Recommend VECTOR / GRAPH / HYBRID retrieval for a query workload, grounded
in Ch1's BenchmarkQED evidence for where vector RAG succeeds and where it
collapses. Classifies the workload on the BenchmarkQED scope x type axes
(local/global, data/activity), weighs multi-hop / temporal / associativity
needs, domain structure, corpus scale, and latency, then returns a
recommendation with the chapter's numbers (vector RAG ~90% on DataLocal vs
20-30% on ActivityGlobal; LazyGraphRAG +50-60% on multi-hop; EyeLevel 12%
vs 2% accuracy drop at 100k pages). Includes the explicit larger-context-
window rebuttal (the ~1M-token BenchmarkQED test) and surfaces GraphRAG's
own costs. Use when…
