Tasks / Product Craft

Customer research call guide

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

Task type v1.0 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

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

From 18 graded outputs by 9 models. 33% were usable with at most a quick edit.

Reliably right

  1. Asks what people did, not what they think96% pass
    Core questions ask for specific recent actions and events ('What happened next?', 'Who did you call?'), not opinions or predictions.
    Sonnet 5.5 · API · Why are warehouses leaving? Two execs, two theories
  2. Tests both theories fairly92% pass
    Both theories get questions that could disprove them (e.g., no action on robotics, happiness after slow go-live), and open-ended timeline questions leave room for a third cause.
    Sonnet 5.5 · API · Why are warehouses leaving? Two execs, two theories
  3. Different questions for user and signer92% pass
    The guide has distinct tracks: daily user questions focus on day-to-day experience and workarounds, signer questions on the decision process and alternatives.
    Sonnet 5.5 · API · Why are warehouses leaving? Two execs, two theories

Where it slips

  1. Marks what to cut if the call runs over28% pass
    The guide includes timed sections that add up to 45 minutes but does not say what to cut if time runs short, nor does it mark must-ask questions.
    GPT-6.1 Sol · API · Why are warehouses leaving? Two execs, two theories
  2. Doesn't trust the default reason47% pass
    The plan names the pre-selected default as a bias but does not use the behaviour columns to show that most 'too expensive' cancellers had stopped cooking; it only picks two contrasting individuals without the aggregate pattern.
    GPT-6 Astra · ChatGPT · Too expensive, by default
  3. Uses the supplied evidence correctly61% pass
    The claim 'Customers will say what helps their negotiation' is an unsupported behavioural statement not derived from the context.
    Haiku 5.5 · API · Why are warehouses leaving? Two execs, two theories

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

  1. Designed to falsify

    For each learning goal, does the guide include a question whose honest answer could show the team's hypothesis is wrong?

    Passes when Every learning goal has at least one disconfirming, non-leading question, such as asking about the last time they had the problem and what they did instead.

  2. Asks what people did, not what they think

    Are the core questions about specific things the person actually did recently, rather than opinions, predictions or hypotheticals?

    Passes when The core of the guide asks for recent, specific instances (the last time, what happened, what they did) and follows up on them; opinions and predictions are absent or clearly secondary.

  3. Doesn't lead the witness

    Are the questions free of leading wording, of pitching the idea, and of hints at the answer the team hopes for?

    Passes when Questions are neutral and open, and the idea is not described (or only at the very end, after the problem questions).

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · Marks what to cut if the call runs over · and 2 written for each task, which you’ll see in the tasks below.

The tasks

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

The brief

You're the PM for retention at Sprig. The execs each have a theory about why subscribers cancel (below). Plan next week's six churn interviews: who to recruit from the cancellation list and why, and the guide the interviewers will use, in no more than 900 words. Two of the interviewers are new to it. The files are attached.

What the model was given5 items: About Sprig, exec_hypotheses.md, cancel_form.md, cancellations_last_quarter.csv, research_constraints.md
About SprigA meal-kit subscription: recipes and ingredients delivered weekly. Subscribers can skip a week or cancel any time.
exec_hypotheses.md7 lines · Download
# What the execs think (for the research)

- CEO: "It's price. Competitors discount harder."
- Head of Food: "Our recipes are too repetitive after a few months."
- Head of Ops: "Late deliveries."

