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 16 graded outputs by 8 models. 38% were usable with at most a quick edit.

Reliably right

  1. Asks what people did, not what they think92% pass
    Core questions ask about specific recent actions (last boxes, skipped weeks, what they did instead) rather than opinions.
    Opus 5.5 · Claude · Too expensive, by default
  2. Tests both theories fairly91% 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 signer91% 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 has timed sections but does not mark which questions are must-ask or what to cut if time runs short.
    Opus 5.5 · Claude · Too expensive, by default
  2. Doesn't trust the default reason41% 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 correctly64% 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 · v1.0

The outputs

8 setups answered this brief.

Needs more than a quick edit4 of 12 checks passed

The output contains a factual error (12 recipes a week) and leading questions that would require rework before use. A PM would need to re-check the data and rewrite several questions.

Output

Sprig Retention: Churn Interview Plan (Next Week)

To stress-test our executive hypotheses (CEO: Price/Competitors; Head of Food: Repetitive recipes; Head of Ops: Late deliveries) and address the alarming baseline metric—only 34% of cancellers cooked $\ge$3 recipes/week in month one, versus 81% of retained users—we have selected a targeted cross-section of 6 users from last quarter’s cancellation dataset.

---

Part 1: Recruitment Plan (Who & Why)

We need a mix of profiles to isolate the real drivers of churn while accommodating our two interviewers who are new to the process (we will pair them with experienced leads or give them highly structured transcripts).

1. S-8028 (Delivery Problems / High Engagement) * Profile: Subscribed 3 weeks, skipped 2, cooked 9 recipes (heavy initial usage). Reason: Delivery problems. Free text: "kept piling up in the fridge". * Why: Tests Head of Ops' delivery hypothesis against actual usage. With 9 recipes cooked, they were engaged until logistics broke down. Good for new interviewers (clear timeline). 2. S-8056 (Price / Power User / Churn after 29 weeks) * Profile: Subscribed 29 weeks, skipped 1, cooked 12 recipes in the last 4 weeks. Reason: Too expensive (default). Free text: "cheaper at the supermarket". * Why: Tests CEO's price hypothesis. Crucially, they were cooking 12 recipes a week right up until cancellation—meaning value was high, but price sensitivity or a competitor offer triggered the exit. 3. S-8035 (Recipes / Long-term Churn) * Profile: Subscribed 56 weeks, skipped 1, cooked 2 recipes. Reason: Recipes got repetitive. Free text: "didn't have time to cook". * Why: Directly tests Head of Food’s hypothesis on long-term fatigue. Shows how a loyal user of over a year slowly disengaged. 4. S-8005 (Price / Early Drop-off / Zero Engagement) * Profile: Subscribed 20 weeks, skipped 5, cooked 0 recipes. Reason: Too expensive. Free text: "too much food for two". * Why: Represents the bulk of cancellers who fail activation (0 recipes cooked). Unpacks whether "Too expensive" is a proxy for value failure (didn't use it, so it felt like a waste). 5. S-8115 (Delivery / Mid-term / High Usage) * Profile: Subscribed 17 weeks, skipped 2, cooked 10 recipes. Reason: Delivery problems. Free text: "cheaper at the supermarket". * Why: Combines delivery issues with secondary price/supermarket comparison. Great for testing if operational friction causes users to re-evaluate overall cost. 6. S-8214 (Recipes / Mid-term / Moderate Usage) * Profile: Subscribed 31 weeks, skipped 3, cooked 3 recipes. Reason: Recipes got repetitive. Free text: "too much food for two". * Why: Explores the intersection of recipe fatigue and household sizing ("too much food for two"), providing actionable product feedback for the Head of Food.

---

Part 2: 30-Minute Interview Guide

Designed for clarity, supporting our two new interviewers with clear guardrails against leading questions.

Phase 1: Introduction & Context (5 mins)

  • Interviewer Script: "Hi [Name], thanks for taking 30 minutes. I’m with Sprig. We’re not here to win you back or sell you anything—I genuinely want to understand your experience so we can fix things for current subscribers. There are no right or wrong answers."
  • Warm-up: "Take me back to when you first signed up. What were you hoping Sprig would solve for you?"

