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 14 graded outputs by 7 models. 36% were usable with at most a quick edit.

Reliably right

  1. Addresses the actual decision91% 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
  2. Tests one change at a time86% pass
    It explicitly keeps the free-delivery banner out of the variant and explains that adding it would make the result uninterpretable.
    GPT-6 Luna · API · Showing the delivery fee up front
  3. Identifies material uncertainty84% 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. Respects explicit constraints55% pass
    The output is approximately 780 words, exceeding the 'under 700 words' limit.
    Opus 5.5 · Claude · Showing the delivery fee up front
  2. Sized from the real traffic57% 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
  3. Guardrails with thresholds59% pass
    Guardrail metrics are listed but no specific thresholds (e.g., maximum acceptable drop in AOV) are given to block rollout.
    GPT-6 Luna · 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.

What the model was given7 items: About Brisk, What batching should do, What each exec wants to see, Courier agreement in Porto, Timeline, The intern's draft plan, Analyst's note
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 doesThe answer key the graders mark against

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 at 40)
  • 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

Staff-level · v1.1 · synthetic · marketplace, logistics, switchback, network effects, staff-level

The outputs

7 setups answered this brief. Pick up to three to read side by side.

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.

Grades and run details

Decision model 88 · LLM judge 11 of 13 checks
Decision model checks
  • partialUses the supplied evidence correctly8%
  • passAddresses the actual decision43%
  • partialRespects explicit constraints18%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims22%
  • passProduces the required deliverable56%
  • passAvoids contamination between the arms100%
  • passA realistic plan that beats the freeze100%
  • passAn unambiguous primary metric33%
  • partialDecision rule written before the test27%
  • passSized from the real traffic41%
  • passGuardrails with thresholds99%
Run
Run
#1
Time to output
63 s
Submitted
30 Sept 2026

Needs more than a quick edit5 of 12 checks passed

The timeline and sizing need rework to account for clustered windows and adaptation, a trust check is missing, and the decision rule does not cover inconclusive outcomes. A PM would have to redo the power analysis and add missing elements.

Output

Experiment Spec: Order Batching Rollout Evaluation

Author: Staff Product Manager, Core Logistics Target Decision Date: 4 November 2026 (Rollout: 5 November; Peak Code Freeze: 6 November) Stakeholders / Sign-offs: COO, CFO, Head of Operations, Courier Relations Lead Informed: Head of Growth

---

1. Executive Summary & Decision Framework

We are evaluating courier order batching (one courier delivering two proximate orders) across our 14 European markets (~1.9M monthly orders). Our goal is to determine whether to ship batching globally prior to the 6 November code freeze.

Go / No-Go Decision Criteria (Sign-off Contract)

Batching will roll out to all 14 cities on 5 November only if all four conditions are met: 1. Cost Efficiency (CFO & COO): Courier cost per order decreases by $\ge$ 5.0% (baseline: €7.40; target: $\le$ €7.03) at $p < 0.05$. 2. Customer Experience Guardrail (COO & Head of Ops): Share of orders delivered in >45 minutes increases by no more than 0.8 percentage points (baseline: 7.5%; ceiling: $\le$ 8.3%) with no statistically significant drop in order ratings. 3. Courier Welfare & Legal Compliance (Courier Relations & Porto Agreement): Courier earnings per active hour do not decline in any market (baseline: €13.20; target: $\ge$ €13.20, non-inferiority margin of 0%). 4. Leading Retention Indicator (Head of Growth): 7-day reorder rate exhibits no statistically significant negative degradation ($\alpha = 0.05$).

---

2. Flaws in the Intern’s Draft & Required Fixes

Draft ProposalCore FlawResolution
:---:---:---
Order-level 50/50 splitSevere Market Interference (SUTVA violation): Batched treatment orders consume courier capacity unevenly, skewing dispatch speed, courier transit times, and ETAs for control orders sharing the same courier pool.City-Time Switchback Design: Randomize batching policy across all couriers/orders within discrete 2-hour time blocks per city.
30-day reorder rate metricTimeline Impossibility: Testing in October for a 6 November release leaves insufficient time to observe a 30-day reorder window.Leading Indicator Proxies: Measure 7-day reorder rate, delivery ratings (CSAT), and support ticket contact rates during the test; monitor 30-day reorder post-launch.
Porto immediate testingLegal Breach: Porto couriers' agreement requires 14 days' advance written notice and a guarantee of non-negative impact on average hourly earnings.Targeted Notice & Phased Inclusion: Issue formal notice on 1 October. Exclude Porto from Week 1 testing; enter Porto into switchback on 15 October with an earnings-floor guarantee.
Unstratified durationFails to control for intra-day/intra-week demand cyclicality (lunch vs. dinner, weekday vs. weekend).Stratified Block Randomization: Balance treatment and control windows across peak (12:00–14:00, 19:00–21:00) and off-peak windows.

