AI Models tool

Model eval sample size planner

Choose a practical eval size that catches expensive failures without pretending to be a research benchmark.

Fast answerA first eval set with hard cases, common cases, regression cases, and a retest cadence.

Inputs

What to collect

Workflow types	The jobs that will actually run in production.
Failure categories	Hallucination, missed constraint, bad code, unsafe action, weak citation, or slow answer.
Release cadence	How often prompts, models, or tools change.

Method

How to use it

1	Start with 20-50 real cases per high-value workflow.
2	Include known failures and ordinary happy-path tasks.
3	Add cases after incidents, not just before launch.

Output

How to read the result

Pilot	20-50 cases	Enough to expose obvious failures and compare candidates.
Production	100+ cases	Better for routing, regression checks, and model upgrades.
Critical workflow	Human-reviewed gold set	Use expert grading where failure cost is high.

Thresholds

Useful vs risky

Healthy	Each eval case maps to a real task and has an expected decision.
Risky	The eval is a generic benchmark that does not predict user pain.

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