Phase 2: The Journey & Engagement (10 mins)

(Focusing on the gap between our 81% retained benchmark and their early experience) * "Looking back at your first month, how easy or hard was it to get through [X] recipes a week?" * Probe (for New Interviewers): Listen for signals matching the data (e.g., "I see you skipped a few weeks around month two..."). Ask: "What caused you to skip those weeks?" * "When you thought about skipping or pausing, what usually drove that decision? (e.g., schedule, food piling up, getting bored)?"

Phase 3: Stress-Testing Executive Hypotheses (10 mins)

  • Testing Delivery (Ops): "Did delivery timing or box condition ever impact your cooking plans? Can you share a specific instance?"
  • Testing Recipes & Portions (Food): "You mentioned [repetitive recipes / too much food for two]. At what point did the menu start feeling stale? Were there specific meals you started dreading?"
  • Testing Price & Alternatives (CEO): "When you selected 'Too expensive' / noted supermarket prices, what were you comparing Sprig to? Did a competitor offer trigger the switch, or did the value equation shift over time?"

Phase 4: The Final Decision & Wrap-Up (5 mins)

  • "Take me to the exact week you decided to cancel. What was the final straw?"
  • "If you could wave a magic wand and change one thing about Sprig the day before you cancelled, what would it have been?"
  • Close: "Thank you so much for your candor. This is immensely helpful for our team."

Claims the judge couldn’t find in the brief

  • S-8056 was cooking 12 recipes a week right up until cancellation.

Grades and run details

Decision model 63 · LLM judge 31
Decision model checks
  • passUses the supplied evidence correctly36%
  • partialAddresses the actual decision24%
  • partialRespects explicit constraints68%
  • partialIdentifies material uncertainty76%
  • passAvoids unsupported claims24%
  • partialProduces the required deliverable80%
  • partialDoesn't trust the default reason88%
  • passRecruits from the data72%
  • partialDesigned to falsify57%
  • partialAsks what people did, not what they think82%
  • partialDoesn't lead the witness46%
  • partialMarks what to cut if the call runs over57%
Run
Run
#1
Time to output
6 s
Submitted
5 Oct 2026

Needs more than a quick edit10 of 12 checks passed

The output fails to show that most 'Too expensive' cancellers had stopped cooking, a key insight from the data, and does not mark what to cut if the call runs over. A PM would need to re-analyze the data and add cut instructions.

Output

Churn interviews: recruiting plan and guide

What the data tells us

  • The form can't be trusted. "Too expensive" is pre-selected and listed first, and roughly half of cancellers have it. Many of those cooked heavily or wrote free text about something else. Treat the selected reason as a lead, not an answer.
  • Cancellers look like two groups, whatever they ticked.
  • Fading: 3-5 skipped deliveries and 0-2 recipes cooked in 4 weeks. Their free text is mostly "piling up in the fridge", "too much food for two" and "no time", which is about quantity and routine. That fits none of the three exec theories directly.
  • Still cooking: 0-1 skips and 6-12 recipes. Many of them ticked price, and almost all the delivery complainers sit here.
  • Caveat: the retained-vs-churned stat (first-month cooking) isn't the same measure as the CSV (last 4 weeks), so I can't tell yet whether the fading group never built the habit or lost it. The calls should find out.

Six calls can't size anything. The aim is to find out what's really behind each pattern, so we can fix the form and then measure.

Who to recruit

#SubscriberWhy
1S-8005 (price, 20 wks, 5 skips, 0 cooked, "too much food for two")Fading group with a default-looking price tick. Tests whether the real issue is portion size or cadence.
2S-8056 (price, 29 wks, 1 skip, 12 cooked, "cheaper at the supermarket")Heavy user with a price complaint. This is the best test of whether the CEO's theory holds.
3S-8141 (price, 15 wks, 0 skips, 12 cooked, no text)Heavy user, price tick, no explanation. Likely clicked the default. What was the real reason?
4S-8133 (repetitive, 45 wks, 1 skip, 3 cooked, no text)Long-tenure subscriber with no competing explanation, so a fair test of the Head of Food's theory.
5S-8097 (delivery, 2 wks, 10 cooked)Cooked almost every recipe, then left in week 2. Tests whether delivery or the first-box experience is the issue.
6S-8062 (moving/travelling, 36 wks, 0 skips, text: "kept piling up")Tests whether travel was the cause or a polite exit, and whether a pause would have kept them.