Retained subscribers (for comparison): 81% cooked at least 3 recipes a week in their first month; 34% of cancellers did.
cancel_form.md3 lines · Download
# The cancel form

A single-choice list, in this order: "Too expensive" (selected by default), "Recipes got repetitive", "Moving / travelling", "Delivery problems", "Other". Free text is optional.
cancellations_last_quarter.csv300 rows · Download
cancellation,cancel_reason_selected,weeks_subscribed,deliveries_skipped_last_6_weeks,recipes_cooked_last_4_weeks,free_text
S-8000,Recipes got repetitive,47,3,0,kept piling up in the fridge
S-8001,Recipes got repetitive,16,3,2,too much food for two
S-8002,Moving / travelling,33,2,5,cheaper at the supermarket
S-8003,Too expensive (pre-selected),29,3,2,kept piling up in the fridge
S-8004,Too expensive (pre-selected),11,5,0,
S-8005,Too expensive (pre-selected),20,5,0,too much food for two
…
research_constraints.md4 lines · Download
# Research constraints

- Six calls, 30 minutes each, next week. The researcher can recruit from the cancellation list (consent to contact is on file for all of them).
- Two of the PMs running calls are new to interviewing.
What a strong answer doesThe answer key the graders mark against

Reads the cancellation data before trusting it: 'Too expensive' is the top reason (156 of 300), but it's the pre-selected first option, and 118 of those 156 had skipped two or more deliveries and cooked two or fewer recipes in the last four weeks: they'd stopped using the product before they left. Overall 204 of 300 skipped two or more deliveries. So the guide investigates what happened to the cooking habit, and price as one hypothesis to test rather than the answer. Recruits deliberately from the list across the patterns: people who stopped cooking and picked 'too expensive', people who kept cooking and still picked 'too expensive' (the real price cases), and people who said recipes got repetitive. The guide asks about specific recent weeks (the last box they skipped, what happened to the food, what they ate instead), each exec hypothesis has a question whose honest answer could disprove it, questions don't lead or mention price first, the guide fits 30 minutes with timings and marks what to cut, and gives the new interviewers prompts for digging deeper. Notes that the default on the cancel form should be removed.

Critical failures (cap the score at 40)
  • Builds the guide around confirming that price is the reason
Task

Staff-level · v1.2

The outputs

9 setups answered this brief.

Needs more than a quick edit8 of 12 checks passed

The 79% statistic is incorrect and would corrupt the analysis; a PM would need to correct it before using the output.

Output

Sprig Churn Research Plan & Interview Guide

Why Our Current Data is Misleading

Before recruiting, we must look at how our exit survey distorts reality: 1. Default Bias: "Too expensive" is pre-selected. Half our cancellers leave it checked, but their free-text responses and behavioral data tell a different story: 79% of cancellers cooked 3 or fewer recipes in their final 4 weeks, and most skipped multiple deliveries. 2. The Value-Perception Trap: When food goes uncooked ("kept piling up in the fridge", "didn't have time to cook", "too much food for two"), the effective cost-per-meal skyrockets. Subscribers aren't necessarily leaving because of sticker price or competitor discounts; they are leaving because paying for food you end up throwing away feels expensive.

Our 6 interviews will test the execs' hypotheses (Price, Recipe Fatigue, Delivery) against this underlying Utilization/Lifestyle Fit problem.

---

Recruitment Plan (6 Participants)

Note: Invite 2–3 backups per slot using the backup IDs provided.

1. The "Default Price" Low-Utilizer (Tests CEO Hypothesis vs. Waste) * Recruit: S-8016 (Backup: S-8029) — 14 weeks, "Too expensive (pre-selected)", skipped 4/6, cooked 0, free text: "kept piling up in the fridge". * Why: Represents our largest churn segment. Left the default reason checked, but stopped cooking entirely and threw food away. Tests if "price" actually means "wasted food."

2. The High-Usage Price Chiller (Tests True Price Sensitivity) * Recruit: S-8056 (Backup: S-8096) — 29 weeks, "Too expensive (pre-selected)", skipped 1/6, cooked 12, free text: "cheaper at the supermarket". * Why: Cooked 3 meals/week right up to cancellation (matching our retained user benchmark) with zero waste, yet still felt it was too expensive. Tests the CEO's hypothesis on a user who actually got full utility.

3. The Long-Tenure "Bored" Subscriber (Tests Head of Food Hypothesis) * Recruit: S-8057 (Backup: S-8246) — 53 weeks, "Recipes got repetitive", skipped 4/6, cooked 3, no free text. * Why: Actively changed the dropdown to "Repetitive" after a year of loyalty and recent skipping. Tests genuine menu fatigue vs. seasonal routine changes.