---

3. Experiment Design & Methodology

Design: Stratified City-Time Switchback

  • Unit of Randomization: City $\times$ 2-hour window (11:00–13:00, 13:00–15:00, ..., 21:00–23:00 = 6 windows/day per city).
  • Intervention:
  • Treatment Window: Batching algorithm active (up to 2 orders per courier from nearby pickup points).
  • Control Window: Pure single-order dispatch (status quo).
  • Buffer/Washout: 15-minute dispatch transition buffer between blocks. Orders placed in the final 15 minutes of a window cannot be batched if the subsequent window is Control, eliminating spillover across windows.

Power & Sample Size

  • Required Sample: 740 two-hour windows to detect a 5% shift in cost per order ($\alpha = 0.05, \beta = 0.80$).
  • Capacity:
  • 13 cities $\times$ 6 windows/day = 78 windows/day.
  • Porto joins on Day 8 (15 October): 14 cities $\times$ 6 windows/day = 84 windows/day.
  • Duration: 14 days (8 October – 21 October).
  • 7 days $\times$ 78 windows + 7 days $\times$ 84 windows = 1,134 windows.
  • This provides >150% of required sample, allowing high statistical power to detect sub-1% changes in courier hourly earnings and Ops tail-delays.

---

4. Stakeholder Metrics & Success Criteria

``` +---------------------------------------------------------------------------------------+ | PRIMARY METRIC | | Courier Cost per Order (CFO/COO) | Target: >= 5% reduction (<= €7.03 vs. €7.40) | +---------------------------------------------------------------------------------------+ | +---------------------------------+---------------------------------+ | | | +------------------+ +-------------------+ +--------------------+ | OPS GUARDRAIL | | COURIER GUARDRAIL | | GROWTH GUARDRAILS | | (Head of Ops) | | (Courier Lead) | | (Head of Growth) | | >45m Late Share | | Courier Earnings | | • 7-day Reorder | | Baseline: 7.5% | | per Active Hour | | • Order Rating | | Ceiling: <= 8.3% | | Baseline: €13.20 | | • Missing/Cold Food| | Second-order ETA | | Floor: >= €13.20 | | Refund Rate | | impact: <= +5m | | (Non-inferior) | | | +------------------+ +-------------------+ +--------------------+ ```

Metric Definitions & Instruments

1. Courier Cost per Order (CFO): Total courier payout (base + distance fees + batch bonuses) divided by completed orders within the window. 2. Late Delivery Share >45m (Head of Ops): P90 delivery time and proportion of orders delivered >45 minutes from placement. Simulation projects a 3–6 min increase for the 2nd order; total late share must not breach 8.3%. 3. Courier Earnings per Active Hour (Courier Relations): Gross earnings divided by active hours (online and on-trip/assigned). Risk: Because couriers are paid per order + distance, batching two orders saves distance, potentially reducing payout unless batch base rates or drop-off multipliers offset it. 4. Customer Experience & Retention Proxy (Growth): * 7-day reorder rate for cohorts exposed to $\ge 2$ batched deliveries. * Delivery-related customer support contacts per 1,000 orders.

---

5. Porto Compliance & Courier Safeguards

Our Porto agreement mandates a 14-day notice period for dispatch adjustments and guarantees that courier average hourly earnings will not fall.

  • Timeline Action: Formal written notice delivered to the Porto Couriers’ Association on Wednesday, 1 October 2026.
  • Phased Rollout: Porto remains 100% Control through 14 October. On 15 October, Porto enters the switchback rotation.
  • Earnings Floor Mechanism: If average courier earnings per active hour in treatment windows drop below €13.20 in Porto (or any city), an automatic “Batch Adjustment Top-Up” will credit couriers the difference at end-of-day. This eliminates legal exposure while allowing us to evaluate the raw algorithm’s unit economics.

---

6. Execution Timeline

DateMilestoneOwner
:---:---:---
30 SepSpec review and executive alignment.Staff PM
1 OctSend 14-day formal notice to Porto couriers' association.Courier Relations
1–7 OctEngineering builds city-level switchback toggle + 15m buffer logic.Engineering
8 OctExperiment Launch: 13 cities enter switchback (Porto held in control).Ops / Analytics
15 OctPorto enters switchback upon notice expiry.Ops
21 Oct (23:00)Experiment Concludes (1,134 windows logged).Analytics
22–27 OctCollect 7-day reorder data for final test cohorts.Data Science
28 Oct–2 NovDeep-dive analysis: cost, earnings, ETA tail, customer sentiment.Analytics / PM
3 NovFinal Sign-off Meeting with COO, CFO, Ops, Courier Lead.Staff PM
5 NovFull rollout across all 14 cities (1 day).Engineering
6 NovPeak-Season Code Freeze takes effect.Platform Eng

---

7. Circuit Breakers & Rollback Criteria

The experiment toggle will automatically shut down batching within 15 minutes if any of the following triggers trip during live operations: * Severe Operations Failure: City-level late delivery rate (>45 min) exceeds 12.0% across two consecutive peak windows. * Courier Backlash/Disruption: Courier unassigned order rate spikes by $>25\%$ or courier offline churn spikes by $>15\%$ compared to historical control. * Severe Food Quality Impact: Customer support contact rate for "cold food" or "late arrival" doubles ($>2.0\times$).

