Interview with user: best questions for user interview to collect deeper feedback with conversational AI surveys

Finding the best questions for user interview sessions can make or break your feedback collection efforts.

Great user interviews go past surface answers, uncovering the real needs, frustrations, and goals people hold back in ordinary surveys.

Here, I’ll share 20+ proven interview prompts organized by goal—and show you how AI-powered conversational surveys automatically probe for deeper, richer insights by following up intelligently with every user.

Why traditional user interviews fall short

Manual user interviews often demand a huge time investment from both the interviewer and participant. There’s back-and-forth scheduling, transcription work, and the headache of sifting through pages of messy notes just to find themes. These barriers can stifle your momentum—and limit how many voices you actually hear.

Consistency issues: Different interviewers ask different follow-up questions, or react differently to vague answers, leading to uneven data quality across sessions. Teams end up comparing apples to oranges rather than tracking meaningful trends from interview to interview.

Scale limitations: Even the most diligent team can only run interviews with a handful of users due to time constraints. That means you risk missing the diversity of experience across your customer base, and potentially designing for outliers, not the majority.

With AI-powered surveys—especially those that adapt question flow with automatic follow-ups—you get interview-quality insight at true scale. AI conversational surveys have been shown to deliver 200% more actionable insights because the bot can detect vague answers and dig deeper in real time [1]. That’s simply not possible with manual interviews, unless you have unlimited budget and time.

Interview questions to uncover user needs

Understanding what users truly need—not just what they say they want—is the cornerstone of any successful product. The questions below target jobs to be done, current workflows, and unmet needs.

• Exploring users’ core tasks and why they matter:

  What are the main tasks you use [product or service] to accomplish each week?

• Digging into jobs to be done:

  Can you describe a recent situation where you needed to solve a problem [our product addresses]?

  • AI Follow-up path: If the user response is vague, the AI can clarify:

    Can you walk me through exactly what you did, step by step?

• Mapping current workflows outside your tool:

  How did you manage this task before you started using our product?

• Probing for missing capabilities:

  Is there anything important you can’t currently do with our product?

  • AI Follow-up path: The AI might ask:

    How are you handling those needs today—are there workarounds or other tools involved?

• Investigating context and frequency of key jobs:

  How often do you need to solve this problem in a typical month?

• Benchmarking minimum viable needs:

  If you could change one thing about your workflow, what would it be?

• Testing for novelty and unmet needs:

  Is there something you wish existed, but have never found in any tool?

• Prioritizing urgent needs:

  Which of your daily tasks feels most frustrating or urgent right now?

Specific’s AI adapts follow-up questions live, zeroing in on detailed use cases you could easily miss in a scripted survey. If someone's answer is unclear, or they hint at a workaround, the AI asks clarifying questions automatically—thanks to automatic follow-ups feature. You can analyze the depth and themes of these responses using AI-powered survey analysis tools, so patterns pop out without hours of manual coding.

Example prompt for analyzing user needs survey:

Summarize the most repeated needs mentioned by users in their responses about our main features. Identify any patterns around jobs to be done and current workflow pain points.

Learn more about dynamic probing and follow-up customization in our AI follow-up questions guide.

Questions that reveal pain points and frustrations

Users rarely volunteer their biggest pain points unprompted—especially in static surveys or when they worry about offending you. Smart interviews use targeted prompts, then adapt based on signals like frustration, emotional language, or described workarounds.

• Surfacing persistent annoyances:

  What’s the most frustrating part of using [product or service]?

  • AI Follow-up path:

    Can you give an example of a time this happened recently?

• Finding broken flows or abandoned features:

  Is there any feature you tried but stopped using? Why?

• Spotting friction in the user journey:

  Have you ever gotten stuck, confused, or lost while using the product? Tell me more.

  • AI Follow-up path:

    What did you try to do next, and how did you eventually resolve it?

• Probing for things that slow users down:

  Are there steps in your workflow that take longer than you’d like?

• Capturing workaround detection:

  Have you come up with your own solution or “hack” to work around an issue with [product]?

  • AI Follow-up path:

    How effective has your workaround been, and what would make it unnecessary?