Recruit a backup for each slot with a similar profile: S-8008, S-8090, S-8196, S-8153, S-8091 and S-8025. The Head of Ops' theory gets one slot because delivery complainers are few and look different from the rest.

Running the calls

  • Pair each new interviewer with an experienced one. One leads and one takes notes, and they swap roles on the next call.
  • New interviewers should lead calls 2 and 3, which are the least delicate.
  • Spend 10 minutes after each call writing the cancellation story in three lines and noting which exec theory it supports, contradicts or ignores.

Interview guide (30 min)

Rules for new interviewers 1. Ask open questions and then stay quiet. Silence gets more than prompting. 2. Don't mention price, recipes or delivery first. Let them raise it. 3. Ask for specific events ("the last box"), not opinions ("what do you generally think?"). 4. Don't defend Sprig, don't pitch, and don't ask "would you come back if…?" People answer hypotheticals unreliably. 5. Follow up with "Tell me more", "What happened next?" and "Can you give me an example?"

1. Open (0-3 min) "Thanks for talking to us. We're trying to learn, not win you back, and nothing you say is wrong. Can we record for note-taking?"

2. Context (3-8 min) - "Who did you cook for? Tell me about a typical week of eating before Sprig." - "What made you sign up? What did you hope it would do for you?"

3. Timeline (8-20 min), the core of the call - "Take me through your first few weeks." (Probe: how many recipes did you cook, and what did it feel like?) - "Tell me about the last box you received. What happened to it, from the doorstep onwards?" - "When did you first think about cancelling? What was going on that week?" - "What did you do for meals instead?" - "Did you think about skipping a week instead? What happened with that?"

4. Only if not raised unprompted (20-26 min) - Cost: "How did the cost sit with you, and compared with what?" - Food: "How did you feel about the recipes over time? Any you made again or avoided?" - Delivery: "What was getting the box like?"

5. The form (26-29 min) Show what they selected. "You chose '[reason]'. What was going through your mind? Does it capture it?" For #2, #3 and #6, add: "Is there anything else that mattered more?"

6. Close (29-30 min) - "If you could change one thing about Sprig, what would it be?" - "Is there anything I should have asked and didn't?"

Tailored probes - #1: "Tell me about the week you had too much food. What did you do with it?" - #2: "Where did you do the comparison, and what exactly was cheaper?" - #3: "You were cooking a lot. What changed?" - #4: "Describe a week when you didn't feel like any of the recipes." - #5: "Walk me through your first delivery." - #6: "Could you have paused? Did you know that?"

After the calls

Review all six together and note which exec theories were supported, contradicted or absent. Separately, remove the default selection from the cancel form now, so the next batch of data is cleaner.

Grades and run details

Decision model 88 · LLM judge 77
Decision model checks
  • passUses the supplied evidence correctly17%
  • passAddresses the actual decision57%
  • passRespects explicit constraints24%
  • passIdentifies material uncertainty45%
  • partialAvoids unsupported claims24%
  • passProduces the required deliverable57%
  • partialDoesn't trust the default reason30%
  • passRecruits from the data99%
  • passDesigned to falsify70%
  • passAsks what people did, not what they think83%
  • passDoesn't lead the witness66%
  • partialMarks what to cut if the call runs over39%
Run
Run
#1
API response time
51 s
Submitted
6 Oct 2026

Needs more than a quick edit8 of 12 checks passed

The free-text claim is misleading, and the guide lacks cut marks for time management, so a PM would need to recheck the free-text analysis and add cut instructions.