---

8. Stakeholder Sign-Off

By signing below, stakeholders agree that if the primary and guardrail criteria in Section 1 are satisfied by 3 November, order batching will be deployed to all 14 cities on 5 November 2026.

  • Chief Operating Officer: _________
  • Chief Financial Officer: _________
  • Head of Operations: _________
  • Courier Relations Lead: _________

Grades and run details

Decision model 54 · LLM judge 7 of 13 checks
Decision model checks
  • failUses the supplied evidence correctly35%
  • passAddresses the actual decision38%
  • failRespects explicit constraints37%
  • partialIdentifies material uncertainty34%
  • partialAvoids unsupported claims44%
  • partialProduces the required deliverable30%
  • passAvoids contamination between the arms100%
  • partialA realistic plan that beats the freeze87%
  • partialAn unambiguous primary metric40%
  • partialDecision rule written before the test98%
  • partialSized from the real traffic58%
  • passGuardrails with thresholds83%
Run
Run
#1
API response time
37 s
Submitted
1 Oct 2026

Needs more than a quick edit9 of 12 checks passed

The spec lacks a carry-over buffer at switch boundaries and a trust check for the primary metric, and it defers sample-size calculation to a simulation instead of sizing from the given traffic. A PM would need to add these before use.

Output

Brisk order-batching experiment spec

Decision required: Whether to enable batching in all 14 cities on 5 November 2026, ahead of the 6 November code freeze. Batching pairs two eligible orders from nearby restaurants for one courier.

Decision principles and sign-off

Launch only if the experiment shows a worthwhile courier-cost reduction, customer lateness stays within an agreed limit, and courier earnings protections are met. An inconclusive result is not a pass.

Before launch, the COO and Head of Operations must approve the customer-delay limit below; the courier relations lead and Legal must confirm the Porto protocol in writing. If those approvals or the schedule cannot be met, do not claim evidence for an all-city rollout before the freeze.

Design

Randomise city × two-hour operating windows, not individual orders. Orders interact through shared courier supply, and individual randomisation would contaminate the comparison. In each treatment window, the batching policy is enabled; in each control window, batching is disabled. Randomise and balance windows within each city, weekday and daypart, aiming for an even treatment/control split. Keep the assignment schedule fixed before the test begins.

An order is analysed according to its city-window at placement (intention to treat). Batch only orders placed in the same treatment window; do not pair across windows or with control orders. This limits cross-arm contamination at switchovers. Record actual batching eligibility and use, but do not reclassify orders based on whether they were batched.

Timeline and Porto condition

  • 30 September: Send Porto’s couriers’ association written notice. The notice requirement applies to the experiment as well as any rollout. Start Porto treatment no earlier than 15 October, allowing 14 full days.
  • By 7 October: Complete the per-city switch, monitoring, and rollback controls; test the assignment and cost calculations.
  • 15 October–4 November: Run the experiment in all cities, subject to Porto approval.
  • 5 November: Review results and, only if all launch gates pass, enable batching in all 14 cities in one day. Otherwise keep it off. Do not shorten the experiment to meet the freeze.

Porto must not enter treatment until Legal and the courier relations lead confirm the notice and trial protocol comply with the agreement. Agree a written earnings-protection mechanism with the association before starting; any top-ups count in courier cost. If this cannot be agreed, exclude Porto from treatment and do not describe the result as support for a 14-city rollout.

Measures and launch gates

Estimate treatment effects against control, with confidence intervals and pre-specified analysis. Baselines: courier cost €7.40/order, orders over 45 minutes 7.5%, courier earnings €13.20/active hour.

OwnerMeasure and proposed launch gate
CFO / COO — primaryAll-in courier cost per completed order, including pay, distance payments, incentives and earnings-protection top-ups. Launch only if estimated savings are at least 5% and the 95% confidence interval supports a real saving (excludes zero savings). Report the interval; the point estimate alone is not proof.
