Tasks / Product Craft

Make the launch call

Can the model make a clear go / no-go call from mixed launch evidence, checked against the criteria agreed up front?

Task type v1.2 · 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. 69% were usable with at most a quick edit.

Reliably right

  1. Finds the Scotland breach100% pass
    Finds Scotland's refund breach (+66% overall, +143% since 15 Oct), ties it to the supplier change, notes acceptance dip, and holds Scotland.
    Sonnet 5.5 · API · Go/no-go for AI substitutions, from the rollout data
  2. Treats the overwrites as data loss100% pass
    Treats the overwritten notes on Android 11 as a breach of the no-data-loss criterion and makes fixing the storage-API issue a condition for that platform.
    Opus 5.5 · Claude · Offline, on older phones
  3. Addresses the actual decision98% pass
    Commits to a clear no-go for 2 Nov, conditional go from 4 Nov for four regions, and no-go for Scotland, with conditions that would change it.
    Sonnet 5.5 · API · Go/no-go for AI substitutions, from the rollout data

Where it slips

  1. Limits the downside of being wrong63% pass
    It does not name a specific post-launch signal, a threshold that would trigger pause or rollback, or flag any irreversible actions.
    GPT-6 Luna · API · Go/no-go for AI substitutions, from the rollout data
  2. Judges the launch against a baseline set now66% pass
    Does not name a specific review point or when the team will review results to decide next steps; only gives a pause trigger without a scheduled evaluation.
    GPT-6.1 Sol · API · Offline, on older phones
  3. Checks every criterion for every platform69% pass
    Android 13's error-free sync is 98.9%, below the 99% threshold, yet the table marks it as 'GO WITH CONDITIONS' instead of 'NO-GO' for that criterion, misapplying the hard launch criteria.
    Gemini 3.5 Flash-Lite · Gemini · Offline, on older phones

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.

  1. Limits the downside of being wrong

    Does the call say how exposure is limited if it's wrong: what is watched after launch, the threshold that would pause or roll back, and which parts can't be undone?

    Passes when Names the post-launch signal, a threshold or trigger, and the action, and flags anything irreversible (customer communications, pricing, migrations).

  2. Judges the launch against a baseline set now

    Does the call name the metric and the current baseline the launch will be judged against, and when the team will review the result to decide what's next?

    Passes when Names the success metric, its baseline from the material, and a review point with what each result would lead to.

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 · Gets the base of every number right · and 5 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 Staff PM for fulfilment at Basketful. On 30 October the launch meeting decides whether AI auto-substitutions (the model picks a replacement when an item is out of stock, instead of the picker) roll out to every region on Monday 2 November, ahead of the Christmas delivery-slot booking window that opens on 9 November. The rollout workbook is attached as two files: daily_metrics.csv (28 days of the regional test, by region and arm) and incidents.csv. Work from the data, not the summaries people have given you. Write your recommendation for the meeting: go, no-go or go with conditions, region by region, and why. Include a table that checks each launch criterion for each region with the figures. Keep the prose under 900 words.

