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

Gemini 3.8 FlashAPI

Needs more than a quick edit4 of 13 checks passed

The duration is miscalculated (3 weeks instead of 4), guardrail thresholds are missing, and the decision rule lacks an inconclusive branch. These require rework beyond a quick edit.

Output

Experiment Spec: Delivery Fee Transparency in Basket

To: Chloe (Approval), Ravi (Build), Ines (Analysis) From: Checkout PM

---

1. Objective & Hypothesis

Context: 12% of support contacts complain about surprise delivery fees at final checkout.
Hypothesis: Displaying the delivery fee (£3.99, or "Free" if >£60) upfront in the basket will set accurate expectations, reduce support contacts, and build customer trust without reducing net orders.
Risk (Chloe’s concern): Upfront cost shock could cause drop-off before checkout begins.

---

2. Corrections to the Draft Plan

1. Primary Metric Changed: Draft used Checkout Completion Rate (Orders ÷ Starts). Correction: Use Basket-to-Order Conversion Rate (Orders ÷ Basket Viewers). Measuring only checkout starts introduces severe selection bias—if fee visibility deters low-intent users at the basket, checkout completion would artificially increase while overall orders plummet. 2. Unit of Randomization Changed: Draft used session-level. Correction: Randomize by User ID (falling back to persistent anonymous visitor ID). 50% of weekly viewers are repeat shoppers; session-level randomization would expose returning users to conflicting experiences. 3. No Peeking / Fixed Duration: Draft suggested stopping once "significant." Correction: Continuous monitoring with early stopping inflates false-positive rates to >30%. We will fix sample size upfront and run for complete weekly cycles. 4. Scope Control (Chloe’s Banner): Cut the promotional free-delivery banner from this test. Adding a promotional banner introduces a confounding variable, making it impossible to isolate whether fee transparency helped or hurt. We will test the banner in a follow-up experiment.

---

3. Variants (For Ravi)

Allocation: 50/50 split at User/Persistent Device ID level. Triggered on basket view.
Control (A): Current basket. No delivery fee shown (fee appears on final checkout step as today).
Variant (B): Add a delivery fee line item to the Basket Order Summary:
Basket < £60: `Delivery: £3.99`
Basket ≥ £60: `Delivery: FREE`
Include delivery fee in the displayed Basket Total.
Note: Do not add promotional progress bars or marketing banners.

---

4. Metrics Framework (For Ines)

Primary Metric: Basket-to-Order Conversion Rate (Total Unique Ordering Users ÷ Total Unique Basket Viewers).
Guardrail Metrics:
Average Order Value (AOV baseline: £47).
Basket-to-Checkout Start Rate (tracks where abandonment occurs).
Total Net Revenue (Orders × AOV).
Secondary / Diagnostic Metrics:
Delivery-fee-related customer support ticket rate per 1,000 orders.
Checkout Completion Rate (Checkout Starts → Orders).

---

5. Sample Size & Run Time (For Ines & Chloe)

Baseline Conversion: 8.0% of basket viewers place an order.
Minimum Detectable Effect (MDE): Absolute ±0.5 percentage points (7.5% or 8.5%, a 6.25% relative change), per Chloe's threshold.
Statistical Parameters: $\alpha = 0.05$ (two-tailed), Power ($1 - \beta$) = 80%.
Required Sample: ~47,000 unique users per variant (~94,000 total unique basket viewers).
Duration: 3 full weeks (21 days).
With 40,000 viewers/week (~20k new, ~20k repeat), 3 weeks yields ~80,000–90,000 unique basket viewers across three full grocery-shopping cycles.
Ines will run the read-out strictly after the 21-day window closes.

---

6. Decision & Rollout Criteria (For Chloe)

Ship Variant: Primary conversion does not drop by more than Chloe’s threshold of -0.5pp (non-inferiority) AND delivery-related support contacts decrease significantly.
Iterate / Re-test: Basket-to-order conversion drops by >0.5pp. In this scenario, early fee disclosure hurts conversion, justifying Chloe’s concern; we then test the £60 threshold banner/progress bar to offset drop-off.
Kill Immediately: Overall revenue drops >5% statistically significantly at the end of Week 1 (safety check only; not an early win stop).

Grades and run details

Decision model 65 · LLM judge 36

Decision model checks

failUses the supplied evidence correctly54%
passAddresses the actual decision66%
partialRespects explicit constraints27%
passIdentifies material uncertainty64%
partialAvoids unsupported claims48%
partialProduces the required deliverable28%
passTests one change at a time100%
passFixes the duration up front73%
partialAn unambiguous primary metric31%
partialDecision rule written before the test82%
partialSized from the real traffic94%
partialGuardrails with thresholds89%
passSays why a test is worth it here31%

Run

Run: #1
API response time: 19 s
Submitted: 1 Oct 2026

Gemini 3.5 Flash-LiteGemini

Needs more than a quick edit0 of 13 checks passed

The spec bundles the banner, uses an incorrect duration, and lacks a decision rule and guardrail thresholds. A PM would need to redo the sizing and separate the banner test.

