Your Vector Database Is Lying to You About Multi-Hop
Dense retrieval hits a proven geometric ceiling on multi-hop questions and fails silently — similarity scores stay confident even when the evidence is missing.
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Dense retrieval hits a proven geometric ceiling on multi-hop questions and fails silently — similarity scores stay confident even when the evidence is missing.
Ground the model in retrieved evidence, constrain its output, verify its claims, and measure everything. A layered defense against LLM hallucination.
RAG quality by vibes doesn't survive a second engineer. Decompose by stage, build the eval set from real failures, calibrate the LLM judge, and gate CI.
A RAG demo proves the happy path exists. Production is everything else — tracing, drift, evals, and learning to say 'I don't have that part.'
Most LLM hallucination is a retrieval failure in disguise. Fix the context first, force citations, and give the model a sanctioned way to say 'I don't know.'
Inference cost optimization is a measurement problem in disguise. Fix the quality metric first, then trim context, route models, and cache the stable prefix.