AI Evals vs. Benchmarks vs. ML Metrics
NewAll three are 'measuring how good the AI is', and resumes mix them up. Benchmarks are public standard tests used to compare models (MMLU, SWE-bench) — mostly quoted, rarely run, by product teams. Evals are a team's own tests on its own use case, run every time the prompt or model changes. Classic ML metrics such as precision, recall and accuracy measure prediction models against labelled data. Building evals for a real product is the scarce skill.
How to tell them apart on a resume
Benchmarks
MMLU, SWE-bench, GPQA, leaderboards, comparing model releases — research labs, or someone citing scores when choosing a model.
Evals
Test sets built from real user cases, LLM-as-judge, regression testing for prompts, Braintrust, LangSmith, Promptfoo — product teams shipping AI features.
ML metrics
Precision, recall, F1, AUC, RMSE, confusion matrix, cross-validation — classic prediction models like fraud or churn.
The question that settles it
“How did you know your AI feature had got better or worse after a change — what exactly did you measure, and on which examples?”
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