Tasks / Discover

Customer research call guide

Can the model write a guide that uncovers behaviour rather than opinions?

Measures the modelTask v1.0 · 2 casesDifficulty

What AI gets right here, and what you’ll still have to catch

From 9 graded outputs by 5 models. 50% were usable with at most a quick edit.

Reliably right

  1. Identifies material uncertainty100% pass
    Names specific unknowns (cost of lateness, ease of chasing, automation fit, willingness to pay) and says mixed findings mean targeted follow-up.
    GPT-6.1 Sol · API · Would freelancers pay to stop chasing invoices?
  2. Designed to falsify100% pass
    Every learning goal has disconfirming questions, e.g., 'What did you do next?', 'When do you avoid sending another reminder?'
    GPT-6.1 Sol · API · Would freelancers pay to stop chasing invoices?
  3. Asks what people did, not what they think100% pass
    Core questions ask for the most recent overdue invoice and specific actions taken, not opinions or predictions.
    GPT-6.1 Sol · API · Would freelancers pay to stop chasing invoices?

Where it slips

  1. Fits the call33% pass
    Timings add up to 30 minutes but no must-ask questions are marked and no guidance on what to cut if time runs short.
    GPT-6.1 Sol · API · Would freelancers pay to stop chasing invoices?
  2. Respects explicit constraints81% pass
    The guide is clearly over 900 words, violating the explicit length constraint.
    Opus 5.5 · Claude · Would freelancers pay to stop chasing invoices?
  3. Different questions for user and signer81% pass
    The guide does not provide distinct question tracks for the daily warehouse operations manager and the executive who signed; it only differentiates churned vs at-risk customers, with the same questions for both roles within each track.
    GPT-6 Luna · API · Why are warehouses leaving? Two execs, two theories

Case viewer

Read the brief, then put up to three outputs side by side, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're a PM at Paydeck. Next week, Mei (a designer) and Tomas (an engineer) will run 8 customer calls to test whether we should build Autochase next quarter. Neither has run a customer interview before. Write the call guide they'll use: the learning goals, the questions with rough timings for a 30-minute call, notes for first-time interviewers, who we should talk to, and what we'd need to hear to go ahead or to stop. Keep it under 900 words. Sam, the PM who owns Autochase, has drafted some questions and a recruiting plan. They're below with everything else we know.

About PaydeckInvoicing software for freelancers and small agencies (1 to 10 people). 48,000 accounts, 6,100 of them paying $12 a month for Pro.
The ideaAutochase: automatic, escalating reminders for overdue invoices, sold as a $10-a-month add-on. There is no prototype yet.
Sam's hypothesis“Late payments cost our users real money, and chasing them is painful enough that they'd pay $10 a month to automate it.”
What we know34% of invoices sent through Paydeck are paid after their due date; the median is 16 days late. The manual 'Send reminder' button was used by 12% of accounts in the last 90 days. 40 support tickets last quarter asked for automatic reminders.
Sam's draft questions1. How frustrating do you find chasing late payments? 2. Would an automatic reminder feature be useful to you? 3. How much would you pay for it: $5, $10 or $15 a month? 4. Do you think your clients would be annoyed by automated reminders? 5. What other features would you like us to build?
Sam's recruiting planEmail the 300 accounts that used the 'Send reminder' button most last month, and book the first 8 who reply.
What a strong answer does

A guide that splits Sam's hypothesis into goals that can each fail: do late payments actually cost users time or money, how do they deal with them today, would they pay to fix it, and does chasing harm client relationships. The core asks for the story of the last overdue invoice (what happened, what they did, how long it took, what it cost) and what they've already tried or paid for, instead of opinions or predictions. It replaces Sam's leading and hypothetical questions: no 'how frustrating', no 'would it be useful', no price menu; payment is tested through past spending or a real commitment (say, a paid pilot sign-up) at the end, after the problem questions and without pitching Autochase first. It fits 30 minutes with timings, coaches first-timers (ask for specifics, follow up with 'what happened next', stay quiet, don't pitch or defend), and fixes the recruiting plan: the heaviest reminder users will over-state demand, so it adds accounts with overdue invoices that never send reminders, and says who to exclude or balance. It ends with clear go and stop signals tied to what people did, such as most calls describing recent chasing that took real time plus a workaround they've tried or paid for, against most saying one nudge does the job.