Output

Churn interviews: recruiting plan and guide

What the data says (and doesn't)

Across 300 cancellations last quarter:

Reason selectedCountShare
Too expensive (pre-selected default)15652%
Recipes got repetitive5217%
Moving / travelling4114%
Delivery problems2910%
Other227%
  • The price number is inflated. "Too expensive" is pre-selected, so we can't tell deliberate choices from people who clicked Next. Treat it as a hypothesis to test, not a finding.
  • Free text points away from all three exec theories. Where people wrote something, the recurring themes were over-supply ("too much food for two," "kept piling up in the fridge") and time ("didn't have time to cook"). Neither is in the exec hypotheses.
  • Delivery cancellers were engaged. All 29 cooked at least 3 recipes in the last 4 weeks, and many cooked 8 or more. Late deliveries may be hurting people who want to cook.
  • The retention benchmark isn't testable here. The 34% figure is first-month cooking. The export only has the last 4 weeks, so we can't compare the two. Early-tenure cancellers are the closest we can get, so we'll include one.

Who to recruit (6 calls)

Recruit from the list by reason, tenure, and cooking level so each call tests a different explanation. Keep 2 backups for each slot.

#AccountWhy this person
1S-8007 (Too expensive, 37 wks, 4 skips, 0 recipes last 4 wks)Price claim from a long-tenure subscriber who stopped cooking. Tests whether price is the real reason or cover for disengagement.
2S-8056 (Too expensive, 29 wks, 12 recipes last 4 wks, "cheaper at the supermarket")Engaged cook who cites price. Tests the CEO's theory where it's most plausible.
3S-8186 (Repetitive, 54 wks, 2 recipes)Tests the Head of Food's theory with a long-tenure subscriber.
4S-8115 (Delivery problems, 17 wks, 10 recipes)Tests the Head of Ops's theory with an engaged cook.
5S-8171 (Too expensive, 6 wks, 0 recipes, "kept piling up in the fridge")Early-tenure, non-cooking cancel. Closest to the first-month question. Explores whether the box was used at all.
6S-8253 (Too expensive, 32 wks, 5 skips, 1 recipe, "too much food for two")Surfaces the over-supply theme no exec raised.

Backups: for slot 5, S-8153 (2 wks tenure, 8 recipes, delivery). For slot 6, S-8055 (Other, 5 skips, "too much food for two").

Interview guide (30 minutes)

Rules for all interviewers - Don't mention the cancel form's default or any exec theory. - Don't ask leading questions ("Was it the price?"). Ask what happened, then probe. - Let silences run. Don't fill them with suggestions. - Ask for specific examples: "Tell me about the last time…" - Don't defend Sprig or argue with the subscriber.

1. Opening (2 min) "Thanks for making time. We're trying to understand what happened with your subscription so we can improve it. There are no right or wrong answers, and nothing you say will affect your account. Is it okay if I take notes and record for accuracy?"

2. Their Sprig story (8 min) - "Take me back to when you first signed up. What did you hope Sprig would be for you?" - "How did a typical week go once the boxes started arriving?" - "What was happening in your life when you decided to cancel?" - "Walk me through the moment you decided. What happened first?"

3. The cancel form (4 min) - "On the cancel screen, you chose a reason. Can you walk me through how you picked it?" - If they say "too expensive": "What made that the best fit? Was there anything else you considered?" - If they chose something else: "What made that the closest match?" - "Was there a reason you wanted to share that the options didn't capture?"

4. Cooking and the box (6 min) - "In your first month, how many of the recipes did you actually cook?" - "What got in the way of cooking the others?" - "What happened to ingredients you didn't use? Walk me through a recent week." - "How did the portion sizes work for you?" - "How did the recipes feel after a few weeks?"

5. Price, delivery, and alternatives (5 min) - "How did the cost compare to what you'd spend otherwise?" - "You mentioned [free text, if any]. Tell me more about that." - "Have you had any issues with deliveries? What happened, and how did it affect your week?" - "Did you pause or skip boxes before cancelling? What was going on then?"

6. Counterfactual (3 min) - "If Sprig had changed one thing, would you have stayed? What would it have been?" - "What would have had to be different for you to skip only for a month instead of cancelling?"

7. Close (2 min) "Is there anything I didn't ask that you think we should know?" Thank them and confirm how the findings will be used.

Notes for the two new interviewers

  • Pair each new interviewer with an experienced one for the first call, with the experienced person leading.
  • Practise the "walk me through" and silence-holding moves beforehand. Most of the signal comes from follow-up probes.
  • If a subscriber gets upset, acknowledge it briefly, offer to end early, and move to the close.
  • Write down direct quotes, not summaries, especially about the cancel form and the portions.

After the calls

Code each interview against the three exec theories and two emerging themes (over-supply, time to cook). Report what each subscriber did and said, not just their stated reason. Flag any case where the stated reason and the story don't match, since that's the pre-selection problem showing up.

Claims the judge couldn’t find in the brief

  • Free text points away from all three exec theories, with recurring themes over-supply and time to cook.

Grades and run details

Decision model 83 · LLM judge 62
Decision model checks
  • passUses the supplied evidence correctly41%
  • passAddresses the actual decision37%
  • partialRespects explicit constraints31%
  • passIdentifies material uncertainty54%
  • passAvoids unsupported claims34%
  • passProduces the required deliverable38%
  • partialDoesn't trust the default reason33%
  • passRecruits from the data100%
  • passDesigned to falsify62%
  • partialAsks what people did, not what they think35%
  • passDoesn't lead the witness67%
  • partialMarks what to cut if the call runs over52%
Run
Run
#1
API response time
46 s
Submitted
8 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 6

Uses the supplied evidence correctlyMixedRightMixed
Gemini 3.5 Flash-Lite · Gemini

The output claims S-8056 was cooking 12 recipes a week, but the data shows 12 recipes over 4 weeks, not per week.

Sonnet 5.5 · API

All factual claims about the current situation are directly supported by the supplied context or derived from the data without invention.

Haiku 5.5 · API

The claim that free text points away from all three exec theories is incorrect; free text includes price-related comments like ‘cheaper at the supermarket’ that support the CEO’s theory.

Identifies material uncertaintyWrongRightRight
Gemini 3.5 Flash-Lite · Gemini

The output does not name unknowns that could change the plan, such as the unreliability of the cancel form default or how interviews might surface unexpected reasons.

Sonnet 5.5 · API

It identifies the key unknown about whether the fading group never built the habit or lost it, and notes the calls should find out.

Haiku 5.5 · API

It identifies the pre-selection bias, notes the data gap for first-month cooking, and flags that the price number is inflated.

Avoids unsupported claimsMixedRightMixed
Gemini 3.5 Flash-Lite · Gemini

It presents interpretations like 'value was high, but price sensitivity triggered the exit' and 'engaged until logistics broke down' as facts without labelling them as hypotheses.

Sonnet 5.5 · API

Interpretations like the two-group pattern are presented as observations from the data, not as proven causes, and are appropriately hedged.

Haiku 5.5 · API

The statement that free text points away from all three exec theories is unsupported by the data, because some free text supports the price hypothesis.

Recruits from the dataMixedRightRight
Gemini 3.5 Flash-Lite · Gemini

It picks six individuals but does not define recruitment groups (e.g., stopped cooking, still cooking but price) with counts from the data.

Sonnet 5.5 · API

It defines behavioral groups (fading, still cooking, repetitive, delivery) and selects six interviewees from the list to test each exec hypothesis, with clear reasons.

Haiku 5.5 · API

It picks six interviewees that cover the needed behaviour patterns: stopped cooking, still cooking but price, repetitive recipes, delivery, early-tenure, and over-supply.

Designed to falsifyWrongRightRight
Gemini 3.5 Flash-Lite · Gemini

The recipe question ('At what point did the menu start feeling stale?') assumes fatigue and is leading, so it cannot cleanly disprove the hypothesis.

Sonnet 5.5 · API

Each exec hypothesis has a non-leading question that could disprove it (e.g., 'Where did you do the comparison, and what exactly was cheaper?' for price).

Haiku 5.5 · API

The guide includes open questions (e.g., on cost, recipes, deliveries) whose honest answers could disprove each exec hypothesis.

Doesn't lead the witnessWrongRightRight
Gemini 3.5 Flash-Lite · Gemini

Questions like 'At what point did the menu start feeling stale?' and 'Were there specific meals you started dreading?' lead the participant toward the fatigue hypothesis.