What the model was given6 items: The test, Launch criteria (agreed in the PRD), What people have said, Engineering, daily_metrics.csv, incidents.csv
The test1–28 October, five regions. In each region orders were split between control (the picker chooses substitutes, as today) and treatment (AI auto-substitutions). The split was not 50/50 everywhere: treatment got 30% of orders in London, 50% in the South East and North West, and 70% in the Midlands and Scotland.
Launch criteria (agreed in the PRD)1. Substitution acceptance (subs_accepted ÷ subs_offered) of at least 80% in treatment, in every region. 2. Refund requests per 1,000 orders in treatment no more than 10% above control, in every region. 3. Zero dietary or allergen mismatches (a substitute that breaks a gluten-free, vegan, vegetarian, nut-free, halal or kosher attribute). 4. Average basket value not lower in treatment than control.
What people have saidHead of Commercial: “Acceptance is up 15 points and refunds are within the guardrail overall. Every day we wait costs us Christmas.” Head of Analytics: “Treatment baskets are £4 smaller across the test. That worries me.” Ops director (Scotland): “The supplier change caused some noise, but it's settling down.”
EngineeringThe dietary-attribute hard filter (never substitute across those attributes) is merged and scheduled to deploy on 4 November after regression tests. Scotland's catalogue re-map for the new dairy supplier is in progress with no date. Rollout can be switched on region by region.
daily_metrics.csv283 rows · Download
date,region,arm,orders,items_ordered,items_out_of_stock,subs_offered,subs_accepted,refund_requests,refund_value_gbp,complaints,avg_basket_gbp
2026-10-01,London,control,1757,52273,2351,1953,1387,32,160.71,6,72.89
2026-10-01,London,treatment,692,20455,871,820,708,12,62.05,2,72.42
2026-10-01,South East,control,853,22846,996,833,593,16,74.49,3,65.62
2026-10-01,South East,treatment,928,25223,1166,1106,952,15,75.93,3,66.59
2026-10-01,Midlands,control,467,10468,465,383,268,8,34.57,1,54.92
2026-10-01,Midlands,treatment,1113,25136,1166,1087,947,21,106.35,3,55.33
…
incidents.csv13 rows · Download
date,region,arm,type,severity,summary,status
2026-10-02,London,treatment,wrong_size,low,"4-pint milk substituted with 1-pint, customer accepted at door",closed
2026-10-04,Midlands,treatment,dietary_mismatch,high,"Gluten-free sliced loaf substituted with standard white loaf; customer coeliac, noticed at home",closed: refund and apology
2026-10-06,South East,control,wrong_item,low,"Picker substituted oat milk with soya milk; customer rejected at door",closed
2026-10-07,North West,treatment,data_export,low,"Daily export job re-ran after a timeout on 8, 9 and 10 Oct; analytics team flagged possible duplicate rows",open
2026-10-09,London,treatment,price,medium,"Substitute priced higher than original; customer charged the difference against policy",closed: policy fix deployed 11 Oct
2026-10-12,Scotland,control,late_delivery,low,"Van breakdown, 14 orders late",closed
…
What a strong answer doesThe answer key the graders mark against

No-go for a national launch on 2 November, because criterion 3 fails everywhere until the dietary hard filter ships: incidents.csv has three treatment dietary mismatches (4 Oct Midlands, 19 Oct South East, 23 Oct London, the last involving nuts and a child with an allergy). Go with conditions, region by region, once the filter is deployed and verified (planned 4 November, still before the 9 November booking window): London, South East, Midlands and North West pass the other criteria (acceptance about 86% against 71% control; refunds 2–5% below control). Hold Scotland: over the month its treatment refunds are about 66% above control, and from the 15 October supplier change about 143% above (43 against 18 per 1,000 orders), with acceptance falling to about 79%, below the 80% bar; it waits for the catalogue re-map and a clean re-test. The pooled refund rate (about 8% above control) hides this, and the criterion is per region. The pooled basket gap (about £65 against £61) is an artefact of the uneven split, since treatment baskets are equal or higher in every region (Simpson's paradox), so criterion 4 passes. Notices that North West treatment rows for 8–10 October are duplicated (an extra 2,194 orders), removes them, and says it doesn't change the result. States what would change the call: the filter slipping past 9 November, or Scotland's refunds not recovering.

Critical failures (cap the score at 40)
  • Recommends any rollout before the dietary hard filter is deployed
  • Recommends rolling out to Scotland while its refund guardrail is breached
Task

Staff-level · v1.12

The outputs

8 setups answered this brief.

Usable with a quick edit12 of 14 checks passed

The output gives a complete, evidence‑based recommendation with a criteria table, region‑specific calls, and clear conditions. A PM could use it with minor edits without re‑checking the data or reasoning.

