Module 3 of 6 · Expert · 9 min read
Evals in production
Offline test sets, online monitoring and regression gates for AI features.
Key takeaways
- 1Every AI feature needs an eval set before it ships.
- 2Combine code-based checks, model graders and human review.
- 3Monitor live traffic for drift and new failure types.
01The eval stack
Use the cheapest grader that is reliable for each criterion.
- Code checks: format, schema validity, exact matches, tests passing.
- Model graders: rubric-based judgements for quality, tone, faithfulness.
- Human review: calibrate graders and catch what they miss.
02Regression gates
Run the eval suite in CI on every prompt, model or retrieval change. Block deploys when key metrics drop. Model upgrades in particular can improve averages while breaking specific cases you care about.
Watch out
Do not trust a model grader you have not checked against human labels on at least a few dozen examples.
03Online monitoring
Sample production traffic, grade it automatically, and track metrics over time. Collect user feedback signals (thumbs, edits, retries). Feed newly discovered failures back into the offline eval set so they never regress.
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