40 Questions UX Designers Should Ask Before Trusting AI Outputs
When Dovetail AI summarises your user research or Figma AI suggests a component pattern, you face a choice between speed and certainty. The questions you ask before accepting these outputs determine whether you are designing for real users or for the models that summarise them.
These are suggestions. Use the ones that fit your situation.
Questions About Research Synthesis and Insight Loss
1When Dovetail AI grouped these 47 research quotes into five themes, which quotes did it exclude and why might they matter to your design?
2The AI summary says users want 'simplicity', but what specific friction or complaint led to that word appearing in the original research?
3Did the AI flatten a contradiction between what users said and what they did, and if so, which behaviour should your design address?
4Your research included users who abandoned a task halfway through. Does the AI summary explain why they left or only that they did?
5When you read the original research notes yourself, what insight jumps out that the AI summary buried or missed entirely?
6The AI identified your users' stated needs, but where in the raw research do you see the needs they could not articulate?
7If you removed the five highest-volume quotes from the AI summary, would the remaining insights change your design direction?
8The AI grouped feedback from 12 users into one theme. Did those 12 users actually describe the same problem or different versions of it?
9What emotional tone or frustration level did the original research contain that the AI summary reduced to neutral language?
10Which user segment does the AI summary treat as an outlier, and could that segment's behaviour reveal a genuine need rather than noise?
Questions About Pattern Adoption and Design System Defaults
11Figma AI recommended this component pattern. Did it recommend it because it solves your specific user problem or because it appears in its training data?
12The AI suggested a three-step modal flow. What principle about your users' cognitive load or context of use does that pattern actually address?
13Before accepting this design system pattern, what did you test with your actual users versus what you assumed would work?
14This interaction pattern appears in the AI suggestion. Does your accessibility audit show it works for users with motor or cognitive differences?
15The AI borrowed this pattern from high-traffic apps. Are your users' goals and constraints similar enough to those apps' users to make the pattern relevant?
16If you removed this AI-recommended pattern and designed from your user research alone, what would you build instead?
17The pattern works well at scale. Does your user base have the same scale, literacy level, or technical comfort as the population where the pattern was proven?
18When the AI suggested this component variation, did it show you where and why this pattern was adopted in other products?
19Which assumption about your users' behaviour does this AI-recommended pattern embed, and have you actually verified that assumption?
20The design system pattern is efficient to implement. Is efficiency the primary goal for this moment in the user journey or is it something else?
Questions About AI-Generated Research Synthesis and Usability Judgment
21Maze AI reported that 73 percent of users completed the task. Did it flag which 27 percent did not and what they did instead?
22ChatGPT identified your users' mental model of this feature. When you tested it, did users actually think about it that way?
23The AI summary says users found this flow 'intuitive'. Did it identify which specific steps felt intuitive and which ones required the user to stop and think?
24Adobe Firefly generated five variations of this design. Which one did you test with users and what did you learn that the visual alone did not show?
25The AI analysis flagged a usability problem. Have you watched a user encounter that problem or are you relying on the AI's interpretation of behaviour data?
26When the AI rated this design's accessibility compliance, did it test with assistive technology or report what the code technically supports?
27The synthesis says users perform action X most frequently. Does frequency mean it matters most to them or does it mean your interface makes it easiest?
28ChatGPT predicted that users would struggle with this terminology. What actual user language from your research supports or contradicts that prediction?
29The AI identified a usability bottleneck. Before redesigning, have you confirmed whether users are aware of the bottleneck or whether the AI detected something users have accepted?
30Dovetail's summary clusters user feedback into sentiment. Within the 'frustrated' cluster, are users frustrated by the same thing or by different aspects of the experience?
Questions About Maintaining Empathic Imagination and Design Ownership
31When did you last sit with contradictory user feedback long enough to discover what it was really telling you about your design?
32The AI recommended a solution. Can you articulate why this solution serves your user better than three alternatives you considered and rejected?
33If ChatGPT had never generated suggestions for this feature, what would your instinct tell you a user actually needed?
34Your research showed users struggling with this task. Before the AI suggested a solution, what did you imagine the design could do differently?
35The AI pattern is efficient. Have you deliberately explored a less efficient design that might build more user understanding or confidence?
36Which user insight from your research has sat with you for days because it troubled you or did not fit neatly into any pattern?
37When you disagreed with the AI's recommendation, did you pursue your instinct or did you defer to its analysis?
38Figma AI generated a design you find yourself using without modifying. What would change if you redesigned it from scratch without the AI suggestion?
39You observed a user behaviour that surprised you. Did you report it to the AI analysis tool or did you first spend time wondering what it might mean?
40Who in your team is developing the skill of sitting with ambiguity long enough to find genuine insights rather than convenient answers?
How to use these questions
Before accepting a Dovetail AI insight summary, read 10 raw research quotes with your own eyes. Notice what the summary preserved and what it compressed.
When Figma AI suggests a pattern, ask your team to design the interaction a different way using only your user research. Compare both versions.
Test AI-generated design variations with real users, not by reviewing the visuals yourself. Your expert eye will miss the friction that a non-designer encounters.
Track which design decisions you made because of an AI recommendation versus which ones you made because your research and instinct aligned. Look for patterns in what you deferred.
Reserve one research insight each project cycle that contradicts your AI tools' analysis. Design against it deliberately. You might discover something neither you nor the AI saw coming.