ChatGPT prompts for UX research: 30+ templates for every research stage

ChatGPT will not run your UX research for you, but it will make the craft work around it dramatically faster. Drafting a research plan, writing non-leading interview questions, building a screener, clustering notes into themes, turning findings into a readable summary: these are exactly the tasks where a well-prompted AI saves hours. The catch is knowing the line, AI accelerates the work around research, but the actual evidence still has to come from real users.

This guide gives you 30-plus copy-paste prompts organized by research stage, plus clear guidance on what to trust, what to verify, and what AI simply cannot do. Adapt the bracketed placeholders to your project and you have a working prompt library.

Before you start: how to prompt well

Every prompt below works better when you give ChatGPT four things: a role, context, a specific task, and constraints. Tell it who it is (“You are a senior UX researcher”), what the situation is (audience, product, goal), exactly what you want, and any rules (format, length, avoid leading questions). Vague prompts produce generic, averaged output; precise prompts produce something usable. And keep sensitive participant data out of prompts, anonymize anything real before pasting it in.

Stage 1: Research planning

Draft a research plan

You are a senior UX researcher. I need to research [goal, e.g. why users abandon onboarding] for [product] used by [audience]. Draft a research plan including objectives, research questions, recommended methods, participant criteria, and a rough timeline. Flag any assumptions you are making.

Sharpen a fuzzy objective

My stakeholder wants to “understand our users better.” Help me turn this into 3 to 5 specific, answerable research questions for [product and audience], and note which method suits each.

Choose a method

For the research question “[question],” compare which method fits best among usability testing, interviews, surveys, and diary studies. Give the trade-offs for my context: [timeline, budget, audience].

Pressure-test a plan

Here is my research plan: [paste]. Act as a skeptical research lead and point out gaps, biases, or questions it will fail to answer.

Stage 2: Recruiting and screening

Write screener questions

Write a participant screener to qualify [target user, e.g. B2B product managers who run research] for a study on [topic]. Include questions that screen out people who do not fit, and avoid questions that telegraph the “right” answer.

Design red-herring screener items

Add 2 screener questions that detect participants who are exaggerating their experience or speeding through, without being obvious.

Define segments

Given my target audience [describe], suggest 2 to 3 distinct participant segments worth recruiting separately, and what each would tell me.

A good screener only matters if you can reach real, qualified people, which is the part AI cannot do. Pair these prompts with a real recruitment plan; our user interview guide covers how the screener feeds the session.

Stage 3: Interview and usability guides

Generate interview questions

You are a UX researcher. Write 10 open-ended, non-leading interview questions to understand [topic] for [audience]. Order them from broad to specific, and avoid yes/no and leading phrasing.

Convert leading questions

Rewrite these leading questions to be neutral: [paste questions].

Build a usability test script

Write a moderated usability test script for [product/flow]. Include a warm-up, 4 to 6 realistic tasks phrased as scenarios (not instructions), and follow-up probes for each.

Create follow-up probes

For the task “[task],” give me 5 follow-up probes I can use when a participant hesitates or goes quiet, without leading them.

Draft a discussion guide

Turn these research questions [paste] into a 45-minute interview discussion guide with timeboxes.

Stage 4: Synthesis and analysis

These work on data you have already collected, anonymize first.

Cluster notes into themes

Here are anonymized notes from [n] interviews: [paste]. Cluster them into themes, name each theme, and list supporting observations. Tell me where evidence is thin.

Find contradictions

Across these notes, where do participants disagree or contradict each other? [paste]

Summarize a single session

Summarize this anonymized transcript into key findings, notable quotes, and open questions: [paste].

Affinity mapping starter

Group these observations into an affinity map structure with categories and sub-groups: [paste].

Severity and prioritization

For these usability issues [paste], suggest a severity rating (cosmetic to critical) for each based on likely frequency and impact, and explain your reasoning so I can adjust.

A caution: AI synthesis can invent plausible themes that the data does not support. Always check its output against the real transcripts. It drafts; the researcher decides.

Stage 5: Reporting and communication

Write a stakeholder summary

Turn these findings [paste] into a one-page executive summary for product leaders: top 3 insights, implications, and recommended actions. Keep it concise and non-jargony.

Draft recommendations

For each finding [paste], suggest a concrete, actionable recommendation and which team would own it.

Create a presentation outline

Outline a 10-slide readout of this study [paste objective and findings] for a mixed audience of designers, PMs, and executives.

Tailor the message

Rewrite this insight [paste] three ways: for an engineer, a designer, and a CEO.

Where ChatGPT helps, and where it cannot

The prompts above all share a pattern: AI is excellent at the language-heavy craft around research, drafting, rephrasing, structuring, summarizing, and weak or dangerous at anything requiring real evidence. It cannot recruit participants, observe behavior, or validate a decision. Asking it to “act as a user” and answer your questions produces a plausible response with no predictive value, because it models averages rather than your actual users. The same limit applies to synthetic respondents: useful for exploration, never for validation.

The reliable workflow is a partnership. Use ChatGPT to plan faster, write better questions, and synthesize quicker, then collect the findings that matter from real, qualified participants. CleverX supports that side with an 8M+ verified B2B and B2C panel across 150+ countries, where participants are identity-verified and screened on professional and consumer attributes, so the screeners and guides you draft with AI get answered by people who genuinely match your audience. AI sharpens the instrument; real participants provide the signal. For AI applied to the interview itself rather than the prep, see AI-moderated interview platforms.

Conclusion

ChatGPT is a force multiplier for UX research when you use it for what it is good at: planning, drafting screeners and guides, synthesizing anonymized data, and communicating findings. Keep the bracketed prompts above as a starting library, give the model rich context, protect participant data, and review every output against reality. Then do the one thing AI cannot, put your questions in front of real, verified users, because that is where genuine insight comes from.