# Evals-as-Unit-Tests

> Treat model evaluations as a CI/CD test suite — run them automatically on every checkpoint so quality regressions are caught in the pipeline, not in production.

- **Category**: Language Models
- **Subcategory**: Benchmarking
- **Canonical URL**: https://designpattern.fyi/patterns/evals-as-unit-tests/

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## 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.



## Use Cases
Any model training pipeline with automated checkpoint generation. Fine-tuning workflows where regressions are a real risk. Production deployments with committed quality SLAs. Teams iterating on fine-tuning who cannot manually evaluate every run.





## Trade-offs


### 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




### Considerations & Drawbacks

- Eval suite adds wall-clock time to the training pipeline proportional to suite 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