Glossary · Definition
Evals (AI evaluation)
Evals are systematic tests of AI model quality — graded test sets that measure performance on specific tasks. Critical for picking models, validating fine-tunes, and not shipping regressions.
Definition
Evals are systematic tests of AI model quality — graded test sets that measure performance on specific tasks. Critical for picking models, validating fine-tunes, and not shipping regressions.
What it means
Public evals: MMLU (general knowledge), SWE-bench (real GitHub issues), HumanEval (Python coding), GSM8K (math), MATH (harder math), TruthfulQA (faithfulness), MT-Bench (chat). Production teams build CUSTOM evals on their own task distribution — what public evals don't cover. Tools: OpenAI Evals, Promptfoo, LangSmith, Braintrust, Inspect, Phoenix. The most important rule: don't trust public benchmarks alone for your decision.
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Why it matters
Picking a model on benchmark scores alone is a leading cause of production-AI disappointment. The model that wins MMLU may lose on YOUR specific task. Building custom evals (30-50 representative tasks) is the antidote — and it's how serious AI teams pick models in 2026.
Related free tools
Frequently asked questions
How many tasks for a useful eval set?
30-50 minimum. Less = noise dominates. More = diminishing returns until you're at 200+.
Best tools to start?
Promptfoo (YAML-driven, easiest entry) or OpenAI Evals (Python framework). LangSmith for trace+eval workflows.