Evals-as-Unit-Tests
Treat model evaluations as a CI/CD test suite — run on every checkpoint automatically so quality regressions are caught in the pipeline, not discovered by users.
Intent & Description
🎯 Intent
Make model quality regressions visible at the same cadence as code regressions — caught before shipping, not discovered from user complaints after.
📋 Context
Model training is iterative. Fine-tuning on new data improves targeted behavior while silently degrading others. Without automated eval gates on every training run, you discover regressions from user feedback — after they’ve already shipped.
💡 Solution
Define an eval suite covering critical capabilities for your deployment (domain accuracy, instruction following, safety, refusal rate, output format compliance). Run the full suite automatically on every checkpoint. Set pass/fail thresholds based on production baseline scores. Block promotion of any checkpoint that regresses beyond threshold on any eval. Treat a failing eval exactly like a failing unit test — it must be investigated before the checkpoint advances.
Real-world Use Case
📌 TL;DR
Run evals on every checkpoint like unit tests. Regressions caught in the pipeline stay out of production — ones caught from user reports already shipped.
Advantages
- Regressions caught at training time — not after deployment
- Provides a quantitative quality baseline that persists and compounds across training runs
- Same CI/CD mental model as software testing — familiar workflow for engineering teams
Disadvantages
- Eval suite adds wall-clock time to the training pipeline proportional to coverage
- Suite blind spots are production blind spots — coverage gaps let regressions through
- Pass/fail thresholds require calibration and drift over time as model capability improves