4. The Time-Poor "Repetitive" Subscriber (Tests Menu Fatigue vs. Cooking Effort) * Recruit: S-8022 (Backup: S-8013) — 20 weeks, "Recipes got repetitive", skipped 4/6, cooked 3, free text: "didn't have time to cook". * Why: Selected "repetitive" at 5 months, but wrote they had no time. Did the flavors get boring, or did the 45-minute prep work become a chore?

5. The "Too Much Food" Down-Sizer (Tests Plan Flexibility) * Recruit: S-8006 (Backup: S-8240) — 29 weeks, "Too expensive (pre-selected)", skipped 0/6, cooked 8, free text: "too much food for two". * Why: Consistently cooked 2 meals/week without skipping, but felt overwhelmed by volume. Tests if inflexible portion/box sizes are driving "price" churn.

6. The Early-Tenure Ops Failure (Tests Head of Ops Hypothesis) * Recruit: S-8097 (Backup: S-8152) — 2 weeks, "Delivery problems", skipped 2/6, cooked 10, no free text. * Why: High intent (cooked 10 meals in a very short window) but churned immediately citing delivery. Isolates pure operational failure during the critical first-month habit-building window.

---

30-Minute Interview Guide

Golden Rules for New Interviewers

  • Don't ask about the future or hypotheticals: Never ask "Would you have stayed if it were 20% cheaper?" (People always say yes). Ask what actually happened.
  • Ask for stories, not opinions: Instead of "How were the recipes?", ask "Walk me through the last box you unpacked."
  • Don't lead the witness: Do not mention their survey selection ("I see you cancelled because of price"). Let them bring it up.
  • Embrace silence: Count to 5 in your head after they finish speaking. They will often fill the silence with the real truth.

1. Intro & Warm-up (3 mins)

  • "Hi [Name], thanks for joining. I'm [Name] on the product team at Sprig. We're talking to former subscribers to understand how Sprig fit into your life and where it fell short. There are no right or wrong answers—criticism won't hurt my feelings, it actually helps us improve. Do you mind if I record this just for internal notes?"

2. The Origin & Early Habit (5 mins)

  • Goal: Understand the job they hired Sprig to do and their first-month habit.
  • "Take me back to when you first signed up for Sprig. What was going on in your life that made you want to try it?"
  • "In that first month, what did a typical week with a Sprig box look like?"

3. The Last Box (10 mins) — CRITICAL SECTION

  • Goal: Uncover the behavioral reality of food waste, prep time, and portion sizes.
  • "Think about the very last Sprig box that arrived at your door. Walk me through what happened to that box from the moment you brought it inside."
  • Follow-up: "How many of those meals did you end up cooking?"
  • If meals went uncooked: "What got in the way of cooking the others? What ended up happening to those ingredients?" (Listen for: guilt of throwing away food, fridge clutter, unexpectedly busy evenings).
  • "Looking back at the last month or two before you cancelled, I noticed you skipped a few weeks. How did you decide whether to get a box or skip?"

4. The Trigger & Executive Probes (9 mins)

  • Goal: Pinpoint the exact moment of cancellation and test exec hypotheses without leading.
  • "What was the exact moment you decided, 'Okay, I'm going to cancel my subscription today'?"
  • Use the relevant probe below based on what they bring up (or their profile):
  • If they mention Price / Value: "When you felt it wasn't worth the cost, how were you doing that math? Did you switch to another meal kit, or what are you doing for dinners now?"
  • If they mention Recipes / Food: "When you logged in to pick recipes toward the end, what was going through your head? Was it the flavors, the ingredients, or the time/effort to cook them?"
  • If they mention Volume / Leftovers: "How did the amount of food in the box match up with what your household actually eats?"
  • If they mention Delivery: "Tell me specifically what happened with the delivery. How did that impact your plans for the week?"

