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

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
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
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

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
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
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.

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.
Judd AntinLenny’s Podcast
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.
Bob MoestaLenny’s Podcast
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).
Product and growth leadersOn Lenny’s Podcast

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.

Opus 5.5Claude94Usable with a quick edit GPT-6 AstraChatGPT85Usable with a quick edit Sonnet 5.5API79Needs more work GPT-6.1 SolAPI77Needs more work GPT-6 LunaAPI71Usable with a quick edit Haiku 5.5API69Needs more work Gemini 3.8 FlashAPI56Needs more work Gemini 3.5 Flash-LiteGemini44Needs more work

GPT-6.1 SolAPI

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.

Recruit	Evidence	Why this person
S-8056	29 / 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-8003	29 / 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-8000	47 / 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-8072	23 / 1 / 8; delivery problems; no comment	Continued cooking despite a delivery complaint. Reconstruct a concrete delivery incident, its consequences and whether lateness actually drove cancellation.
S-8024	6 / 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-8250	44 / 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 83 · LLM judge 77

Decision model checks

passUses the supplied evidence correctly56%
passAddresses the actual decision35%
partialRespects explicit constraints26%
partialIdentifies material uncertainty37%
passAvoids unsupported claims82%
passProduces the required deliverable47%
partialDoesn't trust the default reason66%
passRecruits from the data98%
passDesigned to falsify71%
passAsks what people did, not what they think92%
passDoesn't lead the witness75%
partialMarks what to cut if the call runs over45%

Run

Run: #1
API response time: 47 s
Submitted: 6 Oct 2026

Haiku 5.5API

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 selected	Count	Share
Too expensive (pre-selected default)	156	52%
Recipes got repetitive	52	17%
Moving / travelling	41	14%
Delivery problems	29	10%
Other	22	7%

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.

#	Account	Why this person
1	S-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.
2	S-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.
3	S-8186 (Repetitive, 54 wks, 2 recipes)	Tests the Head of Food's theory with a long-tenure subscriber.
4	S-8115 (Delivery problems, 17 wks, 10 recipes)	Tests the Head of Ops's theory with an engaged cook.
5	S-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.
6	S-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 2

Uses the supplied evidence correctlyRightMixed

GPT-6.1 Sol · API

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

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.

Avoids unsupported claimsRightMixed

GPT-6.1 Sol · API

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

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.

All got wrong 2

Doesn't trust the default reasonWrongWrong

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).

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 overWrongWrong

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.

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 8

Addresses the actual decisionRightRight

GPT-6.1 Sol · API

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

Haiku 5.5 · API

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

Respects explicit constraintsRightRight

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.

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.

Identifies material uncertaintyRightRight

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.

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.

Produces the required deliverableRightRight

GPT-6.1 Sol · API

The output provides a complete, usable recruitment plan and 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.

Recruits from the dataRightRight

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.

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 falsifyRightRight

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.

Haiku 5.5 · API

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

Asks what people did, not what they thinkRightRight

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.

Haiku 5.5 · API

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

Doesn't lead the witnessRightRight

GPT-6.1 Sol · API

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

Haiku 5.5 · API

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

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 · Harness	Decision model	LLM judge	Runs	Critical failures
1	Sonnet 5.5withAPI	86.1	88.5	2	None
2	GPT-6.1 SolwithAPI	87.8	84.9	2	None
3	Opus 5.5withClaude	92.1	74.7	2	None
4	GPT-6 AstrawithChatGPT	89.7	74.5	2	None
5	Haiku 5.5withAPI	87.8	70.1	2	None
6	GPT-6 LunawithAPI	81.6	73.9	2	None
7	Gemini 3.8 FlashwithAPI	69.7	51.9	2	None
8	Gemini 3.5 Flash-LitewithGemini	60.1	26.1	2	1 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.

Best valueNobody beats these on both score and costAt least one critical failure

Task score and relative cost by setup
Setup	Task score	Relative cost (cheapest = 1)
GPT-6 Luna · API	77	1.0×
Haiku 5.5 · API	77	2.3×
Gemini 3.5 Flash-Lite · Gemini	41	3.0×
Gemini 3.8 Flash · API	58	8.3×
GPT-6.1 Sol · API	85	13×
Sonnet 5.5 · API	86	43×
GPT-6 Astra · ChatGPT	81	66×
Opus 5.5 · Claude	82	71×

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

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

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

Reliably right

Where it slips

How it’s graded

Designed to falsify

Asks what people did, not what they think

Doesn't lead the witness

Plus our standard checks

The tasks

The outputs

Output

Aim and guardrails

Recruit these six

Shared 30-minute interview guide

0–2 minutes: Introduction

2–6 minutes: Expectations and first month

6–16 minutes: Reconstruct the cancellation timeline

16–21 minutes: Replacement and trade-offs

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

27–30 minutes: Close

Support and synthesis

Grades and run details

Decision model checks

Run

Output

Churn interviews: recruiting plan and guide

What the data says (and doesn't)

Who to recruit (6 calls)

Interview guide (30 minutes)

Notes for the two new interviewers

After the calls

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Run

Check by check

Where they differ 2

All got wrong 2

All got right 8

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured