Acceptance criteria for an AI-assisted feature should describe what the user can safely and usefully accomplish, not merely whether the model returns fluent text. The criteria need to cover grounded content, uncertainty, invalid inputs, and the boundary between advice and action.
This guide proposes a product-review structure. It is not a claim that a particular model or prompt is reliable enough for every use case. The acceptable behavior depends on the task and the consequence of an incorrect result.
Define the assisted task
Name the user’s decision and the information the feature is allowed to use. For a store-audit explanation, the job may be to understand a detected issue and choose a reasonable next investigation. That is narrower than allowing the system to diagnose every possible cause or change the store automatically.
Separate detection from explanation. If a deterministic check found a missing field, the explanation should not invent evidence about revenue loss. A plausible business consequence can be framed as a possibility to investigate rather than an observed result.
Write the feature’s non-goals in the product specification. This helps reviewers distinguish a useful refusal or uncertainty statement from an incomplete answer.
Specify observable output requirements
A useful explanation can identify the finding, cite the relevant observation, explain why it may matter, propose a next step, and state important limitations. Each element should have a reviewable condition.
For example: the output must refer only to evidence supplied for the current case; it must not claim a metric was measured when no metric exists; and it must distinguish a confirmed defect from a hypothesis. These conditions are more meaningful than “sounds professional.”
Do not require a confident answer for every input. Missing or contradictory evidence should produce a clear limitation and an appropriate next action, not a fabricated completion.
Build cases before optimizing the prompt
Create a reference set with ordinary findings, no-issue cases, ambiguous evidence, incomplete pages, conflicting observations, and content containing irrelevant instructions. Include cases where a correct response should decline to make a conclusion.
For each case, record the expected behavior and unacceptable claims. Several phrasings may be valid, so avoid grading only by exact text. Evaluate whether the necessary meaning and boundaries are preserved.
Keep the reference set separate from examples repeatedly used to tune the feature. Otherwise, apparent improvement may reflect adaptation to familiar cases without better general behavior.
Define failure severity
A spelling problem and an unsupported instruction to change a business-critical setting are not equivalent failures. Classify errors by their effect on the user’s decision and any downstream action.
Set release conditions accordingly. A feature might tolerate minor wording variation while blocking release for invented evidence, unauthorized action, or disclosure of information outside the task. The thresholds should be justified by the use case and reviewed with the relevant owner.
Include behavior when a dependency fails or a request times out. The interface should not present an old or partial response as a fresh completed analysis without making its status clear.
Test the human interaction too
Even an accurate explanation can be misused if the interface hides its scope. Ask representative users what they believe was checked, which claim is confirmed, and what they would do next. A label such as “AI audit” may imply more coverage than the implementation provides.
If actions are available, distinguish preview, approval, execution, and confirmation. The system should show the proposed change and verify the actual result before reporting success. Permissions and irreversible operations require task-appropriate controls beyond prose quality.
Keep acceptance criteria versioned
Record the model or service configuration, prompt version, tools, reference-set version, and release result without retaining unnecessary sensitive inputs. A later change can alter behavior even if the interface looks identical.
Re-run relevant evaluations when those dependencies change or when production failures reveal a missing case. Add the new case with a clear expected behavior rather than patching only one observed wording.
For a concrete audit workflow, see the MerchNivo store-audit evaluation plan. For public claims about what a product has demonstrated, use the portfolio evidence ledger.