Lesson 1495 of 1596
AI Evals: Testing AI Outputs Like You'd Test Code
Eval frameworks let you measure prompt and model quality on a fixed test set.
Creators · Tools Literacy · ~7 min read
The premise
You can't improve what you don't measure. Eval suites turn 'feels better' into 'scored 87 vs 82.'
What AI does well here
- Run a fixed test set against new prompts/models.
- Compare outputs on rubric scores.
- Surface regressions when you change a prompt.
- Generate test cases when seeded with examples.
What AI cannot do
- Replace human judgment for subjective dimensions.
- Catch edge cases you didn't include in the eval.
Key terms in this lesson
Practice this safely
Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.
- 1Ask AI to explain evals in plain language, then underline anything that sounds uncertain or too broad.
- 2Give it one detail from "AI Evals: Testing AI Outputs Like You'd Test Code" and ask for two possible next steps plus one reason each step might be wrong.
- 3Check test-set against a trusted source, teacher, adult, expert, or original document before you use it.
End-of-lesson quiz
Check what stuck
10 questions · Score saves to your progress.
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