Test an AI feature's promises, uncertainty and user control

Define the feature's role in the task

Is the AI producing a draft, making a recommendation or taking an action? Those roles create different expectations. A suggested response should not be presented as if it has already been sent, and a completed action should not look like an editable preview.

Use an illustrative task with safe data, such as drafting a summary of a synthetic support case. Define what would count as useful output and what would require the person to intervene.

Inspect the promise before generation

Check whether the input requirements are understandable. Does the interface explain any meaningful limitation that affects the task? Can the person tell whether the model sees the selected item or a wider collection?

Do not infer the actual data boundary from a reassuring sentence. Technical verification should establish what is transmitted. The UX check establishes whether the visible explanation matches the intended behavior and gives the person an appropriate choice.

Give correction a real place in the journey

Use a controlled example where the output needs revision. Can the person edit, reject or regenerate it without losing the original context? Does a new output replace the old one in a way that remains understandable?

If the feature shows supporting evidence or citations, inspect whether the relevant material is available and actually supports the associated claim. A link icon alone is not evidence of factual correctness.

Check the boundary before action

Identify any step that sends, publishes, changes permissions or modifies a business record. The interface should make the consequence and required approval understandable before execution.

For a drafting feature, a useful test may stop before sending. For an action-capable feature, use explicit scope and safe fixtures. Do not allow the model's suggestion to expand the authorization of the test itself.

Avoid measuring trust by appearance

A polished response, confidence label or successful synthetic run does not establish that people trust the feature appropriately. Human understanding and reliance need their own research questions and evidence.

Report the observable experience: the promise shown, data explanation, correction controls and action boundary. Keep model-quality evaluation, safety testing and participant research separately identified where the product needs them.

The best outcome is an experience in which the person can understand the feature's role, inspect what matters and retain meaningful control over what happens next.

Next step: Read AI usability testing: evidence and limits before turning an AI interaction review into broader claims about users.