AI SignalEvaluating AI features without burning trust (or latency budgets)
A simple framework for instrumentation, staged rollouts, and human-visible failure modes.
9 min read
Most regressions users feel are not “model got dumber”; they are changes in grounding, verbosity, refusal behavior, and tool-selection mistakes.
Treat evaluation as layered: synthetic checks catch obvious drift, curated internal tasks catch regressions relevant to your product, and in-product instrumentation catches what labs never modeled.
Make failure graceful: degrade to deterministic paths, cite uncertainty, and always keep a reversible rollout switch.