Lesson 1087 of 1596
Evaluation suite fundamentals: what to measure and how
Build an eval suite that mixes deterministic checks, LLM-as-judge, and human review — knowing each one's limits.
Creators · AI Foundations · ~7 min read
The premise
A real eval suite combines fast deterministic checks, mid-cost judge models, and slow human review; each layer covers what the others miss.
What AI does well here
- Design a tiered eval suite with appropriate cost per tier.
- Draft regression-set hygiene rules to prevent eval rot.
What AI cannot do
- Replace human review for subjective qualities.
- Eliminate the maintenance cost of eval suites.
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 deterministic eval in plain language, then underline anything that sounds uncertain or too broad.
- 2Give it one detail from "Evaluation suite fundamentals: what to measure and how" and ask for two possible next steps plus one reason each step might be wrong.
- 3Check LLM as judge 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.
Tutor
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