Practical guide
AI Product Evaluation Plan: What to Test Before Launch
Build an AI product evaluation plan with acceptance criteria, representative cases, failure boundaries, human review, and launch decisions.
Last updated
2026-09-14
Why evaluation comes before launch
A product can appear impressive in a demonstration and still fail in ordinary use. Evaluation turns a promise into a set of observable expectations and test cases.
For AI products, evaluation should cover usefulness and failure. It should identify what the system can do, where it is uncertain, who reviews it, and what decision follows when results do not meet the standard.
What to show in your work
Choose one feature and define its user, outcome, acceptance criteria, test set, review boundary, and launch recommendation. Include examples that are incomplete, ambiguous, or deliberately difficult.
Iteretta's AI Product Management lab teaches problem framing, evaluation, build-versus-buy decisions, responsible adoption, and evidence-led product communication.
A practical evaluation plan
- 01Outcome: define what useful means for the user and business.
- 02Cases: select representative, difficult, and unsafe examples.
- 03Criteria: state what must be correct, complete, timely, or understandable.
- 04Review: define the human checks and disagreement process.
- 05Decision: set the evidence needed to pilot, improve, defer, or stop.
Common questions
Is an evaluation plan the same as a model benchmark?
No. A benchmark may measure model performance in a controlled setting. A product evaluation plan connects quality to a user workflow, business outcome, operating constraints, and review process.
Can I create an evaluation plan without building a model?
Yes. Product judgement includes defining what should be tested before implementation. You can create a useful plan using representative examples and explicit criteria.
This resource is maintained by Iteretta. It is educational information, not legal, financial, medical, employment, or other professional advice.