Critical failures (cap the score)
  • Relies on hypothetical questions (would you use it, how much would you pay) as the main evidence for the decision
  • Describes or pitches Autochase before asking how the person handles late payments today
Case

v1.0 · synthetic · B2B SaaS, freelancers, willingness to pay, first-time interviewers

Critical failure: Relies on hypothetical questions (would you use it, how much would you pay) as the main evidence for the decision. Score capped at 40.

Needs more than a quick edit9 of 12 checks passed

A PM could use most of it with light edits, but the prohibited hypothetical price/trial questions and missing must-ask/cut guidance need correction before it is safe to run.

Output

Autochase customer call guide (30 minutes)

What we need to learn

  1. How often late payments happen, and what customers actually do when they do.
  2. Whether chasing creates meaningful time, cash-flow, or relationship costs—and how customers handle those today.
  3. When automatic reminders would help, and what control or safeguards customers would need.
  4. Whether the value could justify a $10/month add-on.

Who to talk to

Recruit 8 account owners or purchase decision-makers who send invoices through Paydeck and have had at least one late invoice in the last 90 days. Aim for: - 4 who used “Send reminder” and 4 with late invoices who rarely or never used it. - A mix of freelancers and small agencies, account sizes, and Pro/free plans.

Don’t recruit only the 300 most frequent button users: they’re likely to overrepresent people already motivated to chase. Ask about recent late invoices; don’t tell people the feature idea before the call.

30-minute call

0–2 min | Set the tone “Thanks for speaking with us. We’re learning how invoicing works for you; there are no right answers. I’m interested in what you’ve actually done, not what you think we want to hear. We’re exploring ideas, not selling a product.” Ask permission before recording; otherwise take notes.

2–8 min | Recent experience “Think of the most recent invoice that was paid late. Can you walk me through what happened, from sending it to getting paid?” Probe: When was it due and paid? When did you realize it was late? What did you do next? What happened?

8–15 min | Current workflow “What, if anything, did you do to follow up?” Probe: How did you decide when and how to contact them? What did you send, and through what channel? How often does this happen? What do you do when you don’t send a reminder? Have you tried tools or changed your process?

15–20 min | Impact and trade-offs “What effect did that late payment have on your work or business?” Probe: Time spent? Cash-flow impact? Client relationship? What was most frustrating—or not especially difficult? How do you prioritize which invoices to chase?

20–25 min | Explore the idea After hearing their story, say neutrally: “We’re exploring a paid add-on that sends escalating reminders automatically when an invoice becomes overdue. There isn’t a product yet.” “What’s your first reaction?” Probe: What would be useful or unhelpful? Which invoices, if any, would you trust it with? What would you need to control—timing, wording, recipients, stopping reminders? What situations would make you not use it?

25–28 min | Value and price “How do you currently spend time or money dealing with late invoices?” “If this cost $10 a month, how would you decide whether it was worth it?” Probe: What would it need to save or improve? Would you trial it at that price? Why or why not? Treat stated intent as a clue, not proof of demand.

28–30 min | Close “What have I not asked that matters about getting paid late?” Thank them. Don’t promise a launch or specific functionality.

Notes for first-time interviewers

  • Mei leads; Tomas takes notes. Agree beforehand who will ask follow-ups. Don’t interrupt or tag-team.
  • Let the customer finish. Use silence; ask “Can you tell me more?” or “What happened next?”
  • Ask about a specific past event before opinions or hypotheticals. Avoid “Would this be useful?”, “How frustrating is it?”, feature wishlists, and leading follow-ups.
  • Don’t defend the idea, pitch solutions, or share Paydeck’s late-payment statistics; these can bias answers.
  • Capture examples, exact phrases, workarounds, and contradictions—not just “yes/no.” Separate what people did from what they say they might do.

