Tasks / Experiment

Experiment specification

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

Measures the modelTask v1.0 · 2 casesDifficulty

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

From 9 graded outputs by 5 models. 56% were usable with at most a quick edit.

Reliably right

  1. A realistic plan that beats the freeze100% pass
    It converts 740 windows to about 9 days implicitly, explains that correlation and weekly cycles require a longer test (20 days, ~3 weeks), and provides dates that fit before 6 November with Porto notice given on 30 September.
    GPT-6.1 Sol · API · Batching deliveries before peak season
  2. Addresses the actual decision94% pass
    The output commits to a clear decision rule: launch only if all gates pass, otherwise do not launch, and an inconclusive result is not a pass.
    GPT-6.1 Sol · API · Batching deliveries before peak season
  3. Identifies material uncertainty92% pass
    It identifies unknowns like power under correlation, carryover, and city differences, and states that if power is insufficient the rollout will not proceed, resolving the uncertainty.
    GPT-6.1 Sol · API · Batching deliveries before peak season

Where it slips

  1. An unambiguous primary metric53% pass
    The spec names courier cost per order as the primary metric with a rationale, but it does not include a planned trust check such as a sample ratio check or verification that the arms receive the scheduled share of windows and similar order volumes.
    Sonnet 5.5 · API · Batching deliveries before peak season
  2. Guardrails with thresholds58% pass
    Guardrails are named but lack specific numeric thresholds; 'materially worse' and 'drops meaningfully' are too vague to block rollout unambiguously.
    Sonnet 5.5 · API · Showing the delivery fee up front
  3. Sized from the real traffic58% pass
    Sample size is correct, but the test stops when the enrolment target is reached rather than running fixed whole weeks.
    GPT-6.1 Sol · API · Showing the delivery fee up front

Case viewer

Read the brief, then put up to three outputs side by side, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're a Staff PM at Brisk. We want to know whether to roll out order batching (one courier carrying two orders from nearby restaurants) to all 14 of our cities before the peak-season code freeze on 6 November 2026. Write the experiment spec. Our COO, CFO, Head of Operations and courier relations lead will all sign it off, and each wants something different from it. Keep it under 1,200 words. A draft plan from our data science intern is below. Fix what needs fixing.

About BriskFood delivery in 14 European cities, about 1.9 million orders a month. Couriers are paid per order plus distance. Courier cost per order averages €7.40.
What batching should doCOO: “Roll batching out before the freeze if it cuts courier cost per order by at least 5% without making customers wait noticeably longer.” Simulations suggest it cuts courier cost per order by 6–11% and adds 3–6 minutes to the second order in each batch.
What each exec wants to seeCFO: courier cost per order. Head of Operations: the share of orders delivered more than 45 minutes after ordering (today 7.5%). Courier relations lead: courier earnings per active hour (today €13.20). Head of Growth, copied in: 30-day reorder rate.
Courier agreement in PortoOur agreement with the Porto couriers' association requires 14 days' written notice of any change to how orders are assigned, and says changes must not reduce couriers' average hourly earnings.
TimelineToday is 30 September. Engineering needs one week to put batching behind a switch that can be turned on and off per city at any time. Rollout to all cities takes a day. Operating hours are 11:00 to 23:00 in every city.
The intern's draft planRandomise orders 50/50 in every city: orders in the treatment group can be batched, control orders never are. Success metrics: courier cost per order, late deliveries, courier earnings per hour and 30-day reorder rate. Run for two weeks.
Analyst's noteIf we switch batching on and off by city in two-hour windows, detecting a 5% change in courier cost per order needs about 740 windows in total, assuming each window is independent of the others.
What a strong answer does

A spec that sees the draft can't work: batched and unbatched orders in the same city share one pool of couriers, so batching in the treatment group frees couriers for the control group and contaminates the comparison. It uses a switchback design instead (batching on or off by city in randomised two-hour windows), with a buffer at each switch (for example, orders placed shortly before a switch are excluded or assigned by dispatch time) so carry-over doesn't blur the arms. It picks courier cost per order as the one primary metric, because cutting it is batching's purpose and a fall is unambiguously good, and makes the others guardrails with thresholds: late deliveries (for example, no more than a 1pp rise from 7.5%), courier earnings per active hour (no fall, which the Porto agreement requires), with 30-day reorder rate named as a follow-up because it can't be read before the freeze. It sizes the test honestly: 14 cities × 6 windows a day is 84 windows, so 740 windows is about 9 days, but windows in the same city on the same day aren't independent, so it needs more, and a full weekly cycle plus time for couriers to adapt argue for about three weeks. It fits the timeline: build ready around 7 October, Porto given notice now and joining once its 14 days are up (or excluded), about three weeks of data, a read-out around 1 November and rollout before 6 November. It plans trust checks (the arms get the scheduled share of windows and similar order volumes; batching actually happens in 'on' windows), and states the decision rule up front: roll out if cost per order falls at least 5% and guardrails hold; don't if a guardrail is breached; and what happens if the result is inconclusive by the freeze.