Head of Operations — customer guardrailShare delivered more than 45 minutes after ordering. Propose a maximum acceptable increase of 0.5 percentage points versus control; launch only if the 95% confidence interval rules out a larger increase. COO and Operations must approve this “noticeably longer” limit before the test. Also report delivery-time distributions, cancellations and complaints.
Courier relations — earnings guardrailEarnings per active hour, including all payments and top-ups, with active time defined consistently across arms. No rollout if the overall result shows a material decline against a margin agreed in advance. In Porto, meet the association-approved earnings protection; do not treat a non-significant decline as proof of compliance. Report by city as well as overall.
Growth — secondary30-day reorder rate for orders exposed to each arm, analysed by assigned window. This outcome will mature after the November decision (roughly mid-December for the final cohort); report it then. It is not a pre-freeze launch gate.

The proposed 0.5pp lateness margin and the earnings non-inferiority margin are policy choices, not facts in the brief; the accountable leads must sign them off before randomisation. Do not change margins after seeing results.

Analysis and power

The schedule contains 1,764 city-windows (14 cities × 6 windows/day × 21 days), before any exclusions. The analyst’s estimate of 740 assumes independent windows; successive windows may be correlated, and couriers and demand carry across time. Before the test, use historical city-window data and the planned randomisation to simulate power, accounting for serial correlation and city/daypart differences. Use blocked randomisation-based inference or an appropriate model with uncertainty at the randomised-window level. Do not claim the test is powered merely because 1,764 exceeds 740. If adjusted power is inadequate, or results are too imprecise to meet a gate by 5 November, the decision is inconclusive: no rollout before freeze.

Operations and rollback

Monitor treatment uptake, cost, lateness and earnings daily, but do not stop early for a favourable result. Pause a city for a safety, service or earnings breach under pre-agreed operational thresholds; document the incident and retain it in intention-to-treat analysis. Keep the city switch available for immediate rollback. Sign-off: COO, CFO, Head of Operations, courier relations lead; Growth acknowledges the delayed reorder readout.

Grades and run details

Decision model 83 · LLM judge 9 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly13%
  • passAddresses the actual decision55%
  • partialRespects explicit constraints19%
  • passIdentifies material uncertainty93%
  • passAvoids unsupported claims60%
  • partialProduces the required deliverable34%
  • passAvoids contamination between the arms87%
  • passA realistic plan that beats the freeze93%
  • partialAn unambiguous primary metric36%
  • passDecision rule written before the test92%
  • partialSized from the real traffic18%
  • passGuardrails with thresholds49%
Run
Run
#1
API response time
44 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 9

Uses the supplied evidence correctlyRightMixedRight
Opus 5.5 · Claude

All claims about the current situation are taken directly from the brief or supplied context.

Gemini 3.8 Flash · API

All factual claims about the current situation are directly from the brief or supplied context.

GPT-6 Luna · API

All claims about the current situation are taken directly from the brief or supplied context, with no invented facts.

Respects explicit constraintsRightMixedRight
Opus 5.5 · Claude

The output is under 1,200 words, respects the Porto 14-day notice requirement, and addresses all named signers.

Gemini 3.8 Flash · API

The output is an experiment spec under 1,200 words, addresses each stakeholder's metric, respects the Porto notice period, and fits the timeline before the freeze.

GPT-6 Luna · API

The spec respects the word limit, addresses each exec's needs, handles the Porto notice and earnings requirement, and fits the timeline before the freeze.

Identifies material uncertaintyRightWrongRight
Opus 5.5 · Claude

It 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.

Gemini 3.8 Flash · API

The spec does not name specific unknowns that could change the decision (e.g., whether the 7-day reorder rate proxies 30-day retention, or how courier adaptation might evolve beyond the test window) or say how they would be resolved.

GPT-6 Luna · API

It identifies correlation across windows, the need for power simulation, and that margins are policy choices, and says how each would be resolved.

Avoids unsupported claimsMixedRightRight
Opus 5.5 · Claude

It 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.

Gemini 3.8 Flash · API

Interpretations like the SUTVA violation are presented as design reasoning, not as established facts about the current situation, and no other unsupported factual claims are made.

GPT-6 Luna · API

Causal claims like contamination are presented as design rationale, not as established facts, and hypotheses are labelled with 'may'.

