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 16 graded outputs by 8 models. 31% 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
Addresses the actual decision91% pass
Commits to a clear experiment plan with a decision rule that maps every outcome (ship, don't ship, kill, extend) to an action.
Sonnet 5.5 · API · Show the fee sooner

Where it slips

Guardrails with thresholds50% 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
Says why a test is worth it here59% 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
Uses the supplied evidence correctly64% pass
Unsupported claims about the traffic data's adequacy and the code freeze's preapproval requirement are presented as facts, not in the brief.
GPT-6.1 Sol · API · 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 · v1.3

The outputs

8 setups answered this brief.

Sonnet 5.5API93Usable with a quick edit Haiku 5.5API93Usable with a quick edit GPT-6 AstraChatGPT80Needs more work GPT-6.1 SolAPI79Usable with a quick edit GPT-6 LunaAPI77Usable with a quick edit Opus 5.5Claude75Needs more work Gemini 3.8 FlashAPI48Needs more work Gemini 3.5 Flash-LiteGemini18Critical failure

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 85 · LLM judge 100

Decision model checks

failUses the supplied evidence correctly33%
passAddresses the actual decision79%
passRespects explicit constraints14%
passIdentifies material uncertainty45%
passAvoids unsupported claims28%
passProduces the required deliverable34%
passTests one change at a time95%
passFixes the duration up front97%
passAn unambiguous primary metric77%
passDecision rule written before the test41%
partialSized from the real traffic40%
partialGuardrails with thresholds91%
passSays why a test is worth it here62%

Run

Run: #1
API response time: 29 s
Submitted: 8 Oct 2026

Check by check

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

Mixed 1

Uses the supplied evidence correctlyMixed

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

Got right 12

Addresses the actual decisionRight

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 constraintsRight

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

Identifies material uncertaintyRight

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 claimsRight

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

Produces the required deliverableRight

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 timeRight

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 frontRight

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 metricRight

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 testRight

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 trafficRight

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.

Guardrails with thresholdsRight

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 hereRight

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.

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 85% of 39 checks.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	Sonnet 5.5withAPI	92.6	96.4	2	None
2	Haiku 5.5withAPI	83.4	96.7	2	None
3	Opus 5.5withClaude	88.7	79.0	2	None
4	GPT-6 AstrawithChatGPT	88.9	76.0	2	None
5	GPT-6 LunawithAPI	89.1	65.2	2	None
6	GPT-6.1 SolwithAPI	83.1	69.3	2	None
7	Gemini 3.8 FlashwithAPI	77.3	47.9	2	None
8	Gemini 3.5 Flash-LitewithGemini	47.8	13.3	2	1 capped

What you get for the cost

No setup scores within 5 points of the leader at half its cost or less here, so the leader is also the best value.

Nobody 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	76	1.0×
Gemini 3.5 Flash-Lite · Gemini	29	2.1×
Haiku 5.5 · API	89	2.7×
Gemini 3.8 Flash · API	60	7.4×
GPT-6.1 Sol · API	75	13×
Sonnet 5.5 · API	95	33×
Opus 5.5 · Claude	81	49×
GPT-6 Astra · ChatGPT	79	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