Output

Launch recommendation: AI auto-substitutions, 2 November

Recommendation: do not launch to all regions on 2 November. Switch on North West once the dietary hard filter is confirmed live (target 5 November). Hold London, South East and Midlands, and re-decide on 6 November. Keep Scotland on picker substitutes.

Criterion 3 is a hard PRD gate, and the test recorded three high-severity dietary breaks in treatment. The hard filter does not deploy until 4 November, so a 2 November launch would go live without the control that would have prevented them.

Data notes

  • Duplicates removed. North West treatment rows for 8, 9 and 10 October are duplicated (the export re-ran). I kept one copy of each. Including them barely moves the result (acceptance 86.2%, refunds 17.5 per 1,000), but the incident is still open and the export should be fixed.
  • Split is uneven, so I used rates. Acceptance, refunds per 1,000 orders and basket are computed per region and arm, not from totals.
  • Basket: the £4 gap isn't in the file. Daily average basket is higher in treatment in every region (+£0.24 to +£0.46 per order on simple averages). A £4 gap would need a different definition, such as totals, which differ because arm volumes differ. Analytics should share its query before anyone quotes the figure. I haven't checked order-weighted averages, but I don't expect them to reverse the sign.
  • Pooled figures hide the problem. Pooled, treatment refunds are 19.5 per 1,000 against 18.1 for control (+8%), which passes. That is only because four regions are under control and Scotland is far over it.

Criteria check (treatment vs control)

Region1. Acceptance (≥80%)2. Refunds per 1,000 (≤10% above control)3. Dietary breaks (zero)4. Basket (not lower)Call
London86.7% vs 71.4% ✔17.4 vs 18.0 (−3%) ✔1 high: nut-free → hazelnut, 23 Oct, open ✘£72.72 vs £72.48 ✔No-go
South East86.4% vs 71.0% ✔17.5 vs 17.9 (−2%) ✔1 high: vegan → dairy cheddar, 19 Oct ✘£66.66 vs £66.20 ✔No-go
Midlands86.3% vs 71.1% ✔17.5 vs 17.9 (−2%) ✔1 high: gluten-free → standard loaf, 4 Oct ✘£55.66 vs £55.35 ✔No-go
North West86.3% vs 71.2% ✔17.6 vs 18.5 (−5%) ✔0 logged ✔£57.52 vs £57.28 ✔Go with conditions
Scotland82.8% overall ✔; 79.1% since 15 Oct ✘30.2 vs 18.2 (+66%) ✘0 logged ✔£52.98 vs £52.62 ✔No-go

Region by region

North West: go with conditions. It is the only region that passes all four criteria on clean data. Its zero is partly luck, though. The three dietary breaks show the model crosses attribute boundaries with no hard filter, so the filter is a precondition here as well. Conditions: 1. The hard filter is deployed and regression-tested (4 November), and North West's catalogue is checked against it. 2. The duplicate-row export issue is closed and the North West figures are re-run on the fixed export. 3. Switch on around 5 November with the kill switch ready. Review refunds and dietary complaints at one week.

London, South East, Midlands: no-go for 2 November. Acceptance and refunds are fine, and basket is up. But each region has a high-severity dietary break in treatment, and the nut-free case (London, 23 October) is still open, with a child with a nut allergy involved. Before we re-decide on 6 November, we need: 1. The hard filter live and regression-tested. 2. A retrospective audit of every treatment substitution in these regions against product attributes. The logged incidents are only the ones customers reported. 3. Root cause for the London nut-free break and confirmation the price-policy fix (11 October) holds. London's basket gain may partly reflect higher-priced substitutes, which is the issue that prompted that fix. 4. A short ramp (for example 10% of treatment orders) from 4–6 November, with a zero-mismatch check, before any wider switch-on. If the audit finds further breaks, these regions stay off.