Avoids contamination between the armsRightRightMixed
Opus 5.5 · Claude

It explains the shared-courier contamination problem and uses a switchback design with a 30-minute washout to keep the arms separate.

Gemini 3.8 Flash · API

The spec explicitly identifies the shared-courier contamination problem, adopts a city-time switchback design, and includes a 15-minute buffer to prevent carry-over at switches.

GPT-6 Luna · API

It explains the shared-courier problem and uses a switchback design, but does not handle carry-over at switches with a buffer or an assignment rule that excludes orders near the switch boundary.

A realistic plan that beats the freezeRightWrongRight
Opus 5.5 · Claude

It 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.

Gemini 3.8 Flash · API

The plan takes the 740 independent-window requirement at face value without adjusting for within-city clustering or weekly cycles, and proposes only 14 days, which is too short for courier adaptation and robust weekly coverage.

GPT-6 Luna · API

It gives Porto notice on 30 Sep, starts the experiment on 15 Oct, runs for three weeks, and delivers a decision on 5 Nov before the 6 Nov freeze, with realistic acknowledgment of correlation.

An unambiguous primary metricMixedWrongWrong
Opus 5.5 · Claude

It 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.

Gemini 3.8 Flash · API

The spec names courier cost per order as the primary metric but does not include a planned trust check (e.g., sample ratio check or balance verification) to confirm the experiment's integrity.

GPT-6 Luna · API

It names courier cost per order as the primary metric but does not include a planned trust check such as a sample ratio check or verification that arms received the scheduled share of windows.

Decision rule written before the testRightWrongRight
Opus 5.5 · Claude

It 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.

Gemini 3.8 Flash · API

The decision rule only covers the case where all conditions are met; it does not state what action to take if the result is inconclusive (e.g., p-value borderline, guardrail ambiguous).

GPT-6 Luna · API

Every outcome (gates pass, guardrail breached, inconclusive) maps to a stated action with thresholds, all written before the test.

Sized from the real trafficRightWrongWrong
Opus 5.5 · Claude

Sample size is derived from the analyst's 740 windows, increased for correlation, and runs full weeks (21 days).

Gemini 3.8 Flash · API

The sample size calculation uses the analyst's 740 windows without adjusting for the non-independence of windows within the same city, and the 14-day duration does not account for the need for multiple full weekly cycles.

GPT-6 Luna · API

It does not derive sample size from the 1.9 million monthly orders and the 5% effect; it only references the analyst's 740 windows and defers to a simulation without calculating required duration from real traffic.

All got right 3

Addresses the actual decisionRightRightRight
Opus 5.5 · Claude

The 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.

Gemini 3.8 Flash · API

The spec commits to a conditional rollout (ship if all four conditions are met, otherwise do not) and states the exact conditions that would change the call.

GPT-6 Luna · API

The output commits to a clear decision rule (launch if gates pass, otherwise don't, inconclusive means no rollout) and states what would change it.

Produces the required deliverableRightRightRight
Opus 5.5 · Claude

The output is a complete experiment spec with timeline, metrics, decision rule, and sign-off sections, under the word limit, and usable by the signers.

Gemini 3.8 Flash · API

The output is a complete experiment spec with decision framework, design, metrics, timeline, and sign-off, usable by the named stakeholders with light edits.

GPT-6 Luna · API

The output is a complete experiment spec under 1,200 words, addressed to the execs, and usable as a decision document.

Guardrails with thresholdsRightRightRight
Opus 5.5 · Claude

It 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).

Gemini 3.8 Flash · API

Guardrail metrics (late delivery share, courier earnings per hour, 7-day reorder rate, second-order ETA impact) are named with explicit thresholds that would block rollout.

GPT-6 Luna · API

It names late deliveries (0.5pp max increase) and courier earnings (no material decline) as guardrails with thresholds that would block rollout.

Results

Every setup we’ve tested on this task, across all cases and repeats, graded on the current checklist. Calibrated: the graders match our PM on 90% of checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6.1 SolwithAPI87.588.52None
2Sonnet 5.5withAPI81.388.52None
3Opus 5.5withClaude85.476.92None
4GPT-6 LunawithAPI85.473.12None
5GPT-6 AstrawithChatGPT87.565.42None
6Gemini 3.8 FlashwithAPI60.438.52None
7Gemini 3.5 Flash-LitewithGemini37.515.422 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