Critical failures (cap the score)
  • Randomises by order or by customer within a city, where both arms share the same couriers
  • Changes order assignment in Porto without the 14 days' notice
Case

v1.0 · synthetic · marketplace, logistics, switchback, network effects, staff-level

Usable with a quick edit11 of 12 checks passed

The spec is complete and actionable; a quick edit to add a sample ratio check or similar trust check would make it fully robust.

Output

Order Batching Experiment Spec

Decision: Roll out batching to all 14 cities before the 6 Nov 2026 code freeze, or not. Owner: Staff PM. Sign-off: COO, CFO, Head of Operations, Courier Relations Lead. Head of Growth is copied in.

1. What changed from the intern's draft

DraftProblemFix
Randomise orders 50/50 within each cityTreated and control orders share the same couriers and the same pool of nearby orders. Halving the batchable pool understates batching, and control orders are affected by treatment (couriers are tied up on batches). Both effects bias the result.Switch batching on and off by city and two-hour window (switchback).
Porto included from day oneThe agreement requires 14 days' written notice of any change to order assignment, and a test is such a change.Send notice today. Porto joins on 15 Oct.
Four metrics, no thresholdsNobody can say what "good" means.Pre-agreed decision rules (section 4).
30-day reorder rate as a success metricA test starting in October cannot produce a 30-day read before 6 Nov.Monitor it, but it does not gate the decision.
Two weeks, no justificationSee section 3.Three full weeks.

2. Design

  • Unit: city × two-hour window. The windows are 11–13, 13–15, 15–17, 17–19, 19–21 and 21–23, so six per city per day.
  • Assignment: Within each city-day, the windows form three adjacent pairs. In each pair, one window is randomly batching-on and the other is off. This balances lunch, afternoon and dinner across arms and controls for city and weekday.
  • Attribution: An order belongs to the window in which it was placed. The pre-specified sensitivity analysis drops orders placed in the first 30 minutes of each window, because in-flight batches and courier positioning carry over from the previous window.
  • Exposure: Batching pairs only nearby restaurants, using the same rules the simulation assumed. The batching configuration is frozen before the test starts.

3. Duration and power

The analyst's figure of about 740 windows assumes windows are independent. They are not: adjacent windows share couriers and demand, and cities differ. I therefore treat 740 as a floor and plan for up to double that.

  • Engineering needs one week, so the switch is ready on 7 Oct and the test runs Thu 8 Oct – Wed 28 Oct (21 days, three of every weekday).
  • 13 cities × 6 windows × 21 days = 1,638 windows. Porto adds 6 × 14 days = 84. The total is about 1,720 windows, roughly 2.3× the floor.
  • We do not stop early on a good-looking interim result. The end date is fixed.
  • Analysis: A window-level regression on total cost ÷ total orders, with city and weekday × hour fixed effects and standard errors clustered by city-day. We will confirm with randomisation inference.

4. Metrics and decision rules

All four sign-offs are needed on these thresholds by Tue 6 Oct, before any data exist.

OwnerMetricRoleRule to ship
COO / CFOCourier cost per order (today €7.40)PrimaryEstimated reduction is ≥5% and the 95% CI excludes zero
Head of OpsShare of orders delivered >45 min after ordering (today 7.5%)GuardrailIncrease of no more than +1.0 pp (upper 95% CI bound ≤ 8.5%)
Courier RelationsCourier earnings per active hour (today €13.20)GuardrailLower 95% CI bound of the difference ≥ −€0.13 (about −1%)
Head of GrowthReorder rateMonitor onlyReported, does not gate

The COO's "without making customers wait noticeably longer" is operationalised as the +1.0 pp cap. The simulations add 3–6 minutes to the second order in each batch, so this cap is the threshold most likely to be contested. The Head of Ops should confirm or change it before the start.

Tension to surface: Cost per order is forecast to fall 6–11%, and couriers are paid per order plus distance. Earnings per hour therefore stay flat only if batching raises orders per courier-hour by at least as much as pay per order falls. The test settles this, and the CFO's and Courier Relations' metrics are not independent.

Secondary metrics (descriptive only): - Mean delivery time, and delivery time for the second order in a batch. - Share of orders batched. - Orders per courier-hour. - Customer support contacts. - Cancellations.

Reorder rate: We report the 7-day rate for test customers at the decision. The 30-day rate is tracked after rollout for customers by batched-order exposure, and it cannot be treated as causal. Growth should know this now.

5. Porto

  • Written notice goes to the couriers' association today (30 Sept). Legal should word it to cover both the test and permanent rollout. The earliest compliant start is 14 Oct, so Porto joins the test on 15 Oct.
  • The contract says changes must not reduce average hourly earnings. Porto therefore has a stricter rule than other cities: the estimated earnings difference must be ≥ €0 for Porto to ship.
  • Porto has only about 84 windows, so its own estimate will be noisy. We pool it for cost and lateness. For earnings we report Porto separately, daily, with the stop rule below.
  • If Porto fails or is ambiguous, we ship in the other 13 cities and keep Porto off. Legal should confirm that running the test in Porto does not itself breach the agreement. If it might, exclude Porto from the test and roll out there only on the pooled evidence plus the earnings rule.

