Tasks / Metrics & Experimentation

Experiment specification

Can the model design a test that could actually change the decision?

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. 28% were usable with at most a quick edit.

Reliably right

Randomises by user100% pass
Randomises by logged-in user ID across platforms, explicitly citing cross-platform usage and session non-independence.
Sonnet 5.5 · API · Show the fee sooner
Works around the calendar94% pass
Addresses both the payments test and the Black Friday freeze, proposes concurrent independent assignment with an interaction check, and avoids a one-week squeeze.
Sonnet 5.5 · API · Show the fee sooner
Tests one change at a time89% pass
The free-delivery banner is explicitly excluded from the variant, with an explanation that bundling would prevent attribution, and a separate test is proposed.
Sonnet 5.5 · API · Showing the delivery fee up front

Where it slips

Says why a test is worth it here42% pass
The output does not explain why this decision merits a test given the traffic and stakes, nor does it recommend a cheaper alternative.
Opus 5.5 · Claude · Show the fee sooner
Guardrails with thresholds56% pass
Guardrail metrics are named but lack explicit numeric thresholds; 'materially worse' and 'drops meaningfully' are too vague to block rollout without further definition.
Sonnet 5.5 · API · Showing the delivery fee up front
Doesn't let the metric flatter the change61% pass
The primary metric is from basket entry and explains why last-step completion misleads, but it does not include a basket drop-off guardrail with a threshold to block rollout.
GPT-6 Astra · ChatGPT · Show the fee sooner

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.

An unambiguous primary metric
Does the spec name one primary metric, explain why an increase is unambiguously good for the user and the business, and say how the result's trustworthiness will be checked?
Passes when One primary metric with a clear rationale for its direction, plus a planned trust check such as a sample ratio check.
Ronny KohaviLenny’s Podcast
Decision rule written before the test
Does the spec say, before the test runs, which result leads to which action (ship, don't ship, iterate), including what happens if the result is inconclusive?
Passes when Every outcome, including an inconclusive one, maps to a stated action, with the thresholds that separate them.
Ronny KohaviLenny’s Podcast
Sized from the real traffic
Is the sample size and duration worked out from the supplied traffic, baseline and smallest effect worth detecting, run in full weekly cycles?
Passes when Sample size follows from the baseline and the smallest effect worth acting on, the duration follows from the traffic the brief gives (including how units accumulate), and it runs whole weeks.
Product and growth leadersOn Lenny’s Podcast
Guardrails with thresholds
Does the spec name the guardrail metrics that would stop a rollout even if the primary metric wins, each with a threshold?
Passes when The guardrails that matter for this change are named, each with a threshold that would block the rollout.
Lauryn IsfordLenny’s Podcast
Says why a test is worth it here
Does the spec weigh whether a test is worth running here: enough traffic to detect an effect that matters, and a decision the result would actually change, and does it say what it would do instead if not?
Passes when Explains why this decision merits a test given the traffic and stakes, or recommends a cheaper route where they don't.
Lauryn IsfordLenny’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 · 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 checkout at Basketful. We want to test showing the delivery fee in the basket, instead of only at the last step of checkout. Write the experiment spec for Ravi, the growth engineer who'll build it, Ines, the analyst who'll read it out, and Chloe, our Head of Growth, who approves it. Keep it under 700 words. The team's draft plan is below. Fix what needs fixing.

What the model was given5 items: About Basketful, Why we're testing it, Traffic and baselines, Smallest change worth acting on, The team's draft plan

About BasketfulAn online grocery service. Delivery costs £3.99, or is free on orders over £60. Today the fee first appears on the final checkout step.

Why we're testing it12% of support contacts last quarter were about delivery fees the customer hadn't expected. Design believes showing the fee early will build trust; Chloe worries it will put people off before they start checkout.

Traffic and baselinesAbout 40,000 different customers view their basket each week. About half of each week's basket viewers are new that week; the rest came back from earlier weeks. 8% of basket viewers place an order. Of customers who start checkout, 62% complete it. Average order value: £47.

Smallest change worth acting onChloe: “Anything smaller than half a percentage point on orders isn't worth arguing about.”

The team's draft planPrimary metric: checkout completion rate (orders ÷ checkout starts). Split: 50/50 by session. Duration: we'll check the dashboard every day and stop as soon as it's significant. Chloe's addition: “While we're at it, let's put the free-delivery-over-£60 banner in the variant too.”