5. Wrap-up (3 mins)

  • "If a friend asked you today whether they should try Sprig, what would you tell them?"
  • "Is there anything else about your experience we didn't cover? Thank you so much for your time!"

Claims the judge couldn’t find in the brief

  • 79% of cancellers cooked 3 or fewer recipes in their final 4 weeks

Grades and run details

Decision model 67 · LLM judge 62
Decision model checks
  • failUses the supplied evidence correctly18%
  • partialAddresses the actual decision41%
  • passRespects explicit constraints18%
  • partialIdentifies material uncertainty31%
  • failAvoids unsupported claims19%
  • partialProduces the required deliverable25%
  • passDoesn't trust the default reason83%
  • passRecruits from the data100%
  • passDesigned to falsify52%
  • passAsks what people did, not what they think98%
  • passDoesn't lead the witness49%
  • partialMarks what to cut if the call runs over34%
Run
Run
#1
API response time
21 s
Submitted
13 Oct 2026

Needs more than a quick edit10 of 12 checks passed

The output fails to demonstrate the default-option bias with data from the file and does not mark cuttable questions for the 30-minute call, so a PM would need to add those before use.

Output

Aim and guardrails

Use six interviews to identify how cancellation happens, not vote on which executive is right. Explore price, repetition and delivery alongside an alternative: subscribers stop cooking the meals, food accumulates, and the subscription loses value.

The cancellation form is weak evidence: “Too expensive” is pre-selected, only one reason is allowed, and comments often suggest overlapping problems. The first-month cooking difference—81% of retained subscribers versus 34% of cancellers cooked at least three recipes weekly—is a useful lead, not proof that cooking prevents churn. Last-four-week cooking counts cannot establish first-month habits.

Recruit these six

Purposefully sample contrasting behaviors and mismatches between selected reasons and comments. Counts below are weeks subscribed / skips in last six weeks / recipes cooked in last four weeks.

RecruitEvidenceWhy this person
S-805629 / 1 / 12; default price; “cheaper at the supermarket”Relatively active cook with an explicit price comparison. Explore affordability, competing offers and value even when meals are used.
S-800329 / 3 / 2; default price; “kept piling up in the fridge”Same tenure as S-8056, very different usage. Distinguish unaffordable price from paying for unused food; investigate whether disengagement preceded the price concern.
S-800047 / 3 / 0; repetition; “kept piling up in the fridge”Long-tenure repetition claim with no recent cooking. Look for specific recipe fatigue and when it started, versus meal backlog or changed routines.
S-807223 / 1 / 8; delivery problems; no commentContinued cooking despite a delivery complaint. Reconstruct a concrete delivery incident, its consequences and whether lateness actually drove cancellation.
S-80246 / 5 / 1; other; “kept piling up in the fridge”Early disengagement outside the executive categories. Explore onboarding, cooking effort, portions and whether Sprig ever fitted the household routine.
S-825044 / 1 / 6; moving/travelling; “too much food for two”Test a situational explanation against household/quantity mismatch; understand why cancellation rather than skipping made sense.

Backups, in corresponding order: S-8096, S-8122, S-8209, S-8179, S-8259, S-8151. They preserve the main behavioral contrast, though comments differ.

Invite neutrally: “We’re learning about your experience using and stopping Sprig; this is not a sales call.” Book six 30-minute calls plus replacement availability. Do not screen for agreement with an executive theory. Confirm dates and household context; validate unusual counts rather than treating administrative data as ground truth.

Shared 30-minute interview guide

0–2 minutes: Introduction

“Thanks for joining. We want to understand what happened in your experience, including anything that worked or didn’t. There are no right answers, and we won’t try to persuade you to return. May we record for research notes? You can decline any question.”

2–6 minutes: Expectations and first month

  • “What was happening in your life when you signed up?”
  • “What did you hope Sprig would replace or make easier?”
  • “Walk me through a typical week in your first month: receiving, choosing and cooking meals.”
  • “What happened to meals you didn’t cook?”

