Evaluating AI Features Like Grown-Ups

Mature teams split evaluation into offline and online. Offline suites track accuracy, latency, and cost on curated datasets; online A/Bs measure user value and retention. Human review gates sensitive flows and feeds high-quality labels back into the dataset. Guardrails include deterministic fallbacks, rate limits, and explicit user affordances to report bad results. Evaluation is continuous: a model may pass today and regress tomorrow as distributions drift. Treat evals as living contracts, not one-off reports.