What an Evaluation Plan Should Contain
The minimum useful contents of an AI evaluation plan: target behavior, risks, inputs, replay rows, thresholds, and decision owner.
- Author
- EAVAE Labs
- Reviewed by
- EAVAE Labs
- Published
- Jul 12, 2026
- Updated
- Jul 12, 2026
Define the target behavior
The plan should describe the workflow, user task, allowed behavior, disallowed behavior, and the decision the eval supports.
Without that definition, a test can measure activity while missing the business or engineering risk.
Name the input constraints
List what can be used safely: sanitized descriptions, redacted traces, public repositories, architecture diagrams, papers, sample datasets, or approved private access after scope.
Also state what must not enter the public intake flow: credentials, secrets, raw customer records, production data, or private repository access.
Connect thresholds to decisions
A threshold should explain what happens when it fails. Does the team block release, revise a subsystem, collect more data, or stop the path?
That connection is what turns an eval from a dashboard into a release tool.