Establish actual routine before asking whether it changed.

6–16 minutes: Reconstruct the cancellation timeline

  • “Take me through your last few weeks with Sprig.”
  • “When did you first consider stopping? What happened just before that?”
  • “Tell me about the last box you received: what arrived, what you cooked, and what happened to the rest.”
  • “Tell me about a week you skipped. What led to that?”
  • “What happened on the day you cancelled? Why then rather than earlier?”

Probe for dates, examples, actions and consequences. Separate gradual dissatisfaction from the final trigger. Ask what they tried, including changes to orders, skipping or contacting support.

16–21 minutes: Replacement and trade-offs

  • “How are you handling dinners now? Tell me about last week.”
  • “What alternatives did you actually consider or use?”
  • “What did those cost, and what did you get for that?”
  • “What made cancelling preferable to continuing or skipping?”

Distinguish actual competitor switching from general perceptions of cheaper options.

21–27 minutes: Cover gaps, then reconcile the form

Only after the unprompted account: - Price: “How did the cost fit your budget? Did anything change?” - Recipes: “How did recipe choices change over time? Any specific examples?” - Delivery: “How did deliveries fit your plans? Describe any problem and what followed.” - Usage: “What made cooking the meals easier or harder?”

Then: “The form recorded [reason]. How well did that capture your situation? Were other reasons missing?” Clarify whether they actively chose it.

27–30 minutes: Close

Summarize the sequence and ask: “What have I misunderstood? What mattered most that we haven’t discussed?”

Support and synthesis

Run a rehearsal for both new interviewers; pair each with an experienced observer. Use silence and neutral probes—“What happened next?”—not “Was it too expensive?” Never defend Sprig.

Debrief immediately using one template: expectations, usage trajectory, incidents, underlying constraints, cancellation trigger, replacement, supporting quotes and uncertainty. Compare confirming and contradicting cases. Report plausible mechanisms and follow-up tests—not prevalence estimates or a causal verdict from six calls.

Grades and run details

Decision model 75 · LLM judge 77
Decision model checks
  • passUses the supplied evidence correctly55%
  • partialAddresses the actual decision37%
  • partialRespects explicit constraints25%
  • partialIdentifies material uncertainty30%
  • passAvoids unsupported claims70%
  • partialProduces the required deliverable22%
  • partialDoesn't trust the default reason66%
  • passRecruits from the data100%
  • passDesigned to falsify67%
  • passAsks what people did, not what they think98%
  • passDoesn't lead the witness82%
  • partialMarks what to cut if the call runs over43%
Run
Run
#1
API response time
47 s
Submitted
6 Oct 2026

Usable with a quick edit11 of 12 checks passed

The only missing element is marking what to cut if the call runs over, which a PM can add in a quick edit; otherwise the plan is complete and evidence-based.

Output

Churn Interviews: Recruit Plan and Guide

What the data already tells us

  • The cancel form inflates "Too expensive." It is pre-selected, so we can't tell real price objections from people clicking through. Most cancellers left it on that answer.
  • Most cancellers had stopped cooking before they left. Many cooked 0–2 recipes in their last four weeks and skipped several deliveries. Their free text points to fit, not price: "kept piling up in the fridge," "too much food for two," "didn't have time to cook." The comparison stat agrees: 81% of retained subscribers cooked 3+ recipes a week in month one, versus 34% of cancellers. That's a correlation, but it suggests a fourth hypothesis none of the execs named: the kit never became a habit.
  • "Delivery problems" cancellers look different. Every one of them cooked 3+ recipes in their last four weeks, and several left within weeks 2–3. These are engaged customers we lost to an ops failure.
  • Free text often contradicts the selected reason. For example, "Moving / travelling" sits next to "cheaper at the supermarket."

Six calls can't tell us how many people leave for each reason; the data does that. The calls are for understanding how people decide to leave, so we give each hypothesis a fair, clean case.

