GraphRAG adds an extraction-and-graph-build pipeline on top of RAG. It pays for that complexity only under specific workloads — so the transition should be a measured decision, not a trend-driven one.
Measure three things before deciding:
- Query shape. Sample real queries and label them: single-hop factoid, multi-hop relational, or global/thematic ('summarise all themes'). If multi-hop + global dominate (say >35%), naive top-k will keep failing structurally.
- Corpus signal. Do the same entities recur across many documents, with relationships between documents? GraphRAG's value comes from connecting facts that live in different chunks — no recurring entities, no payoff.
- Failure transcript. Pull answers that were wrong and ask: would a 2–3 hop traversal have fixed this? If the answer is repeatedly 'yes', the graph is earning its keep.
transition_rule:
if multi_hop_ratio >= 0.35 and recurring_entities: add graph extraction + hybrid retrieval
else: invest in better chunking, re-ranking, and query rewriting first
Why not always GraphRAG? The build pipeline (extraction, canonicalisation, community summaries, incremental refresh) is real, ongoing engineering. For a 20-page handbook answered by single-hop questions, it is pure overhead. The honest progression is: exhaust cheap RAG improvements, then upgrade when the data proves multi-hop demand.