GenAI products only improve when evaluation is continuous, cheap and tied to user intent. Static benchmarks help with model selection, but product teams need live feedback loops that reflect their domain, tone and risk tolerance.
Start with explicit quality definitions: what does “good” look like for accuracy, safety, tone, brevity and factual grounding? Turn those into rubrics that reviewers (or a secondary model) can score. Pair them with golden test sets that include edge cases, adversarial inputs and known failure modes.
Operationalise this with three tiers of evals: pre-deploy regression suites for every prompt or policy change, canary evals on a small slice of production traffic, and ongoing post-deploy monitoring that blends automated scoring with user feedback signals. Treat eval failures like test failures: stop the rollout, fix, re-run.
The payoff: product managers get faster confidence in changes, engineers catch regressions before customers do, and risk teams see concrete evidence that safety policies are enforced. Over time, the evaluation loop becomes the backbone for scaling new features, models and regions without losing quality.