wins when queries are direct and single-hop: 'What was the company's Q3 revenue?' has one answer sitting in one chunk. Vector search finds it in milliseconds with no graph infrastructure at all — building a graph pipeline here adds latency, cost, and failure surface for zero benefit.

GraphRAG earns its cost when queries require cross-document synthesis or multi-hop reasoning: 'Which of our suppliers were also named in a competitor's litigation over the past three years?' has no single-chunk answer — it requires composing facts across entities and relationships that naïve RAG structurally cannot assemble, no matter how large k gets.

The decision isn't about corpus size or entity count in isolation — it's about what fraction of real queries are single-hop lookups versus multi-hop relational questions. A 100-million-document corpus where every question is still 'what does clause X say' is still better served by naïve RAG; a modest 10,000-document corpus where users constantly ask 'who's connected to whom' justifies GraphRAG immediately.

GraphRAG is a power tool, not a default. The right architecture depends on the query class, so route by it rather than committing to one pipeline for everything.

Decision rule: GraphRAG pays rent when entities recur across documents and the answer needs paths, not just passages. For localized single-hop questions, naive RAG wins on cost, latency and simplicity.

The mature design keeps both and routes. A classifier (or the supervisor agent in Level 3) sends single-hop factoids to vector search and multi-hop/global questions to GraphRAG. Forcing every query through the graph wastes money on easy questions; forcing every query through naive RAG caps your quality on hard ones. The skill is not picking a winner — it's knowing which question is in front of you.

Naive RAG vs GraphRAG is a bicycle vs a car. For a trip to the corner shop the bike is faster and cheaper; for crossing the country you want the car. Owning only one means you're either exhausted on long trips or stuck in traffic for a loaf of bread. Own both; pick per trip.

• One architecture for all queries. Fix: classify query type and route.
• GraphRAG for a tiny single-hop corpus. Symptom: cost with no quality gain. Fix: naive RAG.
• Naive RAG for relational questions. Symptom: recall ceiling. Fix: route those to the graph.
• No routing telemetry. Fix: log which path each query took and its outcome.