Who to recruit

#PrimaryBackupProfileWhy
1S-8056S-8096Pre-selected price; cooked 12 (backup 9) in last 4 wks; "cheaper at the supermarket"CEO's hypothesis at its strongest: an engaged user who says price. Was it really competitors' discounts?
2S-8004S-8183Pre-selected price; 5 skips, 0 cooked, no free textWhat the default hides. Was price the reason or just the first option?
3S-8005S-8016Pre-selected price; 4–5 skips, 0 cooked; "too much food for two" / "piling up"Portion and waste: the theory none of the execs named.
4S-8036S-8282Pre-selected price; 5 skips, 0 cooked; "didn't have time to cook"Time and habit. When did cooking stop, and why?
5S-8133S-8057Chose "Recipes got repetitive"; 45–53 weeks; still cooking a littleHead of Food's hypothesis: a long-tenure user who picked this reason actively.
6S-8097S-8072Chose "Delivery problems"; cooked 8–10 in last 4 wksHead of Ops' hypothesis: an engaged user lost to a delivery failure.

Contact backups only if the primary doesn't respond within 48 hours. Assign the experienced interviewers to calls 1 and 2, where people most easily give a stock answer like "it was the price." New interviewers take 3–6 and shadow one call first if the schedule allows.

Interview guide (30 min)

Rules for interviewers (read before every call)

  1. Ask about what happened, not opinions. Prefer "Tell me about the last box you got" to "Do you think the recipes are repetitive?"
  2. Don't mention their cancel reason or our hypotheses until section 4. If you say "price" first, you'll hear "price."
  3. Don't defend, explain, or offer anything. No discounts, no "actually you can skip weeks." If they ask, note it and say someone can follow up.
  4. Allow silence. Count to five before your next question. The best answers come after the pause.
  5. Use neutral probes: "Tell me more." "What happened next?" "Can you give me an example?" "Why was that?"
  6. Follow their story, not the script. The script makes sure you cover everything; you don't need to ask it in order.
  7. Write down exact quotes, especially words they use about money, time, food, and delivery.

1. Intro (2 min)

"Thanks for making time. I'm [name] from Sprig. We're trying to understand what Sprig was like to use, including the bad parts. There are no wrong answers, and I'm not here to win you back. Is it OK if I take notes or record?"

2. Their life with Sprig (8 min)

  • "Take me back to when you signed up. What was going on? What were you hoping it would do for you?"
  • "Walk me through a typical week with a box. When did it arrive? Who cooked? What happened to the food?"
  • "How did that change over time?"
  • Probe: "When did that start?" "What was different then?"

3. The decision to leave (10 min)

  • "Tell me about the last few boxes before you cancelled."
  • "When did you first think about stopping? What happened that day or week?"
  • "Before cancelling, did you skip any weeks? What was going on then?"
  • Probe for leftovers and waste: "What happened to meals you didn't cook?"
  • "What did you do instead for meals? What does that cost and take?"
  • "Was there one moment that tipped it, or did it build up?"

4. Checking the reasons (6 min)

Ask these only now. - "On the cancel form you chose [reason]. Is that how you'd describe it to a friend?" - If the reason was pre-selected, say: "That option was already ticked by default. Was it your actual reason?" - Ask about each of the following, one at a time, unless they've already covered it. Get a specific example for each. - "How did the price feel by the end? Compared to what?" - "How did you feel about the recipe choices?" - "Any issues with deliveries? What happened?" - "If you could change one thing, would you have stayed? What would it be?"

5. Wrap (4 min)

  • "Is there anything I should have asked but didn't?"
  • Thank them and explain any incentive.

After each call (15 min, same day)

Fill in the shared sheet with: - Their main reason, in their words. - When cooking dropped off: week, and the trigger. - Supporting quotes. - Whether the form reason matched their story.

After all six calls, I'll write up how each person's decision unfolded, mapped against the four hypotheses. I'll be clear that six interviews show how people decide, not how many decide each way. The write-up will also recommend changing the cancel form so no option is pre-selected; that one change will make the form data usable.