Output

To: Chloe (Head of Growth), Ravi (Growth Engineer), Ines (Data Analyst) From: PM, Checkout Subject: Experiment Spec: Early Delivery Fee Display in Basket

Team, here is the polished spec for our upcoming checkout experiment. I’ve incorporated Chloe’s threshold, fixed a few methodological traps in the draft, and scoped this for a clean, reliable read.

---

1. Objective & Hypothesis

Current State: Delivery fee (£3.99, free over £60) is only revealed on the final checkout step. 12% of last quarter’s support contacts were fee-related surprises.
Hypothesis: Showing the delivery fee (and progress to free delivery) directly in the basket increases customer trust and sets clear expectations upfront.
Expected Impact: While Chloe rightly worries this might cause some upfront drop-off among sub-£60 baskets, we believe the resulting higher-intent checkout starts will lift overall conversion and reduce cart abandonment due to "sticker shock."

---

2. Variant Definition

Control (A): Existing basket experience. Delivery fee hidden until final checkout step.
Variant (B):
Displays the delivery fee clearly in the basket summary.
Note on scope: Per Chloe’s suggestion, we are bundling the "free delivery over £60" progress banner into Variant B. Since the fee and the threshold are conceptually tied, showing one without the context of the other would skew the test.

---

3. Traffic Allocation & Unit of Randomization

Unit of Randomization: User-level (logged-in state or persistent cookie). Correction from draft: Session-level splitting would cause a jarring experience if a user refreshes or returns and sees fluctuating fee visibility.
Split: 50/50 (Control / Variant).
Audience: All customers viewing the basket (~40,000 unique users/week, split roughly 50/50 between new and returning).

---

4. Metrics

Primary Metric (Guardrail for Business Value):
Order Conversion Rate: Completed Orders ÷ Total Basket Views.
(Correction from draft: The draft proposed "Checkout Completion Rate (Orders ÷ Checkout Starts)." That misses top-of-funnel drop-off. If Variant B scares people away before checkout, measuring only checkout-starts will falsely make the variant look better. We must measure from the basket view).
Secondary Metrics:
Checkout Start Rate: Checkout Starts ÷ Basket Views.
Checkout Completion Rate: Orders ÷ Checkout Starts (to isolate downstream behavior).
Average Order Value (AOV): To monitor if the £60 threshold banner successfully drives basket-building behavior (baseline: £47).
Support Metric:
Volume of delivery-fee-related support tickets (Ines, let’s pull this baseline post-launch).

---

5. Success Criteria & Statistical Guardrails

Minimum Detectable Effect (MDE): Per Chloe, we care about a ≥ +0.5 percentage point absolute change in the Primary Metric (Order Conversion Rate from basket view, baseline ~8%).
Duration & Peeking:
Correction from draft: Do not stop the test the moment p < 0.05. Early peeking yields false positives.
We will run the test for a fixed duration of 2 full weeks to capture day-of-week and new vs. returning user cycles (~80,000 total unique users), yielding adequate statistical power for our MDE.

---

Next Steps:

Ravi: Let’s sync on tracking implementation for the basket-level fee and banner. Target build completion: [Insert Date].
Ines: Please set up the tracking dashboard, ensuring we segment by New vs. Returning users and Basket Value (<£60 vs. >£60).
Chloe: Good to approve?

Claims the judge couldn’t find in the brief

We will run the test for a fixed duration of 2 full weeks ... (~80,000 total unique users).
yielding adequate statistical power for our MDE.
Session-level splitting would cause a jarring experience if a user refreshes or returns and sees fluctuating fee visibility.

Grades and run details

Decision model 38 · LLM judge 0

Decision model checks

failUses the supplied evidence correctly16%
failAddresses the actual decision67%
passRespects explicit constraints2%
partialIdentifies material uncertainty75%
partialAvoids unsupported claims30%
partialProduces the required deliverable77%
failTests one change at a time99%
partialFixes the duration up front80%
partialAn unambiguous primary metric64%
partialDecision rule written before the test49%
partialSized from the real traffic74%
partialGuardrails with thresholds41%
failSays why a test is worth it here60%

Run

Run: #1
Time to output: 5 s
Submitted: 30 Sept 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 13

Uses the supplied evidence correctlyMixedMixedWrong

Haiku 5.5 · API

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

Gemini 3.8 Flash · API

All statements about the current situation are taken directly from the brief and are accurate.

Gemini 3.5 Flash-Lite · Gemini

The output claims 80,000 total unique users over 2 weeks and adequate power, neither of which is supported by the brief or arithmetic.

Addresses the actual decisionRightRightWrong

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.

Gemini 3.8 Flash · API

The spec commits to a clear decision rule with ship, iterate, and kill actions based on results.