Sonnet 5.5 · API

Questions are open and neutral, and the guide instructs interviewers not to mention price, recipes, or delivery first.

Haiku 5.5 · API

Questions are neutral (e.g., ‘Walk me through the moment you decided’) and the guide explicitly bans leading wording.

All got wrong 2

Doesn't trust the default reasonWrongWrongWrong
Gemini 3.5 Flash-Lite · Gemini

The plan does not mention that 'Too expensive' is the pre-selected default or use the behaviour columns to show most of those cancellers had stopped cooking.

Sonnet 5.5 · API

It names the pre-selected default as a bias but does not show from the file that most 'Too expensive' cancellers had stopped cooking; it instead says many cooked heavily or wrote other text, missing the key pattern.

Haiku 5.5 · API

It names the pre-selected default but does not use the behaviour columns to demonstrate that most ‘too expensive’ cancellers had stopped cooking; it only states the number is inflated.

Marks what to cut if the call runs overWrongWrongWrong
Gemini 3.5 Flash-Lite · Gemini

Sections are timed to 30 minutes but the guide does not mark must-ask questions or indicate what to cut if time runs short.

Sonnet 5.5 · API

The guide has timed sections adding to 30 minutes but does not mark which questions are must-ask or what to cut if the call runs over.

Haiku 5.5 · API

The guide has timed sections that add to 30 minutes but does not mark which questions to cut if time runs over.

All got right 4

Addresses the actual decisionRightRightRight
Gemini 3.5 Flash-Lite · Gemini

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

Sonnet 5.5 · API

The output commits to a clear plan (six specific recruits and a full interview guide) and states the aim and next steps after the calls.

Haiku 5.5 · API

The output commits to a clear recruiting plan and interview guide, naming specific accounts and a full guide.

Respects explicit constraintsRightRightRight
Gemini 3.5 Flash-Lite · Gemini

The plan is under 900 words, addresses the two new interviewers, and provides the requested recruitment and guide.

Sonnet 5.5 · API

The output is under 900 words, addresses the two new interviewers with pairing and rules, and provides both the recruitment plan and the guide as requested.

Haiku 5.5 · API

The output is a plan plus guide within ~800 words, addresses new interviewers, and respects the six-call, 30-minute constraint.

Produces the required deliverableRightRightRight
Gemini 3.5 Flash-Lite · Gemini

The recruitment list and 30-minute guide are present, complete, and within the word limit.

Sonnet 5.5 · API

The output is a complete, usable plan with recruitment table and timed interview guide, within the word limit.

Haiku 5.5 · API

It delivers a complete recruiting plan and interview guide that a PM could use with light edits.

Asks what people did, not what they thinkRightRightRight
Gemini 3.5 Flash-Lite · Gemini

Core questions ask for specific recent instances (e.g., 'Take me to the exact week you decided to cancel', 'Can you share a specific instance?').

Sonnet 5.5 · API

Core questions ask about specific recent actions (last box, first few weeks, what they did for meals instead) rather than opinions or hypotheticals.

Haiku 5.5 · API

Core questions ask about specific recent weeks, what happened to ingredients, and walk-throughs of events rather than opinions.

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 98% of 42 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Sonnet 5.5withAPI86.188.52None
2GPT-6.1 SolwithAPI87.884.92None
3Opus 5.5withClaude92.174.72None
4GPT-6 AstrawithChatGPT89.774.52None
5Haiku 5.5withAPI87.870.12None
6GPT-6 LunawithAPI81.673.92None
7Gemini 3.8 FlashwithAPI69.751.92None
8Gemini 3.5 Flash-LitewithGemini60.126.121 capped

What you get for the cost

GPT-6.1 Sol · API scores within a point 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 86, 43× the cheapestGPT-6.1 Sol · API: task score 85, 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 77, 2.3× the cheapestGPT-6 Luna · API: task score 77, the cheapestGemini 3.8 Flash · API: task score 58, 8.3× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 41, 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 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 · API771.0×
Haiku 5.5 · API772.3×
Gemini 3.5 Flash-Lite · Gemini413.0×
Gemini 3.8 Flash · API588.3×
GPT-6.1 Sol · API8513×
Sonnet 5.5 · API8643×
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