What a strong answer doesThe answer key the graders mark against

A spec that tests one change and fixes the draft's three problems. The primary metric is orders per basket-viewing customer, not checkout completion rate: showing the fee earlier changes who starts checkout, so completion can rise while orders fall. Customers, not sessions, are randomised, so returning customers see one version. The free-delivery banner stays out of the variant (or gets its own test), or the result can't be attributed. The duration is fixed in advance, with no daily stopping: detecting 0.5pp on an 8% base needs roughly 47,000 customers per arm (about 95,000 in total), and because half of each week's viewers are returning customers, new customers accumulate at about 40,000, 60,000, 80,000 and 100,000 after four weeks, so it runs about four full weeks, not two and a half. Guardrails with thresholds: average order value, fee-related support contacts, and the checkout-start rate as a diagnostic. A sample ratio check before reading results. A decision rule written up front: ship if orders are no worse than 0.5pp down and fee contacts fall; don't ship if orders drop by 0.5pp or more; with a stated plan for an inconclusive result.

Critical failures (cap the score at 40)

Uses checkout completion rate (orders ÷ checkout starts) as the primary metric
Bundles the free-delivery banner into the variant

Task

Mid-level · v1.4

The outputs

9 setups answered this brief.

Sonnet 5.5API89Usable with a quick edit Haiku 5.5API89Usable with a quick edit GPT-6.1 SolAPI79Usable with a quick edit GPT-6 AstraChatGPT75Needs more work Opus 5.5Claude73Needs more work GPT-6 LunaAPI71Usable with a quick edit Gemini 4 ArgonAPI59Needs more work Gemini 3.8 FlashAPI45Needs more work Gemini 3.5 Flash-LiteGemini23Critical failure

GPT-6 AstraChatGPT

Needs more than a quick edit11 of 13 checks passed

Guardrail thresholds are missing and the spec defers their definition, so a PM would need to add them before the spec is actionable.

Output

Experiment: show delivery fees in the basket

Owner/build: Ravi · Analysis: Ines · Approval: Chloe Decision: Should Basketful show delivery charges before checkout?

Hypothesis and scope

Showing the fee in the basket will reduce fee surprises, but could discourage ordering. Unexpected delivery fees accounted for 12% of support contacts last quarter; this is motivation, not a baseline customer complaint rate.

Test fee visibility only. Do not add the proposed free-delivery promotional banner: it introduces a second change and would prevent us attributing results to earlier disclosure.

Experience

Control: Current experience; delivery fee first appears at the final checkout step.
Treatment: Show a clearly labelled delivery line beside the basket total: £3.99 delivery, or Free delivery when eligible. Update the fee and total as the basket changes.
Use the existing pricing service and eligibility rules. Confirm behaviour at exactly £60 before launch; “over £60” must not silently become “£60 or more.”
Leave checkout, pricing and other messaging unchanged.

Eligibility and assignment

Include customers viewing a non-empty, orderable basket. Exclude staff, bots and test accounts using rules fixed before launch.

Randomise 50/50 by customer, not session, at their first eligible basket view. Persist assignment across visits and devices where identity is available. For signed-out customers, use a stable first-party identifier; Ravi must document identity merging and cross-device limitations before launch.

Analyse everyone assigned, whether or not they start checkout or successfully see the fee: intention to treat.

Metrics

Primary: Customer order conversion — percentage of assigned customers placing at least one order within seven days of assignment. Report treatment minus control in percentage points, with a two-sided 95% confidence interval.

Checkout completion is diagnostic only: treatment may change who starts checkout, so orders ÷ checkout starts could improve while overall ordering falls.

Secondary/diagnostic: - Checkout-start rate and checkout completion. - Delivery-fee-related support contacts per assigned customer within seven days, using consistent tagging. - Orders and revenue per assigned customer; average order value and free-delivery eligibility as diagnostics.

Guardrails: Contribution margin per assigned customer, if reliably available; otherwise net revenue per assigned customer as an explicitly limited proxy. Also monitor basket/checkout errors. Chloe and Ines must approve numerical economic and reliability tolerances before launch.

Sample size and schedule

Chloe’s smallest worthwhile order-conversion change is 0.5 percentage points: 8.0% to 8.5%, not a 0.5% relative lift.

At 5% two-sided significance and 80% power, detecting that increase requires approximately 48,000 customers per arm, or 96,000 total.

