TLDR Tech / Data
GraphRAG beyond the demo: Lessons from the trenches
GraphRAG is most useful when questions require multi-hop reasoning across documents, entity relationships, or system-level dependencies: use Vector RAG for simple factual lookups and keep GraphRAG as an opt-in backend. In production, the main pain points are heavy indexing cost, difficult updates, multi-layer evaluation, and infrastructure that usually needs batch jobs rather than request-path execution. Success depends on selective graph scope, explicit update policies, repeatable evals, and strong observability/cost controls.
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