Scotland: no-go. The ops director's "settling down" is not what the data shows. Before the supplier change on 15 October, treatment acceptance was 86.3% and refunds were 17.2 per 1,000, the same as control (17.3). Since 15 October, acceptance has been flat at about 79% (79.0% in week two, 79.2% in week three) and refunds at 43 per 1,000 against 19 for control. Refund value since 15 October is about £2.8k. Control's refund rate barely moved, so the cause is the treatment engine running on the new catalogue: the open wrong-size dairy incident matches the mapping error described in the log. The re-map has no date. - Keep Scotland at 0% treatment. - Re-enter only when the re-map is validated, and treatment acceptance is at least 80% and refunds within 10% of control over two weeks at a small share.

Asks for the meeting

  1. Agree that no region goes live on 2 November, and that criterion 3 is not waived.
  2. Approve North West for go-on-filter (target 5 November), subject to the conditions above.
  3. Set the 6 November gate for London, South East and Midlands, with the audit and ramp as entry conditions.
  4. Keep Scotland on control. Commercial should not plan Scotland into Christmas slots until the re-map is validated.
  5. Analytics to reconcile the £4 basket claim before it goes into any further material.

The Christmas booking window opens on 9 November. The North West switch-on and the 6 November gate both land before then, so the main cost of this recommendation is that London, South East and Midlands may not have a validated launch for the first booking week.

Grades and run details

Decision model 79 · LLM judge 100
Decision model checks
  • failUses the supplied evidence correctly25%
  • passAddresses the actual decision72%
  • passRespects explicit constraints23%
  • passIdentifies material uncertainty85%
  • partialAvoids unsupported claims38%
  • passProduces the required deliverable61%
  • passChecks every criterion, region by region34%
  • passFinds the Scotland breach100%
  • passExplains the basket gap70%
  • passCatches the duplicate rows100%
  • passMakes a clear call85%
  • partialLimits the downside of being wrong95%
  • failGets the base of every number right48%
  • passJudges the launch against a baseline set now92%
Run
Run
#1
API response time
1.9 min
Submitted
8 Oct 2026

Needs more than a quick edit7 of 14 checks passed

The output misses the duplicate rows, does not explain the basket gap as a mix artefact, and lacks a post-launch monitoring plan, so a PM would need to rework these areas before using it.

Output

Launch Recommendation: AI Auto-Substitutions

As the Staff PM for fulfilment, my recommendation for the 30 October launch meeting is a No-Go for nationwide rollout on 2 November.

While the Commercial and Analytics teams are looking at top-line aggregates, a region-by-region audit of the 28-day test data and incident logs reveals critical blockers. Specifically, Scotland fails acceptance and refund criteria due to a supplier mapping failure, Midlands and London recorded high-severity dietary/allergen mismatches, and South East also experienced a dietary mismatch.

We should hold all regions until the dietary hard filter is deployed on 4 November, and until Scotland’s catalogue re-map is fully completed and verified.

---

Regional Evaluation Table (1–28 October Test Data)

RegionSubs Acceptance ($\ge 80\%$)Treatment Refunds vs Control ($\le +10\%$)Dietary/Allergen Mismatches (Target: 0)Basket Value ImpactRecommendation
:---:---:---:---:---:---
London$86.4\%$ (Pass)Control: 1.77%<br>Treatment: 1.63% ($-7.9\%$)1 High Incident (Nut allergy violation on 23 Oct)Control: £72.33<br>Treatment: £72.71 (+0.5%)No-Go (Hold for 4 Nov filter)
South East$87.0\%$ (Pass)Control: 1.66%<br>Treatment: 1.69% ($+1.8\%$)1 High Incident (Vegan dairy violation on 19 Oct)Control: £66.21<br>Treatment: £66.72 (+0.8%)No-Go (Hold for 4 Nov filter)
Midlands$84.5\%$ (Pass)Control: 1.69%<br>Treatment: 1.68% ($-0.6\%$)1 High Incident (Gluten-free coeliac violation on 4 Oct)Control: £55.22<br>Treatment: £55.51 (+0.5%)No-Go (Hold for 4 Nov filter)
North West$88.4\%$ (Pass)Control: 1.73%<br>Treatment: 1.66% ($-4.0\%$)0 IncidentsControl: £57.34<br>Treatment: £57.65 (+0.5%)Go with Conditions (Deploy 4 Nov)
Scotland$77.0\%$ (Fail)Control: 1.79%<br>Treatment: $3.90\%$ ($+117.9\%$)0 Incidents (High dairy wrong-size volume)Control: £52.48<br>Treatment: £52.88 (+0.8%)No-Go (Hold for catalogue re-map)

Note: Refund request rates are calculated per 100 orders for precise comparison against the 10% relative ceiling.

