AI Vocabulary · 07 When It Goes Wrong
What is Evals?
Repeatable tests that show whether your AI setup actually works, and whether a change made it better or worse.
Evals, short for evaluations, are tests for AI systems. You collect realistic examples of the task, define what a good result looks like, run your setup on all of them and measure how often it succeeds. Some checks can be automatic, others need a person or a second model to judge.
Because AI output varies with non-determinism, a few spot checks are not enough. Evals turn "it seemed to work" into a number you can track over time, and compare across models, prompts and providers.
The best starting point is often simple: look closely at real failures, group them into types, and turn each type into a test.
Why it matters for you
Without evals, every change is a gamble: a new model, a rewritten prompt or a different provider might quietly make things worse. With them, you can decide on evidence and show customers or auditors how you checked quality.
Own it
Your eval set is one of the most valuable AI assets you can own. It captures what "good" means for your business, and it lets you switch models safely whenever you want.
Where it comes from
Testing models against benchmarks is as old as machine learning, but the short form "evals" became everyday language in 2023, when OpenAI open-sourced its Evals framework alongside GPT-4. For product teams, Hamel Husain's 2024 essay Your AI Product Needs Evals made the practice mainstream: look at real failures first, then turn them into tests.
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