6. Stop rules

The per-city switch lets us turn batching off immediately. Any of these triggers a pause in that city and a review within 24 hours:

  • The >45-minute share in batching-on windows exceeds off windows by more than 3 pp over two consecutive days.
  • Earnings per active hour in batching-on windows are more than 5% below off windows over three consecutive days. In Porto, the threshold is any sustained shortfall that the Courier Relations Lead judges real.
  • Any safety or serious courier-welfare incident tied to batching.

7. Timeline

DateMilestone
Wed 30 SeptSpec circulated. Porto notice sent. Engineering starts the per-city switch.
Tue 6 OctThresholds signed off. Randomisation schedule locked.
Wed 7 OctSwitch ready, QA complete
Thu 8 OctTest starts in 13 cities
Thu 15 OctPorto joins
Wed 28 OctTest ends
Thu 29 OctAnalysis and go/no-go with all four sign-offs
Fri 30 OctRollout to approved cities (one day)
30 Oct – 5 NovPost-rollout monitoring with the switch live. The freeze is on 6 Nov.

The 29 Oct decision leaves a week of buffer. If the test slips past about 2 Nov, there is no safe rollout before the freeze.

8. Risks and limits

  • Seasonality: October demand is below peak. Higher order density should raise batch rates, but it also strains courier supply. We track batch rate by hour and expect the peak effect to differ from the test estimate.
  • Carryover: Some carryover between windows will remain even with the 30-minute washout. The sensitivity analysis shows how much it matters.
  • Short-run effects: Couriers may change their behaviour as they learn batching, and three weeks may not capture that. We monitor weekly trends within the test.
  • Possible outcomes: The result can be a full rollout, a partial rollout (for example excluding Porto or any city that fails a guardrail), or no rollout. Partial rollout by city is allowed by the switch, but only for cities that individually pass the stop rules. City-level results will be noisy, so we do not make ship decisions city by city beyond this.

9. Sign-off

RoleAgrees to
COODecision rule, timeline
CFOCost metric definition (fully loaded courier pay ÷ delivered orders), ≥5% threshold
Head of Ops+1.0 pp lateness cap, stop rules
Courier RelationsEarnings guardrail, Porto notice and rule

Check by check

Got wrong · 1
  • An unambiguous primary metricThe spec names courier cost per order as the primary metric with a rationale, but it does not include a planned trust check such as a sample ratio check or verification that the arms receive the scheduled share of windows and similar order volumes.
Got right · 11
  • Uses the supplied evidence correctlyAll statements about the current situation are taken directly from the brief or follow by arithmetic.
  • Addresses the actual decisionThe spec commits to a conditional rollout decision with clear thresholds and states what would change the call.
  • Respects explicit constraintsThe spec is under 1,200 words, respects the Porto notice and earnings requirement, and fits the timeline before the freeze.
  • Identifies material uncertaintyIt names seasonality, carryover, courier adaptation, and Porto legal risk, and says how each would be resolved or monitored.
  • Avoids unsupported claimsInterpretations and forecasts are clearly labelled as such; no factual claim is presented as established without support.
  • Produces the required deliverableThe output is a complete experiment spec in the requested form, under the word limit, and usable by the sign-off group.
  • Avoids contamination between the armsIt explains the shared-courier contamination problem and uses a switchback design with a 30-minute buffer to handle carry-over.
  • A realistic plan that beats the freezeIt correctly treats 740 windows as a floor, accounts for dependence and weekly cycles with 21 days, and fits the build, Porto notice, and decision before 6 November.
  • Decision rule written before the testThe decision rule is stated before the test: ship if cost reduction ≥5% with CI excluding zero and guardrails hold; otherwise no rollout, including the inconclusive case.
  • Sized from the real trafficSample size is derived from the analyst's 740-window floor, increased for dependence, and the duration runs three full weeks using the actual traffic and operating hours.
  • Guardrails with thresholdsGuardrails (late deliveries, courier earnings) are named with explicit thresholds that would block rollout.

Grades and run details

Decision model 83 · LLM judge 12 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly13%
  • passAddresses the actual decision14%
  • partialRespects explicit constraints27%
  • passIdentifies material uncertainty98%
  • passAvoids unsupported claims34%
  • passProduces the required deliverable63%
  • passAvoids contamination between the arms100%
  • passA realistic plan that beats the freeze100%
  • partialAn unambiguous primary metric48%
  • partialDecision rule written before the test77%
  • partialSized from the real traffic40%
  • passGuardrails with thresholds97%
Run
Run
#1
API response time
67 s
Submitted
30 Sept 2026

Results

Every setup we’ve tested on this task, across all cases and repeats, graded on the current checklist. Provisional The checklist is still being calibrated against our PM.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6.1 SolwithAPI85.488.52None
2Sonnet 5.5withAPI87.588.52None
3Opus 5.5withClaude79.276.92None
4GPT-6 LunawithAPI81.373.12None
5Gemini 3.5 Flash-LitewithGemini33.315.411 capped

About the task

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