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 edit10 of 12 checks passed

The spec is comprehensive and only needs a quick edit to add a trust check and rephrase one unsupported claim; no re-checking of evidence or redoing of reasoning is required.

Output

Experiment spec: Order batching before the peak-season freeze

Decision owner: COO · Sign-off: COO, CFO, Head of Operations, Courier Relations Lead · Copied: Head of Growth Status: Draft for sign-off, 30 September 2026

1. Decision

Should we switch batching on in all 14 cities before the code freeze on 6 November 2026? The switch works per city, so the result can also be "on in some cities."

2. What changed from the draft plan, and why

DraftProblemFix
Randomise orders 50/50Treatment and control orders share the same couriers. Batching frees courier time, and that spare capacity speeds up control orders, which shrinks the measured difference. Earnings per courier hour can't be split by order at all.Switchback test: randomise each city in 2-hour windows. Everything in a window is either on or off.
Runs in every city from day onePorto needs 14 days' written notice. The earliest a notice sent today can take effect is 14 October.Send notice today. Porto joins on 14 Oct.
"Late deliveries"UndefinedShare of orders delivered >45 min after ordering (baseline 7.5%)
30-day reorder rate as a success metricIt can't be measured before the freeze. In a switchback, each customer also sees both arms.Monitor it after rollout. It is not a decision metric.
No thresholds or stop rulesWe can't make a pass/fail callThresholds in §5, stop rules in §7
Two weeks, assuming 740 independent windowsWindows are not independent. Batches straddle switch times, couriers reposition, and windows on the same city-day are correlated.Exclude a washout period from each window, cluster the analysis, and run 21 days (~1,700 windows).

3. Timeline

DateMilestone
Wed 30 SepWritten notice to the Porto couriers' association. It covers both the test and a possible rollout. Legal to confirm wording today.
30 Sep – 7 OctEngineering builds the per-city switch. Analytics builds the randomisation schedule and dashboard.
Thu 8 OctTest starts in 13 cities
Wed 14 OctPorto joins (notice period complete)
Wed 28 OctTest ends
29 Oct – 1 NovAnalysis
Mon 2 NovDecision meeting
Tue 3 NovRollout (one day)
4–5 NovBuffer for rollback or fixes. Freeze on 6 Nov.

The dates are fixed by two constraints. The Porto notice cannot slip past today without losing Porto days. The test cannot run past 28 October without eating into the rollout buffer.

4. Design

  • Unit: one city in one 2-hour window. Each day has six windows (11–13, 13–15, 15–17, 17–19, 19–21, 21–23).
  • Assignment: randomised 50/50 within each city and time slot. Each city gets a balanced mix of on and off across lunch, dinner, weekdays and weekends.
  • Switch behaviour: new batches form only while the switch is on. Batches already in progress when it turns off complete normally.
  • Washout: the first 30 minutes of each window are excluded from analysis. This leaves 90 analysed minutes per window.
  • Sample: 13 cities × 6 windows × 21 days plus Porto × 6 × 15 days gives 1,728 windows. That is about 2.3× the analyst's 740. The extra covers the washout and the correlation between windows. Analytics to confirm the power calculation using the correlation seen in the first week's data. If power is short, we extend to 30 October and give up one buffer day.
  • Analysis: compare window-level metrics with adjustments for city, day and time slot. Treat all windows in the same city on the same day as one group when calculating uncertainty. Report the network result and each city's result.

5. Metrics and decision rule

MetricOwnerBaselineRule
Courier cost per orderCFO€7.40Primary. Pass if the estimated reduction is ≥5% (≥€0.37) and the 95% confidence interval excludes zero.
Share of orders delivered >45 minHead of Ops7.5%Guardrail. Fail if the increase is more than 1.0 percentage point (to 8.5%).
Courier earnings per active hourCourier Relations€13.20Guardrail. Fail if the drop is more than €0.40 (3%). In Porto, any drop fails.
Median delivery time, second order in a batchHead of Opsn/aDiagnostic. Simulation expects +3–6 min.
Complaints and refunds per 1,000 ordersHead of Opsn/aDiagnostic. This is our short-term stand-in for customer harm.
Share of orders batchedAll0%Diagnostic. Shows whether batching is actually happening.
30-day reorder rateGrowthn/aTracked after rollout. Not a decision metric.

Rollout rule: a city rolls out on 3 Nov if the network result passes the primary metric and both guardrails, and that city's own result shows no clear guardrail failure.

The 1.0pp and €0.40 margins are proposals. Signers must confirm or change them before 8 October. They cannot be changed after the test starts.

6. Tensions signers should know about

What "at least 5%" can mean. We can reliably detect whether batching cuts cost by 5% versus not at all. We cannot prove the true effect is above 5% if it is only 6%, which is the low end of the simulation. The decision rule therefore uses the point estimate. If the estimate lands between 4% and 6%, the COO decides with that uncertainty in view.

Cost savings and courier earnings pull in opposite directions. Couriers are paid per order plus distance, so a lower cost per order is lower pay per order. Earnings per hour only hold up if couriers complete more orders per hour.

