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

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
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
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

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
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
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.

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 · v1.2

The outputs

9 setups answered this brief.

Opus 5.5Claude92Usable with a quick edit GPT-6 AstraChatGPT85Usable with a quick edit Sonnet 5.5API79Needs more work GPT-6 LunaAPI73Usable with a quick edit GPT-6.1 SolAPI73Needs more work Haiku 5.5API69Needs more work Gemini 4 ArgonAPI62Needs more work Gemini 3.8 FlashAPI56Needs more work Gemini 3.5 Flash-LiteGemini44Needs more work

Gemini 4 ArgonAPI

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.

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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.

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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

Check by check

Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Got wrong 4

Uses the supplied evidence correctlyWrong

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

Identifies material uncertaintyWrong

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

Avoids unsupported claimsWrong

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

Marks what to cut if the call runs overWrong

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

Got right 8

Addresses the actual decisionRight

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

Respects explicit constraintsRight

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

Produces the required deliverableRight

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

Doesn't trust the default reasonRight

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

Recruits from the dataRight

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

Designed to falsifyRight

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

Asks what people did, not what they thinkRight

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

Doesn't lead the witnessRight

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

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 · Harness	Decision model	LLM judge	Runs	Critical failures
1	Sonnet 5.5withAPI	88.0	88.5	2	None
2	GPT-6.1 SolwithAPI	83.7	84.9	2	None
3	Opus 5.5withClaude	92.0	74.7	2	None
4	GPT-6 AstrawithChatGPT	89.7	74.5	2	None
5	Haiku 5.5withAPI	89.7	70.1	2	None
6	GPT-6 LunawithAPI	83.7	73.9	2	None
7	Gemini 4 ArgonwithAPI	75.6	70.1	2	None
8	Gemini 3.8 FlashwithAPI	67.8	51.9	2	None
9	Gemini 3.5 Flash-LitewithGemini	63.9	26.1	2	1 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.

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	78	1.0×
Haiku 5.5 · API	78	2.3×
Gemini 3.5 Flash-Lite · Gemini	42	3.0×
Gemini 3.8 Flash · API	57	8.3×
GPT-6.1 Sol · API	83	13×
Gemini 4 Argon · API	70	13×
Sonnet 5.5 · API	87	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