Checklist · Model Evaluation

Model Evaluation Checklist

Select a model with evidence rather than a leaderboard, building the evaluation set, pinning conditions, comparing fairly on cost as well as quality. Free, editable Markdown.

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Task: _______________ Date: _______ Evaluated by: _______________

The question is not which model is best. It is what is the cheapest model that passes my evaluation set: because pricing spans a thousand-fold range and quality on well-specified business tasks does not.

1. Define the task first#

[ ] The task is stated specifically enough to be checked
[ ] "Correct" is defined as properties, not a single expected string
[ ] Consequence of a wrong answer understood, this sets the required bar
[ ] Latency requirement stated
[ ] Volume estimate stated

2. Build the evaluation set#

[ ] 20 to 50 cases drawn from real work
[ ] Typical cases
[ ] Edge cases: empty, malformed, unusually long, wrong language
[ ] Every known past failure, permanently
[ ] Should-refuse cases
[ ] Adversarial cases, injection, contradictory instructions
[ ] Expected outcome recorded for each

3. Pin the conditions#

[ ] Model version string recorded, not "latest"
[ ] Temperature and sampling parameters fixed
[ ] Prompt version fixed and identical across candidates
[ ] Retrieved context fixed, if applicable
[ ] Number of runs per case fixed (5 is a reasonable default)
[ ] Scorer version fixed
[ ] Date recorded. Providers update models under the same name

4. Score#

[ ] Deterministic checks: parses, fields present, arithmetic, no personal data
[ ] Rubric for semantic quality, max 4 dimensions
[ ] Judge is a different model from any candidate
[ ] Pass rate reported per case, not a single aggregate
[ ] Variance reported, not just the mean

5. Compare fairly#

[ ] Same inputs, same scorer, same run count for every candidate
[ ] Equal tuning effort. Do not polish one prompt and not another
[ ] Started from the cheapest candidate, moving up only on failure
[ ] Ties reported as ties, not resolved on a tenth of a point

	Model A	Model B	Model C
Deterministic pass rate
Rubric score
Pass rate at production temperature
p50 / p95 latency
Cost per completed task
Refusal accuracy
Injection resistance

6. The criteria not on any leaderboard#

[ ] Data handling terms. Is your input used for training?
[ ] Regional data residency
[ ] Rate limits at your actual volume
[ ] Deprecation policy and notice period
[ ] Context window, if genuinely needed
[ ] Provider stability

7. Decide and record#

[ ] Chosen model recorded, with the reason
[ ] Rejected candidates recorded, with why, stops the debate recurring
[ ] Model version pinned in configuration
[ ] Evaluation set committed alongside the prompt

8. Re-evaluation triggers#

[ ] Model version change. A full run before shipping
[ ] Prompt change
[ ] Data distribution shift
[ ] New failure mode observed in production
[ ] Scheduled regardless, quarterly floor

Sign-off#

	Name	Date
Evaluated by
Decision approved by

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