Decision after 8 calls

These calls are directional, not a market-size estimate. Go ahead with a narrowly scoped MVP/pilot next quarter if at least 5 of 8 describe recurring late-payment problems with concrete business or time costs, and at least 4 of 8 see a credible reason to try automation at $10/month and can explain the controls they’d need. Check that interest isn’t confined to one narrow user type.

Stop or defer if 2 or fewer describe meaningful recurring burden, most already handle it easily, or trust/client concerns make automation unacceptable even with reasonable controls. If evidence is mixed or concentrated in one segment, don’t commit broadly: refine the target or concept and test again.

Check by check

Got wrong · 1
  • Fits the callThe timings add to 30 minutes, but the guide does not mark must-ask questions or say what to cut if time runs short.
Mixed · 2
  • Respects explicit constraintsIt violates the explicit prohibition on asking people to predict what they would pay or use by asking whether they would trial it at $10/month.The two graders disagreed on this one.
  • Doesn't lead the witnessThe price section asks hypothetical willingness-to-pay/trial questions, and the idea section asks for first reactions to a described product, which are leading relative to the prohibited practices.The two graders disagreed on this one.
Got right · 9
  • Uses the supplied evidence correctlyThe output uses the supplied facts correctly and labels recruiting assumptions and likely bias as hypotheses rather than established facts.
  • Addresses the actual decisionIt gives clear go, stop/defer, and mixed-evidence decision rules tied to specific call outcomes.
  • Identifies material uncertaintyIt names key unknowns such as recurring burden, controls/trust concerns, and segment concentration, and says how calls would resolve them.
  • Avoids unsupported claimsIt avoids presenting unproven causes or demand as fact, using language such as “likely” and “directional.”
  • Produces the required deliverableIt provides a usable 30-minute call guide with learning goals, questions, timings, interviewer notes, recruiting guidance, and decision criteria within the length limit.
  • Talks to the right peopleIt explicitly avoids recruiting only the 300 heaviest reminder users and balances the sample with 4 reminder users and 4 non/rare users with late invoices.
  • Coaches first-time interviewersIt gives first-time interviewers concrete follow-up prompts, silence guidance, note-taking rules, and prohibitions on pitching or defending.
  • Designed to falsifyEach learning goal has questions that could falsify the hypothesis, such as asking what they actually did, what it cost, and what would make them not use automation.
  • Asks what people did, not what they thinkThe core questions ask for the most recent late invoice, what happened, what they did, and current time or money spent.

Claims the judge couldn’t find in the brief

  • The output’s recruiting filter assumes there are at least 8 account owners or purchase decision-makers with a late invoice in the last 90 days.
  • The heaviest reminder users are likely to overrepresent people already motivated to chase.

Grades and run details

Decision model 96 · LLM judge 9 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly66%
  • passAddresses the actual decision29%
  • passRespects explicit constraints19%
  • passIdentifies material uncertainty92%
  • passAvoids unsupported claims78%
  • passProduces the required deliverable95%
  • passTalks to the right people100%
  • passCoaches first-time interviewers100%
  • passDesigned to falsify85%
  • passAsks what people did, not what they think66%
  • passDoesn't lead the witness70%
  • partialFits the call32%
Run
Run
#1
API response time
31 s
Submitted
30 Sept 2026

Results

Every setup we’ve tested on this task, across all cases and repeats, graded on the current checklist. Provisional The checklist is still being calibrated against our PM.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT95.892.31None
2GPT-6.1 SolwithAPI92.192.62None
3Opus 5.5withClaude94.169.22None
4Sonnet 5.5withAPI86.484.62None
5GPT-6 LunawithAPI90.273.921 capped

About the task

The PM job

Preparing for customer calls.

Why it matters

Leading questions produce the answers you wanted. A good guide is designed to prove you wrong.

What good looks like

  • Past-behaviour questions
  • No leading prompts
  • Clear learning goals
  • Could disprove the team's hypothesis

Deliberately not measured

    Capability tested

    Question design

    The failure we’re looking for

    Hypothetical, leading questions

    Grading

    Decision model and LLM judge, calibrated against a blind PM review