---

Region-by-Region Breakdown

  • London, South East, Midlands: All three regions easily clear the 80% acceptance threshold and show neutral-to-positive average basket values (debunking the £4 basket drop concern). However, all three recorded high-severity dietary/allergen failures (gluten, vegan, and nut-allergy breaches). Because the safety hard filter is not deployed until 4 November, rolling out on 2 November exposes customers to unacceptable allergen risks during the peak Christmas slot-booking window.
  • North West: Our strongest performer. It meets all four PRD criteria cleanly3: 88.4% acceptance1, treatment refund rates slightly below control, zero dietary incidents, and a stable basket value. It is ready to roll out immediately following the hard filter deployment.
  • Scotland: A clear No-Go. Acceptance dropped to 77.0% (failing the 80% bar), and treatment refund requests spiked catastrophically by 117.9% over control (driven by the dairy supplier switch on 15 Oct where pack sizes were mismapped). Until the catalogue re-map is finished and stabilized, rolling out here will compound operational friction.

Action Plan

  1. Hold Rollout on 2 November.
  2. Deploy Dietary Hard Filter on 4 November as scheduled, and verify regression tests.
  3. Complete Scotland Catalogue Re-map and clear open wrong-size substitution incidents.
  4. Staged Launch: Roll out to North West, London, South East, and Midlands on Monday, 9 November (concurrent with the Christmas slot-booking window opening), pending a 7-day post-fix stability review.2 Scotland follows only after catalogue remediation.

What a PM had to fix

  1. 1Numbers wrongRedo the arithmeticStart again

    What we’d changeRecompute every figure from daily_metrics.csv, removing the duplicated North West rows. Almost every number in the table is wrong.

  2. 2Contradiction missedSurface the contradictionSubstantial rework

    What we’d changeResolve the contradiction: a 7-day review after the 4 November filter can't finish by 9 November, and North West is marked go in the table but held here. Answer the stakeholders' claims rather than dismissing them.

  3. 3Hypothesis stated as factReframe it as a hypothesisTargeted repair

    What we’d changeZero recorded mismatches in North West isn't evidence of safety. It runs the same unfiltered model as the regions that had them.

Grades and run details

Decision model 46 · LLM judge 60
Decision model checks
  • failUses the supplied evidence correctly50%
  • passAddresses the actual decision35%
  • passRespects explicit constraints11%
  • partialIdentifies material uncertainty64%
  • partialAvoids unsupported claims27%
  • passProduces the required deliverable27%
  • partialChecks every criterion, region by region66%
  • passFinds the Scotland breach96%
  • failExplains the basket gap52%
  • failCatches the duplicate rows99%
  • partialMakes a clear call82%
  • failLimits the downside of being wrong37%
  • failGets the base of every number right70%
  • partialJudges the launch against a baseline set now24%
Run
Run
#1
Time to output
46 s
Submitted
25 Sept 2026

Usable with a quick edit11 of 14 checks passed

The output is clear, complete, and actionable. The minor unsupported phrasing about 'luck' could be edited out without re-checking evidence or reasoning.

Output

AI auto-substitutions: launch recommendation

Oct 29, 2026 · @Martin Slaney · For the 30 October launch meeting

Recommendation

No-go for Monday 2 November in any region. The dietary hard filter isn't live until 4 November, and the test produced three allergen and dietary mismatches.

