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01Insights / technical note

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
Evidence diagramEAVAE Labs diagram
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.
03Field note 1

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.

Evidence diagramEAVAE Labs diagram
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.
04Field note 2

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.

05Field note 3

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.

09Safe first step

Turn this evaluation pattern into an inspectable release decision.

Share the workflow boundary, the failure pattern, and the decision your team needs to make. The first brief should use sanitized context only.

No credentials, production data, customer records, or private repository access in the first brief.

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