Evals inside the product
Mental model: an eval is product infrastructure when it blocks unsafe regressions and turns real failures into a replayable fixture. It belongs in development, CI, staging, production sampling, and the feedback loop.
Mechanism: trace → labeled case → release gate → monitored outcome
AI product quality improves fastest when evals are part of the product workflow, not a separate research ritual. Every important behavior should have a way to be tested before and after release.
Where evals live
- Development: quick fixtures for prompt and context changes.
- CI: regression suite for expected behavior and structured output.
- Staging: trace replay from production-like traffic.
- Production: sampled human review and automated quality checks.
- Feedback loop: user corrections become candidates for eval cases.
Evals should track the actual product contract: task success, groundedness, format, safety, latency, and cost.
Build from real failures
The best eval cases come from production traces, support tickets, human review, and known edge cases. Synthetic cases are useful for coverage, but real failures keep the suite honest.
Release gates
A prompt/model/retrieval change should ship only if it improves the target behavior without regressing safety, cost, latency, or format reliability.
Pitfall
An eval suite that never changes becomes a museum. Keep adding cases when the product, users, or failure modes change.
Connects to: evaluation · ML CI/CD · feedback loops
baseline, candidate, safety_ok = .84, .87, True
print("ship" if candidate >= baseline and safety_ok else "hold")
Run with python3; expected output is ship. Apply the rule to critical slices, p95 latency, cost, schema validity, and refusal behavior—not only an average score.
Sources
- HELM — transparent multi-scenario evaluation.
- NIST AI RMF Measure — measurement and monitoring guidance.