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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.

EAVAE LabsPublished Jul 12, 2026Reviewed by Mohy MabroukUpdated Jul 12, 2026
Abstract editorial image of an evaluation plan notebook with blank replay rows and decision markers.
An evaluation plan should make the decision, risks, inputs, and thresholds visible. Generated editorial image.

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.

Diagram showing target behavior, input constraints, replay rows, thresholds, and decision owner.
The minimum plan connects target behavior to replay evidence and a decision owner. Diagram by EAVAE Labs.

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.