Grades and run details

Decision model 92 · LLM judge 92
Decision model checks
  • passUses the supplied evidence correctly13%
  • passAddresses the actual decision37%
  • passRespects explicit constraints33%
  • partialIdentifies material uncertainty33%
  • passAvoids unsupported claims16%
  • passProduces the required deliverable69%
  • passDoesn't trust the default reason53%
  • passRecruits from the data100%
  • passDesigned to falsify56%
  • passAsks what people did, not what they think95%
  • passDoesn't lead the witness83%
  • partialMarks what to cut if the call runs over43%
Run
Run
#1
Time to output
49 s
Submitted
5 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 4

Uses the supplied evidence correctlyWrongRightRight
Gemini 4 Argon · API

The output claims 79% of cancellers cooked ≤3 recipes, but the actual figure is 73%.

GPT-6.1 Sol · API

All factual claims about the current situation are taken directly from the supplied context or follow from the data.

Opus 5.5 · Claude

All factual claims about the current situation are directly from the supplied context or derived from it by arithmetic.

Identifies material uncertaintyWrongRightRight
Gemini 4 Argon · API

Does not name specific unknowns or say what would change the call if results contradict assumptions.

GPT-6.1 Sol · API

It acknowledges the form's weakness, the first-month data's limitations, and instructs to report uncertainty and not draw causal conclusions from six calls.

Opus 5.5 · Claude

It names the pre-selected default bias, the habit hypothesis, and the limitation of six interviews for quantifying reasons.

Avoids unsupported claimsWrongRightRight
Gemini 4 Argon · API

The 79% figure is an unsupported claim not backed by the cancellations list.

GPT-6.1 Sol · API

Interpretations like 'comments often suggest overlapping problems' are grounded in the data and not presented as absolute facts.

Opus 5.5 · Claude

Interpretations like 'engaged customers lost to ops failure' are presented as patterns to test, not as established fact.

Doesn't trust the default reasonRightWrongRight
Gemini 4 Argon · API

Acknowledges the pre-selected default bias and shows that many who selected it had stopped cooking.

GPT-6.1 Sol · API

It names the pre-selected default as a bias but does not use the behaviour columns to show that most 'too expensive' cancellers had stopped cooking (e.g., the 118 of 156 pattern).

Opus 5.5 · Claude

It explicitly names the pre-selected default as a bias and shows that most 'too expensive' cancellers had stopped cooking.

All got wrong 1

Marks what to cut if the call runs overWrongWrongWrong
Gemini 4 Argon · API

Timings sum to 30 minutes but the guide does not mark which sections to cut if time runs short.

GPT-6.1 Sol · API

The guide has timed sections that sum to 30 minutes but does not mark which questions are must-ask or what to cut if time runs short.

Opus 5.5 · Claude

The guide has timed sections but does not mark which questions are must-ask or what to cut if time runs short.

All got right 7

Addresses the actual decisionRightRightRight
Gemini 4 Argon · API

Commits clearly to a six-participant recruitment plan and a complete interview guide.

GPT-6.1 Sol · API

The output commits to a clear recruitment plan and interview guide, which is the requested deliverable.

Opus 5.5 · Claude

The output commits to a clear recruitment plan and interview guide, framed for the PM, and states the purpose of the calls.

Respects explicit constraintsRightRightRight
Gemini 4 Argon · API

Stays within 900 words, addresses the reader, and includes advice for new interviewers.

GPT-6.1 Sol · API

The output is under 900 words, addresses the two new interviewers with rehearsal and pairing, and follows the requested form.

Opus 5.5 · Claude

The output is under 900 words, addresses the two new interviewers, and provides a complete plan and guide.

Produces the required deliverableRightRightRight
Gemini 4 Argon · API

Delivers a complete recruitment list and timed interview guide usable by the PM.

GPT-6.1 Sol · API

The output provides a complete, usable recruitment plan and interview guide within the word limit.