Ines must first verify that the 8% baseline matches the eligibility rules and seven-day outcome window; recalculate and freeze the sample target if necessary.

Do not count returning customers again. Expected recruitment is approximately:

Week 1: 40,000 unique customers.
Each subsequent week: 20,000 additional customers.
Four weeks: approximately 100,000 unique customers.

Enroll for at least four complete weeks and until the frozen sample target is reached, ending at a full-week boundary. Then wait seven days for the final cohort’s outcomes: roughly five weeks to readout.

Monitoring and decision

Ravi validates assignment persistence, fee accuracy, exposure/order linkage and sample-ratio balance before ramp-up.

Daily checks are for instrumentation and predefined safety breaches—not efficacy stopping. No “stop when significant,” sample extensions based on results, or repeated significance decisions.

Ines delivers one final primary analysis. Chloe’s default rollout rule is: - Estimated conversion gain at least +0.5 points; - 95% confidence interval excludes zero; - Guardrails pass.

Otherwise, do not roll out on this result alone. A non-significant result does not prove no harm. Secondary trust improvements inform follow-up, not a post-hoc replacement for the primary success rule.

Grades and run details

Decision model 77 · LLM judge 79

Decision model checks

passUses the supplied evidence correctly18%
partialAddresses the actual decision35%
partialRespects explicit constraints20%
passIdentifies material uncertainty72%
partialAvoids unsupported claims27%
passProduces the required deliverable27%
passTests one change at a time97%
partialFixes the duration up front88%
passAn unambiguous primary metric55%
passDecision rule written before the test95%
passSized from the real traffic54%
partialGuardrails with thresholds98%
partialSays why a test is worth it here94%

Run

Run: #1
Time to output: 47 s
Submitted: 1 Oct 2026

Haiku 5.5API

Usable with a quick edit12 of 13 checks passed

The spec is complete, directly addresses all team members, and a PM could use it as-is, only needing to fill in the two baselines that are clearly flagged.

Output

Experiment Spec: Delivery Fee in Basket

To: Ravi (build), Ines (analysis), Chloe (approval) From: PM, Checkout Status: For approval

What changed from the draft

Primary metric moved from checkout completion to basket-to-order conversion. Completion only measures people who reach checkout, so it would miss the risk Chloe raised: people leaving before checkout.
Randomise by customer, not session. Half of each week's viewers are returning customers. Session splits would show the same person both versions.
Fixed duration, pre-set sample size. Checking daily and stopping at first significance inflates false positives.
Free-delivery banner removed. It changes the same thing we're measuring, so we couldn't attribute any effect. We'll propose it as a separate test.

Hypothesis

Showing the delivery fee in the basket changes basket-to-order conversion. We expect an increase of at least 0.5 percentage points (8.0% to 8.5%). The test is two-sided, so a decrease would also be recorded and acted on.

Variants

Control: Basket as today. Fee appears only on the final checkout step.
Variant: Basket shows a delivery line under the subtotal: "Delivery: £3.99" or "Delivery: free" once the basket is over £60. Checkout is unchanged.

Ravi: The line must update live as the basket crosses the £60 threshold, and must match the fee charged at checkout exactly.

Population and randomisation

All customers who view their basket during the test period.
Randomise 50/50 by customer ID at first basket view in the test. Assignment stays fixed for the whole test.
Run a sample ratio mismatch (SRM) check on day 1. If the split deviates from 50/50 at p < 0.01, pause and investigate before continuing.

Metrics

Primary: Basket-to-order conversion. Customers who place an order within 7 days of their first basket view in the test, divided by customers who viewed their basket. Baseline: 8%.

Guardrails: - Checkout start rate (checkout starts ÷ basket viewers). Ines to pull baseline. - Checkout completion rate. Baseline: 62%. - Average order value. Baseline: £47. - Delivery-fee support contacts per 1,000 orders. Ines to pull baseline; currently 12% of all contacts.

Sample size and duration

Baseline 8.0%, minimum effect 0.5 percentage points absolute, two-sided α = 0.05, power 80%.
Requires about 47,500 customers per arm (95,000 total).
Traffic is about 40,000 basket viewers per week, of whom about 20,000 are new. Unique customers therefore accumulate at roughly 40k in week 1 and 20k per week after that.
Plan: 4 weeks of enrolment, then a 7-day conversion window, for a fixed end date of [date]. Ines to confirm the unique-customer count, since the 20k-new-per-week figure is an estimate.
No decisions before the end date, with one exception: at week 2, Ines runs a harm-only check on guardrails. We stop early only if a guardrail shows harm at p < 0.001.