Go with conditions for London, South East, Midlands and North West from Thursday 5 November, once the filter is live and verified. That is still four days before the Christmas booking window opens on 9 November.

No-go for Scotland until the dairy catalogue re-map ships and a fresh week of test data passes the criteria.

Launch criteria by region

Figures are for 1–28 October from daily\_metrics.csv, with duplicate rows removed. T = treatment, C = control.

Region1. Acceptance (T), ≥80%2. Refunds per 1k orders, T vs C, ≤+10%3. Dietary mismatches (T), 04. Avg basket, T vs CVerdict
London86.7% ✅17.4 vs 18.0 (−3%) ✅1: nut-free to hazelnut, 23 Oct, still open ❌£72.70 vs £72.49 ✅Go 5 Nov, after filter
South East86.4% ✅17.5 vs 17.9 (−2%) ✅1: vegan to dairy, 19 Oct ❌£66.64 vs £66.20 ✅Go 5 Nov, after filter
Midlands86.4% ✅17.5 vs 17.9 (−2%) ✅1: gluten-free to standard, 4 Oct ❌£55.66 vs £55.35 ✅Go 5 Nov, after filter
North West86.3% ✅17.6 vs 18.5 (−5%) ✅0 ✅ (same model, so not evidence of safety)£57.51 vs £57.29 ✅Go 5 Nov, after filter
Scotland82.8% overall; 79.1% since 15 Oct ❌30.2 vs 18.2 (+66%); since 15 Oct 43.2 vs 17.8 (+143%) ❌0 ✅£52.98 vs £52.62 ✅No-go until re-map and re-test

Why not 2 November

Criterion 3 requires zero dietary or allergen mismatches. Treatment had three, all rated high severity: a gluten-free loaf swapped for a standard one (the customer is coeliac), vegan cheese swapped for dairy cheddar (the customer complained on social media), and a nut-free multipack swapped for one containing hazelnuts, sent to a child with a nut allergy. The nut incident is still open. Control had none.

The fix is the dietary hard filter, which is merged and deploys on 4 November after regression tests. Launching on 2 November would put every order in every region onto the unfiltered model for two days. North West's zero is luck, because it runs the same model.

Strictly, all four "go" regions fail criterion 3 as it's written. I'm asking the meeting to accept the filter as the remedy, on condition that we show it would have blocked all three incidents. If the meeting won't accept that, the honest answer is no-go everywhere.

Waiting three days costs nothing for Christmas, because bookings don't open until 9 November.

What the summaries get wrong

Head of Commercial: "refunds within the guardrail overall." Pooled, treatment refunds are 7.9% above control (19.5 vs 18.1 per 1,000 orders), which is inside 10%. But the criterion applies to every region, and the pooled figure hides Scotland at +66%. In the other four regions, treatment refunds are below control. The 15-point acceptance lift is real in those four regions (+15.0 to +15.4 points). In Scotland it's +11.7 overall and falling.

Head of Analytics: "treatment baskets are £4 smaller." Pooled, the gap is £61.09 vs £65.15, but it comes from how orders were split, not from the feature. Treatment got 70% of orders in Midlands and Scotland, which have the smallest baskets (£52–56), and only 30% in London, which has the largest (£72). Within every region, treatment baskets are £0.21–£0.44 larger, so criterion 4 passes everywhere.

Ops director, Scotland: "it's settling down." It isn't. Before the supplier switch on 15 October, Scotland's treatment arm matched the other regions (86.3% acceptance, 17.3 refunds per 1,000 vs 18.6 in control). Since then, treatment refunds have run at 25–33 a day with no downward trend: 28.1 a day on average from 15 to 21 October and 28.7 from 22 to 28 October. Refund value per order has tripled, from £0.09 to £0.30. Scotland's control arm is unaffected at 17.8 per 1,000. So the problem is specific to the model reading a mis-mapped catalogue: pickers can see the real pack sizes, and the model can't. The re-map has no date.

