Best questions for ux user interviews: how to run a user interview in ux for deeper insights

Running a user interview in ux research? The questions you ask determine the insights you’ll uncover.

We’ve compiled the 25 best questions for UX user interviews—covering every stage from understanding user context to feature validation—plus AI-powered follow-ups that probe deeper for truly actionable answers.

Questions to understand your users' context

Before any UX deep-dive, I always start by anchoring the conversation in the user’s reality. Here are foundational questions every solid interview should include:

• Question 1: "Can you describe your current role and responsibilities?"
  AI follow-up: "Are there any aspects of your role that influence how you use this product?"

• Question 2: "How long have you been using products like this?"
  AI follow-up: "What was your very first experience with this type of product like?"

• Question 3: "What tools do you regularly use as part of your workflow?"
  AI follow-up: "Is there a particular feature in those tools you wish you had here?"

• Question 4: "Can you tell me about a typical day at work?"
  AI follow-up: "When do you typically interact with our product during your day?"

• Question 5: "What experience level do you have with similar software?"
  AI follow-up: "Have you taught others how to use similar tools before?"

Example: Setting up AI follow-ups in the editor

Prompt: "After this question, if the user answer sounds generic or vague (‘I have experience’), please ask ‘Could you give a specific example of a challenge you faced with this software?’"

AI follow-ups adapt to what the user actually says, enabling a flow that feels less like a checklist and more like a natural conversation in the AI Survey Editor. It’s no surprise that conversational AI prompts drive higher data quality and richer answers—studies show AI-powered interviews collect responses that are more relevant and specific than traditional surveys. [1]

Questions to uncover pain points and workflows

Diving into pain points isn’t just about complaints—it’s about mapping how users actually work, what slows them down, and where the real friction lies. For the best insights, careful probing is essential.

• Question 6: "Walk me through how you complete [core task] using our product."

• Question 7: "What’s the most frustrating part of your current workflow?"
  AI follow-up: "When did you last encounter this frustration?"

• Question 8: "Are there any workarounds you use regularly?"
  AI follow-up: "How effective is this workaround in solving the problem?"

• Question 9: "How often do you run into problems or errors?"

• Question 10: "Can you describe a recent time when you felt stuck or slowed down?"
  AI follow-up: "What would have helped you get unstuck faster?"

• Question 11: "How critical are these issues to your work or goals?"

• Question 12: "What’s your usual first step when a problem comes up?"

Mini-table: Surface-level response vs. AI-probed insight

Surface-level Response	AI-probed Insight
"The app crashes sometimes."	"The app crashes every Monday morning when uploading a CSV, so I delay reporting until Tuesday."
"Sometimes it's slow."	"Performance drops when I work with files larger than 10MB, forcing me to split the files."

These questions truly excel in in-product conversational surveys, when delivered the moment a user completes a relevant action. Combined with AI, they don’t just record what happened—they capture how often, how severe, and what real impact it has. Remember, chatbot-driven surveys can boost participation by up to 70% compared to forms. [2]

Questions to explore user goals and motivations

If I want to steer product strategy, I dig into what users truly hope to achieve. These questions channel the jobs-to-be-done approach and reveal deep motivation:

• Question 13: "When you started using our product, what did you hope it would help you accomplish?"

• Question 14: "What’s the most important outcome you’re looking for when you use our tool?"
  AI follow-up: "Why is this outcome important to you personally or professionally?"

• Question 15: "Is there a goal you haven’t yet achieved with our product?"

• Question 16: "Are you using any other tools to meet the same goal?"
  AI follow-up: "What’s missing in our product that those tools provide?"

• Question 17: "If you could wave a magic wand and fix one thing about your experience, what would it be?"

• Question 18: "What keeps you coming back to our product—or what would get you to use it more often?"

Example AI follow-up prompt:

"If the user states a goal like ‘finish tasks faster,’ ask: ‘What currently slows you down most when working towards this goal?’"

Understanding user motivation isn’t just empathetic—it helps teams prioritize features and improvements that truly matter. These questions work especially well as part of conversational survey pages for broader strategic research. AI is especially handy for distinguishing between “I want this feature” and “I actually need to accomplish X.” AI-driven personalization can improve user engagement by up to 80%, unlocking deeper answers. [3]

Questions to validate features and test concepts

I always stress that feature feedback gains value in the context of real-world use. Instead of asking “Would you use X?” probe for specifics about how, why, and when features fit (or don’t).

• Question 19: "Have you used [feature/concept] before? If so, how did it impact your work?"
  AI follow-up: "Can you recall the last time you used it and what you achieved?"

• Question 20: "If you had access to this new feature, how often would you use it?"
  AI follow-up: "What would make you choose this over your current workaround?"

• Question 21: "Is there a scenario where this feature wouldn’t work for you?"

• Question 22: "Would this feature change the way you collaborate with others? How?"

• Question 23: "Have you considered switching to another product because of missing or broken features?"

AI-powered dynamic probing goes further, surfacing details we often miss when users simply say “it’s fine” or “it’s useful.”

With Specific, AI can dig into both positive and negative first impressions, prompting for edge cases (“When would this feature not be helpful?”) and use-case specifics. This results in reliable, actionable insight—not just wishful thinking.

Setting up your UX interview survey for maximum insights

The way you deliver a survey is as important as the questions themselves. For in-depth product experience, use in-product surveys triggered by user actions; for more reflective or strategic feedback, try landing page surveys shared post-interaction.

• Example AI prompt for deeper follow-ups:

  "If the user gives a one-word answer, ask them to elaborate by sharing a personal story or describing a specific scenario."

• Conditional probing for negative feedback:

  "If the answer contains frustration or dissatisfaction, ask: ‘What impact has this had on your work or goals?’"

• Probing for feature suggestions:

  "When a suggestion is made, ask: ‘How would this feature fit into your current process?’"

I recommend keeping interview surveys concise—10–20 minutes max, 7–12 primary questions, and the rest handled by AI follow-ups. Start broad (user background), then narrow in on pain points, goals, and finally features. Want to start from scratch? Use the AI survey generator to create your own interview flow instantly.

When your survey uses follow-ups, it transforms into a genuine conversational survey—increasing both participation rates and the richness of the answers you’ll receive.

For in-product delivery, target users immediately after relevant activity—for example, after completing a key workflow or experiencing an error. This delivers high-quality, in-context feedback right when it matters most.

Turning UX interview responses into actionable insights

Raw responses don’t create impact until they are synthesized, compared, and shaped into decisions. This is where Specific’s AI survey response analysis engine shines. AI finds themes across hundreds of conversations, not just the loudest voices.

My favorite analysis prompts include:

"Summarize the top three recurring UX pain points mentioned by users with less than six months of experience."

"What unmet needs are repeatedly highlighted by users who also use competing tools?"

"List three feature requests mentioned by users who reported high frustration."

You can segment responses by cohort, feature usage, or behavioral triggers for even more targeted understanding. Don’t be afraid to spin up multiple analysis threads to explore retention barriers, pricing feedback, or power-user behaviors independently. AI chat transforms the analysis process—helping you uncover unexpected themes, spot areas of friction, and galvanize your next roadmap sprint.

Start conducting better UX interviews today

Combining thoughtful user interview questions with AI-powered follow-ups delivers insights no static survey can match. Depth is what drives breakthrough UX research, not just breadth.

Conversational surveys transform the way you understand your users—surfacing motivations, struggles, and aspirations hidden in traditional forms. AI follow-ups surface angles and stories you’d otherwise miss.

Ready to unlock sharper UX insights? Create your own survey and start building with Specific.