Decision rules

Ship if: - The primary metric is significantly positive (p < 0.05), with an estimated uplift of at least 0.5 percentage points, and - No guardrail is significantly worse.

Don't ship if: - The primary result is not significant, or - It is significant but below 0.5 points (not worth acting on, per Chloe), or - A guardrail is significantly worse.

We don't iterate on a null result by re-slicing the data until something looks significant.

Analysis (Ines)

Analyse at the customer level, intention-to-treat: all assigned customers, regardless of whether they saw the fee line.
Report absolute difference in percentage points and relative difference, each with a 95% confidence interval.
Report all guardrails.
Pre-registered segment: new vs returning customers. Returning customers may already know the fee, so the effect may differ. Treat any other segment cuts as exploratory.

Timeline and sign-off

Build complete: [date]
Launch: [date]
Fixed end: [date]
Readout: [date + 3 working days]

Approved by: _____ Chloe, Head of Growth Date: ___

Grades and run details

Decision model 77 · LLM judge 100

Decision model checks

failUses the supplied evidence correctly19%
partialAddresses the actual decision23%
passRespects explicit constraints14%
passIdentifies material uncertainty48%
partialAvoids unsupported claims20%
passProduces the required deliverable45%
passTests one change at a time98%
passFixes the duration up front82%
passAn unambiguous primary metric67%
passDecision rule written before the test89%
partialSized from the real traffic49%
passGuardrails with thresholds62%
partialSays why a test is worth it here89%

Run

Run: #1
API response time: 29 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 3

Uses the supplied evidence correctlyRightMixed

GPT-6 Astra · ChatGPT

All factual claims about the current situation are taken from the brief or follow by arithmetic; the pricing service is a reasonable inference from the delivery fee rules.

Haiku 5.5 · API

All statements about the current situation are directly from the supplied context or simple arithmetic; no facts are invented.

Guardrails with thresholdsWrongRight

GPT-6 Astra · ChatGPT

Guardrail metrics are named but no numerical thresholds are set; the spec defers approval of tolerances, leaving the spec incomplete.

Haiku 5.5 · API

Checkout start rate, completion rate, average order value, and delivery-fee support contacts are listed as guardrails, with a requirement that they are not significantly worse for rollout to proceed.

Says why a test is worth it hereWrongRight

GPT-6 Astra · ChatGPT

Does not explain why a test is worth running given the traffic and stakes; it assumes the test is justified without weighing alternatives.

Haiku 5.5 · API

The spec demonstrates the test is powered for the smallest change that matters, and the decision rules show the result would change the team's action, which justifies running the test.

All got right 10

Addresses the actual decisionRightRight

GPT-6 Astra · ChatGPT

Commits to a clear answer (run the experiment, roll out only if conversion gain ≥0.5pp, CI excludes zero, guardrails pass) and states what would change it.

Haiku 5.5 · API

The output commits to a clear experimental design and includes decision rules that map results to specific actions (ship/don't ship), which addresses the team's need to decide how to test and what to act on.

Respects explicit constraintsRightRight

GPT-6 Astra · ChatGPT

The spec is under 700 words, addresses the named readers, and is in the requested experiment-spec form.

Haiku 5.5 · API

The spec is within the 700-word limit, is addressed to Ravi, Ines, and Chloe, and proposes an actionable test plan.

Identifies material uncertaintyRightRight

GPT-6 Astra · ChatGPT

Names specific unknowns (baseline verification, guardrail tolerances, identity merging limitations) and says how they will be resolved before launch.

Haiku 5.5 · API

The output names uncertainty in the new-customer accumulation estimate and asks Ines to confirm, and notes the segment effect may differ; it bounds where possible.

Avoids unsupported claimsRightRight

GPT-6 Astra · ChatGPT

Hypotheses are labelled as such, and confident claims are supported by the brief.

Haiku 5.5 · API

Hypotheses, forecasts, and reasoning are clearly labelled as such; no confident claims go beyond the supplied evidence.

Produces the required deliverableRightRight

GPT-6 Astra · ChatGPT

The experiment spec is complete, under 700 words, and usable by Ravi, Ines, and Chloe with light edits.