  • During the test, the same couriers are logged in whether batching is on or off. That makes the test a fair early read on hourly earnings.
  • After rollout, the saving only stays a saving if we need fewer courier hours. Fewer hours affects couriers' income, even if the hourly rate is protected.
  • The CFO and Courier Relations Lead should agree on this before sign-off, not after results.

Porto. "Must not reduce average hourly earnings" is a contractual condition, not a statistical margin. Porto gives us only ~90 windows, too few to prove "no reduction."

  • If network earnings per hour fall at all, Porto stays off unless it comes with an earnings protection mechanism. One example is a per-batch supplement, which legal and Courier Relations should scope now.
  • If Porto rolls out, we measure its hourly earnings against the pre-batching baseline every week.

"Noticeably longer." We are defining this as the >45-minute late share plus the complaint rate. We are not using average delivery time, because a 3–6 minute delay on second orders is expected by design.

7. Stop rules (checked daily by the on-call analyst)

  • City level: the >45-minute late share in "on" windows exceeds 10% for two consecutive days. Batching goes off in that city pending review.
  • Network level:
  • Any courier safety incident linked to batching, or a formal complaint from a couriers' association: pause everywhere.
  • Earnings per hour in "on" windows below €12.50 for three consecutive days: pause everywhere.
  • Porto only: any clear drop in hourly earnings leads to an immediate pause and notification to Courier Relations.

8. What each signer is agreeing to

  • COO: the dates in §3, the rollout rule in §5, and making the call if the result falls between 4% and 6%.
  • CFO: cost per order as the primary metric, with the ≥5% rule. Also acknowledges that post-rollout savings depend on how many courier hours we buy (§6).
  • Head of Operations: the 1.0pp late-share margin and the stop rules.
  • Courier Relations Lead:
  • the €0.40 margin;
  • the separate rule for Porto;
  • sending the Porto notice today.

9. Open items

1. Legal, today: Porto notice wording, and whether one notice covers both the test and the rollout. 2. Analytics, by 7 Oct: - power calculation that accounts for correlated windows; - randomisation schedule; - dashboard. 3. Engineering, by 7 Oct: per-city switch with switch times logged, tested in one city after hours. 4. Courier Relations and Legal, by 2 Nov: Porto earnings protection option. 5. Signers, by 8 Oct: confirm the guardrail margins in §5.

Check by check

Got wrong · 1
  • An unambiguous primary metricIt does not include a planned trust check such as a sample ratio check to verify that the arms are balanced and the experiment ran as intended.
Mixed · 1
  • Avoids unsupported claimsIt presents as fact that the test cannot prove the effect is above 5% if it is 6% and that Porto's 90 windows are too few to prove no reduction, neither of which is supported by the supplied evidence.The two graders disagreed on this one.
Got right · 10
  • Uses the supplied evidence correctlyAll claims about the current situation are taken directly from the brief or supplied context.
  • Addresses the actual decisionThe spec includes a clear decision rule (roll out if primary metric passes and guardrails hold, COO decides if estimate between 4% and 6%) that answers the question of whether to roll out.
  • Respects explicit constraintsThe output is under 1,200 words, respects the Porto 14-day notice requirement, and addresses all named signers.
  • Identifies material uncertaintyIt names the uncertainty around detecting a 5% vs 6% effect, the tension between cost savings and courier earnings, and Porto's small sample, and says how they would be resolved.
  • Produces the required deliverableThe output is a complete experiment spec with timeline, metrics, decision rule, and sign-off sections, under the word limit, and usable by the signers.
  • Avoids contamination between the armsIt explains the shared-courier contamination problem and uses a switchback design with a 30-minute washout to keep the arms separate.
  • A realistic plan that beats the freezeIt correctly converts 740 windows to about 9 days, explains why correlation and weekly cycles require about 3 weeks, and gives dates that fit before the 6 November freeze with Porto notice respected.
  • Decision rule written before the testIt states a rollout rule (pass primary and guardrails, city-level no clear guardrail failure) and says the COO decides if the estimate is between 4% and 6%, covering the inconclusive case.
  • Sized from the real trafficSample size is derived from the analyst's 740 windows, increased for correlation, and runs full weeks (21 days).
  • Guardrails with thresholdsIt names late delivery share (>45 min) and courier earnings per hour as guardrails with thresholds (1.0pp increase, €0.40 drop, and any drop in Porto).

Grades and run details

Decision model 79 · LLM judge 11 of 13 checks
Decision model checks
  • partialUses the supplied evidence correctly11%
  • passAddresses the actual decision42%
  • partialRespects explicit constraints23%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims20%
  • passProduces the required deliverable60%
  • passAvoids contamination between the arms100%
  • passA realistic plan that beats the freeze100%
  • partialAn unambiguous primary metric29%
  • partialDecision rule written before the test27%
  • partialSized from the real traffic47%
  • passGuardrails with thresholds99%
Run
Run
#1
Time to output
63 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