Gemini 3.5 Flash-Lite · Gemini

The output does not commit to a clear decision rule or say what would change the call; it only states a threshold without mapping outcomes to actions.

Respects explicit constraintsRightRightMixed

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.

Gemini 3.8 Flash · API

The output is under 700 words, addresses the three named readers, and fixes the draft's problems.

Gemini 3.5 Flash-Lite · Gemini

The brief asks to fix the draft plan, but the output keeps the free-delivery banner bundled in the variant, failing to fix that problem.

Identifies material uncertaintyRightMixedWrong

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.

Gemini 3.8 Flash · API

No plan is given for an inconclusive result (e.g., if the difference is not significant or support contacts don't decrease), and the sample-size uncertainty from the 3-week duration is not addressed.

Gemini 3.5 Flash-Lite · Gemini

The output does not name unknowns that could change the decision or how they would be resolved.

Avoids unsupported claimsRightWrongWrong

Haiku 5.5 · API

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

Gemini 3.8 Flash · API

The claim that continuous monitoring inflates false-positive rates to >30% is presented as fact without support from the brief.

Gemini 3.5 Flash-Lite · Gemini

The output presents the 2-week duration as yielding adequate power without labeling it as an assumption or hypothesis.

Produces the required deliverableRightRightWrong

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.

Gemini 3.8 Flash · API

The output is a complete experiment spec in the requested format, usable by the named readers with light edits.

Gemini 3.5 Flash-Lite · Gemini

The spec lacks a decision rule, guardrail thresholds, and correct sizing; it is not usable as is.

Tests one change at a timeRightRightWrong

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.

Gemini 3.8 Flash · API

The free-delivery banner is explicitly removed from the variant and a follow-up test is proposed, with a clear explanation of the confounding risk.

Gemini 3.5 Flash-Lite · Gemini

The spec bundles the free-delivery banner into the variant and argues it is necessary, so the effect cannot be attributed to the fee change alone.

Fixes the duration up frontRightMixedWrong

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.

Gemini 3.8 Flash · API

The duration is set at 3 weeks, but the required sample of ~94,000 unique users needs about 4 weeks to accumulate; the fixed duration is not correctly derived from the sample size.

Gemini 3.5 Flash-Lite · Gemini

The fixed duration of 2 weeks is not derived from the sample size needed; it is an arbitrary choice, not based on the traffic and MDE.

An unambiguous primary metricRightWrongWrong

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.

Gemini 3.8 Flash · API

The primary metric is clearly stated and rationalized, but no trust check (e.g., sample ratio check) is planned before reading the results.

Gemini 3.5 Flash-Lite · Gemini

The spec does not include a trust check such as a sample ratio check to verify the experiment's integrity.

Decision rule written before the testRightWrongWrong

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.

Gemini 3.8 Flash · API

The rule does not cover an inconclusive outcome (e.g., non-significant result or support contacts not decreasing) and lacks a stated action for that case.

Gemini 3.5 Flash-Lite · Gemini

No decision rule is stated; the output does not map possible outcomes (win, lose, inconclusive) to actions.

Sized from the real trafficRightWrongWrong

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.

Gemini 3.8 Flash · API

The sample size calculation is correct, but the 3-week duration yields only ~80,000–90,000 unique viewers, which is below the required ~94,000; the duration is not sufficient.

Gemini 3.5 Flash-Lite · Gemini

The 2-week duration is insufficient given the 8% baseline, 0.5pp MDE, and 40k weekly users with half returning; proper sizing requires about 4 weeks.

Guardrails with thresholdsRightWrongWrong

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.

Gemini 3.8 Flash · API

Guardrail metrics like AOV and basket-to-checkout start rate are listed but no explicit thresholds are set that would block a rollout.

Gemini 3.5 Flash-Lite · Gemini

Guardrail metrics are listed but no thresholds are set that would block a rollout.

Says why a test is worth it hereRightMixedWrong

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.

Gemini 3.8 Flash · API

The spec does not weigh whether a test is worth running given the traffic and stakes, nor does it suggest an alternative if a test is not justified.

Gemini 3.5 Flash-Lite · Gemini

The output does not discuss whether a test is worth running given the traffic and stakes.

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

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 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

Output

Experiment Spec: Delivery Fee Transparency in Basket

1. Objective & Hypothesis

2. Corrections to the Draft Plan

3. Variants (For Ravi)

4. Metrics Framework (For Ines)

5. Sample Size & Run Time (For Ines & Chloe)

6. Decision & Rollout Criteria (For Chloe)

Grades and run details

Decision model checks

Run

Output

1. Objective & Hypothesis

2. Variant Definition

3. Traffic Allocation & Unit of Randomization

4. Metrics

5. Success Criteria & Statistical Guardrails

Next Steps:

Claims the judge couldn’t find in the brief

Grades and run details

Decision model checks

Run

Check by check

Where they differ 13

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured