Adding AI to a product changes the experience as well as the technology. The feature needs to explain what it can do, make uncertainty visible, and give people a clear way to check or correct the result. Trust grows through predictable behavior and useful controls.
Give the feature a narrow job
Describe the moment when the feature appears and the task it supports. Does it help someone find information, draft a response, or organize a set of options? A specific purpose makes the interface easier to explain and the behavior easier to evaluate.
Show what the result is
People should be able to tell when content is generated or suggested by AI. Use language that describes what the feature did, rather than implying that every answer has been verified. If a source can be shown, help the user reach it. If the feature is uncertain, design for that uncertainty.
Leave room for human control
Let users edit, reject, or retry a suggestion when the workflow calls for it. For actions that affect records, money, access, or other people, make the final step explicit. The right control depends on the risk of the task, not on how impressive the model appears.
Evaluate real conditions
Test the feature with the language, data quality, and edge cases people actually encounter. Review accuracy and usefulness alongside failure behavior: what happens with missing context, conflicting sources, or an unavailable service?
- Define examples of acceptable and unacceptable output.
- Check how the interface communicates limitations.
- Include accessibility and mobile use in product testing.
- Provide a route for feedback and correction.
Plan ownership after release
A feature needs someone to monitor feedback, update its source information or instructions, and respond when the workflow changes. Treat evaluation as ongoing product work. A feature that stays understandable and correctable can earn its place over time.