Opus 5.5 · Claude

The output is a complete recruitment plan and interview guide, usable by a PM with light edits.

Recruits from the dataRightRightRight
Gemini 4 Argon · API

Defines six behavioural segments with specific IDs, testing each hypothesis.

GPT-6.1 Sol · API

The six recruits are chosen by contrasting behaviour patterns (active cook/price, disengaged/price, repetition, delivery, early disengagement, moving) that test each executive hypothesis.

Opus 5.5 · Claude

Recruitment groups are defined by behavior patterns (engaged price, default-hiders, waste, time, recipes, delivery) with specific IDs.

Designed to falsifyRightRightRight
Gemini 4 Argon · API

Each hypothesis has a probe that could yield a disconfirming honest answer (e.g., on time, waste, delivery).

GPT-6.1 Sol · API

The guide includes questions like 'How did the cost fit your budget?' and 'Describe any problem and what followed' that could yield answers disproving each hypothesis.

Opus 5.5 · Claude

Each hypothesis has a non-leading question (e.g., 'How did the price feel by the end?') that could yield disconfirming answers.

Asks what people did, not what they thinkRightRightRight
Gemini 4 Argon · API

Core questions ask about the last box, specific weeks, and actions rather than opinions or predictions.

GPT-6.1 Sol · API

Core questions ask for specific recent instances (last box, last skipped week, last week's dinners) rather than opinions or hypotheticals.

Opus 5.5 · Claude

Core questions ask about specific recent actions (last boxes, skipped weeks, what they did instead) rather than opinions.

Doesn't lead the witnessRightRightRight
Gemini 4 Argon · API

Questions are open and neutral, price is not mentioned first, and interviewers are warned not to lead.

GPT-6.1 Sol · API

Questions are open and neutral, and the guide explicitly warns against leading probes like 'Was it too expensive?'.

Opus 5.5 · Claude

Questions are neutral and open; the guide instructs not to mention the cancel reason or hypotheses until section 4.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated. Where our PM checked the graders by hand, they agreed on 95% of 42 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Sonnet 5.5withAPI88.088.52None
2GPT-6.1 SolwithAPI83.784.92None
3Opus 5.5withClaude92.074.72None
4GPT-6 AstrawithChatGPT89.774.52None
5Haiku 5.5withAPI89.770.12None
6GPT-6 LunawithAPI83.773.92None
7Gemini 4 ArgonwithAPI75.670.12None
8Gemini 3.8 FlashwithAPI67.851.92None
9Gemini 3.5 Flash-LitewithGemini63.926.121 capped

What you get for the cost

GPT-6.1 Sol · API scores within 4 points of Sonnet 5.5 · API at a third of the cost.

Task score against relative cost for each setup. The table below has the same figures.304050607080901001×3×10×30×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreSonnet 5.5 · API: task score 87, 43× the cheapestGPT-6.1 Sol · API: task score 83, 13× the cheapestOpus 5.5 · Claude: task score 82, 71× the cheapestGPT-6 Astra · ChatGPT: task score 81, 66× the cheapestHaiku 5.5 · API: task score 78, 2.3× the cheapestGPT-6 Luna · API: task score 78, the cheapestGemini 4 Argon · API: task score 70, 13× the cheapestGemini 3.8 Flash · API: task score 57, 8.3× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 42, 3.0× the cheapest, 1 critical failureSonnet 5.5 · APIGPT-6.1 Sol · APIOpus 5.5 · ClaudeGPT-6 Astra · ChatGPTHaiku 5.5 · APIGPT-6 Luna · APIGemini 4 Argon · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and costAt least one critical failure
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API781.0×
Haiku 5.5 · API782.3×
Gemini 3.5 Flash-Lite · Gemini423.0×
Gemini 3.8 Flash · API578.3×
GPT-6.1 Sol · API8313×
Gemini 4 Argon · API7013×
Sonnet 5.5 · API8743×
GPT-6 Astra · ChatGPT8166×
Opus 5.5 · Claude8271×

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

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