AI Evals for AI Operators, PMs & Engineers by Product Faculty

Define observable criteria for usefulness, correctness, safety, tone, and task success.

Map failure severity across user segments and critical workflows.

Establish decision thresholds for deployment, rollback, and escalation to human review.

Design sampling strategies spanning common cases, long-tail behavior, and adversarial inputs.

Build golden sets grounded in trusted reference judgments.

Evolve datasets with production evidence while avoiding overfitting to easy examples.

Design annotation and adjudication protocols that resolve conflicting interpretations.

Translate subjective quality dimensions into explicit scoring anchors.

Measure reviewer agreement and refine criteria where ambiguity weakens the signal.

Develop reference-aware LLM judge prompts for dimension-specific scoring.

Calibrate automated judgments against expert human annotations.

Assess evaluator bias, variance, agreement, and drift to identify unreliable scoring.

Disaggregate results by user, task, language, risk, and behavior.

Identify recurring failure clusters concealed by aggregate performance.

Investigate regression causes across prompts, retrieval, models, tools, and UX.

Integrate offline evaluation with online monitoring.

Define dashboards, release gates, and accountability for quality decisions.

Establish an evaluation cadence that turns failure diagnosis into prioritized product improvements.