Haiku 5.5 · API

The output is a complete experiment spec with sections for build, analysis, and sign-off, and is usable by the intended readers with minimal editing.

Tests one change at a timeRightRight

GPT-6 Astra · ChatGPT

Explicitly excludes the free-delivery banner from the variant and explains that adding it would prevent attribution.

Haiku 5.5 · API

The free-delivery banner is explicitly removed from the variant with a clear explanation that it would confound attribution, and it is suggested as a separate test.

Fixes the duration up frontRightRight

GPT-6 Astra · ChatGPT

Sets a fixed duration of at least four full weeks, states daily checks are for safety only, and prohibits stopping on significance.

Haiku 5.5 · API

The spec sets a fixed 4-week enrolment plus 7-day conversion window, explains that daily peeking inflates false positives, and only allows a harm-based early stop on guardrails.

An unambiguous primary metricRightRight

GPT-6 Astra · ChatGPT

Names one primary metric (customer order conversion), explains why an increase is unambiguously good, and includes a sample-ratio balance check.

Haiku 5.5 · API

A single primary metric (basket-to-order conversion) is named with a clear rationale for its direction, and a sample ratio mismatch check is planned.

Decision rule written before the testRightRight

GPT-6 Astra · ChatGPT

States a pre-defined rule: ship if conversion gain ≥0.5pp, CI excludes zero, guardrails pass; otherwise do not roll out, including for inconclusive results.

Haiku 5.5 · API

Every possible result is mapped to an action: ship if significant with ≥0.5pp uplift and guardrails safe; don't ship otherwise; no iteration on null results.

Sized from the real trafficRightRight

GPT-6 Astra · ChatGPT

Sample size (48k/arm) is calculated from the 8% baseline and 0.5pp MDE; duration uses the traffic data (40k unique/week, half new) and runs whole weeks.

Haiku 5.5 · API

Sample size is correctly derived from the 8% baseline, 0.5pp effect, 80% power; duration follows from the 40k weekly traffic and half-new accumulation, running whole weeks.

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 87% of 39 checks.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	Sonnet 5.5withAPI	85.2	96.4	2	None
2	Haiku 5.5withAPI	83.1	96.7	2	None
3	Opus 5.5withClaude	83.2	79.0	2	None
4	GPT-6 AstrawithChatGPT	81.3	76.0	2	None
5	GPT-6 LunawithAPI	83.4	65.2	2	None
6	GPT-6.1 SolwithAPI	77.7	69.3	2	None
7	Gemini 4 ArgonwithAPI	79.7	58.1	2	None
8	Gemini 3.8 FlashwithAPI	69.9	47.9	2	None
9	Gemini 3.5 Flash-LitewithGemini	55.4	13.3	2	1 capped

What you get for the cost

Haiku 5.5 · API scores within 2 points of Sonnet 5.5 · API at about a tenth 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	73	1.0×
Gemini 3.5 Flash-Lite · Gemini	32	2.1×
Haiku 5.5 · API	89	2.7×
Gemini 3.8 Flash · API	56	7.4×
Gemini 4 Argon · API	66	11×
GPT-6.1 Sol · API	73	13×
Sonnet 5.5 · API	91	33×
Opus 5.5 · Claude	78	49×
GPT-6 Astra · ChatGPT	76	79×

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

Specifying an A/B test before running it.

Why it matters

Most tests are underpowered, or measure a metric nobody agrees is good.

What good looks like

One primary metric everyone agrees on the direction of
Decision rule stated up front
Guardrails
Power considered

Deliberately not measured

Capability tested

Test design

The failure we’re looking for

A test with no decision attached

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

An unambiguous primary metric

Decision rule written before the test

Sized from the real traffic

Guardrails with thresholds

Says why a test is worth it here

Plus our standard checks

The tasks

The outputs

Output

Experiment: show delivery fees in the basket

Hypothesis and scope

Experience

Eligibility and assignment

Metrics

Sample size and schedule

Monitoring and decision

Grades and run details

Decision model checks

Run

Output

Experiment Spec: Delivery Fee in Basket

What changed from the draft

Hypothesis

Variants

Population and randomisation

Metrics

Sample size and duration

Decision rules

Analysis (Ines)

Timeline and sign-off

Grades and run details

Decision model checks

Run

Check by check

Where they differ 3

All got right 10

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured