Model Evaluation (Evals)

Building evaluation pipelines for AI systems: what to measure, how to build eval datasets, and how to automate regression detection.

An eval is a test for an AI system. The difference between a test and an eval is that tests have deterministic expected outputs; evals measure quality on a distribution. You need both, and data engineers build the infrastructure for both.

The highest-value evals are grounded in real user failures. Collect the cases where your system gave wrong, harmful, or unhelpful outputs. Build your eval set from these. An eval suite built from synthetic edge cases will miss the failure modes your users actually encounter.

Evals require labeled data, which means they require the same infrastructure as training data: a labeling pipeline, quality metrics, versioning. The eval dataset is a first-class data product. The people who label evals should be the same people (or carefully calibrated) who label training data -- otherwise your eval scores and your model quality measure different things.

Automated evals using LLM-as-judge are useful but not sufficient. LLM judges have systematic biases: they prefer longer answers, they prefer confident answers, they are not well-calibrated on factual accuracy. Calibrate your judge against human judgments on a sample before trusting it at scale.

The right eval cadence matches your release cadence. If you ship weekly, run evals on every PR. Build regression detection: alert when a change causes a statistically significant drop on any eval dimension. The cost of catching a regression before it reaches users is much lower than the cost of recovering from it.

Eval coverage is the hardest long-term problem. You will always have blind spots. Build mechanisms to find them: adversarial probing, coverage analysis of your eval set against your traffic distribution, structured feedback from domain experts who use the system daily.