• Surfacing abandoned tasks:

  Was there ever something you tried to do with our product but gave up? What happened?

• Mapping emotions to friction points:

  Which part of the product leaves you feeling disappointed, frustrated, or stressed?

• Testing prioritization of pain points:

  If you could instantly fix one thing about the product, what would it be?

Workaround detection is where AI outshines scripted forms: when a user hints at a “hack,” AI follow-ups dig deeper, drawing out context, cost, or the trigger that led to inventing their own fix.

With conversational surveys, people disclose pain points more openly, in part because the interview feels more like a chat than an exam. Studies show AI-driven surveys deliver more informative, detailed responses and higher engagement than traditional forms [6]. To analyze and synthesize these pain patterns across many users, Specific offers powerful AI survey response analysis—so you can ask, “What’s blocking the most users right now?” and get clear, usable summaries.

Example prompt for analyzing pain point survey responses:

What are the top recurring frustrations identified in user interviews? List any common workarounds or feature requests mentioned in connection with these pain points.

Questions about desired outcomes and success

Not everyone defines “success” the same way—some care about speed, others care about collaboration, reliability, or accomplishment. The prompts below help you prioritize improvements and clarify what truly matters to users.

• Defining user success:

  What does a successful experience with [product or service] look like for you?

  • AI Follow-up path:

    How do you know when you’ve achieved that outcome?

• Exploring metrics and quantifiable results:

  Are there any numbers or indicators you track to measure your results?

• Understanding shorter- vs. longer-term value:

  What’s the biggest benefit you notice right after using our product? What about over weeks or months?

• Prioritizing desired improvements:

  If we could wave a magic wand and improve just one thing, what would make the biggest difference to you?

• Ranking tradeoffs and outcome priorities:

  When tradeoffs are needed, which is more important to you: speed, accuracy, ease of use, or flexibility?

  • AI Follow-up path:

    Why is that your top priority? Can you recall a moment when this made a difference?

• Probing for team/business impact:

  How has our product changed your work or your team’s results compared to before?

• Exploring what users would celebrate:

  If you achieved your ideal outcome with this product, what would you do or say?

Outcome prioritization is key—follow-up questions can force-rank user value signals, and success measurement prompts dig into the actual metrics or moments users care about. This goes far beyond “Would you recommend us?” and gets you tangible improvement targets.

Specific’s conversational approach makes it easier for users to articulate fuzzy or difficult-to-define outcomes by meeting them with clarifying follow-ups in real time. When users struggle to answer, AI gently nudges them (“Can you give an example?” or “Do you track this with a number or just a feeling?”) in a human way.

Example prompt for outcome analysis:

Analyze user interviews for top success indicators. Which outcomes get mentioned most frequently, and are there any metrics users use to measure product value?

Turning interview questions into conversational surveys

The best user interview happens when respondents feel heard, understood, and able to elaborate naturally—not just ticking boxes. To translate this experience to a survey, you should mix open-ended questions (for narrative and detail) with targeted, probing follow-ups (for clarification or quantification).

Follow-ups transform static surveys into engaging conversational surveys. Instead of just moving to the next question, the survey pivots based on the person’s answer, asks for examples, or probes for missing details. This is how every response becomes truly valuable data—not “noise”.

When setting up your AI interviewer, pick a tone that matches your brand and audience. Friendly and encouraging works well for most. You can create robust interview surveys almost instantly using the AI survey generator—just paste your prompts, and let the platform build the structure and flow. Want to tweak wording or add custom logic? Use the AI survey editor to make changes by chatting in plain language—the AI updates your survey instantly with your guidance.

There’s also plenty of flexibility in survey delivery: use standalone survey pages to invite users by email, Slack, or social channels, or in-product conversational widgets to collect feedback where people are already working. The context of delivery can make a massive difference—embedded surveys drive higher completion rates, while shareable links are great for one-off research projects or large-scale feedback drives.

Start collecting deeper user insights today

Transform how you gather feedback—don’t just collect surface-level answers, capture the full context of user experience with AI-powered conversational interviews. If you’re not running these surveys, you’re missing out on essential insights that could spark your next breakthrough, fix costly friction, and