AI Evals (Evaluations)
AI evals are automated or human frameworks that measure model accuracy, bias, safety, and task performance before and after you ship.
Evals turn “does this feel better?” into repeatable scores teams can track across prompt, model, or UI changes.
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What it means
Evaluation suites run test prompts, golden datasets, or human rubrics to compare outputs on criteria like correctness, toxicity, latency, or format compliance.
Why designers should care
Design changes (new prompts, flows, or disclosure copy) shift behavior; evals catch regressions and give designers shared evidence for tradeoffs with engineering.
Example
Before launching a support copilot tone update, the team runs evals on 200 ticket types; UX review focuses on cases where empathy scores rose but factual accuracy dropped.
Common mistakes
- • Shipping prompt or model changes with no regression eval on critical user journeys.
- • Evals that only measure BLEU or length, not task success or harm scenarios.
- • Human review findings with no loop back into prompts, guardrails, or UI fixes.