Data quality

The North West treatment rows for 8, 9 and 10 October each appear twice, because the export job re-ran (the open incident from 7 October). I removed the duplicates. They don't change any verdict: North West acceptance moves from 86.2% to 86.3%.

Conditions for the 5 November go

  1. The dietary filter deploys on 4 November and passes regression tests. We replay the three incident orders and confirm the filter blocks each of them.2
  2. The London nut-allergen incident is closed with a root cause.
  3. We switch on region by region and keep a 10% control holdout in each region through Christmas, so criteria 2 and 4 stay measurable.
  4. Kill switch: any single dietary or allergen mismatch turns the region off, as does a treatment refund rate more than 10% above its holdout over a rolling 3 days.1
  5. Scotland: ship the catalogue re-map, then run 7 days of test and re-check the criteria. If it can't be ready by 9 November, Scotland takes Christmas orders with picker substitutions.

What a PM had to fix

  1. 1Test or gate too weakTighten the testQuick edit

    What we’d changeSet the refund trigger outside day-to-day noise, with a bigger gap or a longer window, or it will switch regions off for no reason.

  2. 2Test or gate too weakTighten the testQuick edit

    What we’d changeThree replays is thin evidence the filter works. Test it across every protected attribute, including missing or conflicting ones.

Claims the judge couldn’t find in the brief

  • North West's zero dietary mismatches is luck, because it runs the same model

Grades and run details

Decision model 82 · LLM judge 93
Decision model checks
  • failUses the supplied evidence correctly13%
  • passAddresses the actual decision83%
  • passRespects explicit constraints23%
  • passIdentifies material uncertainty98%
  • partialAvoids unsupported claims31%
  • passProduces the required deliverable76%
  • passChecks every criterion, region by region52%
  • passFinds the Scotland breach100%
  • passExplains the basket gap99%
  • passCatches the duplicate rows100%
  • passMakes a clear call94%
  • passLimits the downside of being wrong87%
  • failGets the base of every number right33%
  • passJudges the launch against a baseline set now90%
Run
Run
#1
Time to output
2.6 min
Submitted
25 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 6

Identifies material uncertaintyRightWrongRight
Haiku 5.5 · API

Identifies unknowns (duplicate export, audit may find further breaks, catalogue re‑map date, basket metric definition) and says how they would be resolved.

Gemini 3.5 Flash-Lite · Gemini

Does not name the specific unknowns that could change the decision (e.g., filter slipping past 9 Nov, Scotland refunds not recovering) or how they would be resolved.

Opus 5.5 · Claude

Names the dietary filter deployment, Scotland re-map date, and London nut incident as unknowns, and states what would change the decision.

Avoids unsupported claimsRightRightWrong
Haiku 5.5 · API

Speculation about causes is labelled with 'may' and 'partly', and assertions are backed by the data.

Gemini 3.5 Flash-Lite · Gemini

Interpretations like 'strongest performer' are clearly judgment, and causal links (supplier change driving refunds) are supported by incident logs.

Opus 5.5 · Claude

Presents 'North West's zero is luck, because it runs the same model' as fact without evidence that the model is identical across regions.

Explains the basket gapRightWrongRight
Haiku 5.5 · API

Shows that within each region treatment baskets equal or exceed control, explains the pooled £4 gap as an artefact of the uneven split.

Gemini 3.5 Flash-Lite · Gemini

Does not explain that the pooled basket gap is an artefact of the uneven traffic split; only states per-region baskets are neutral/positive without addressing the Simpson's paradox.

Opus 5.5 · Claude

Shows treatment baskets are equal or higher in every region and explains the pooled gap as a traffic-mix effect.

Catches the duplicate rowsRightWrongRight
Haiku 5.5 · API

Notices the duplicated North‑West rows for 8–10 October, removes them, and states that including them barely changes the result.

Gemini 3.5 Flash-Lite · Gemini

Does not mention or handle the duplicated North West treatment rows for 8–10 October flagged in incidents.csv.

Opus 5.5 · Claude

Notices the duplicate North West rows, removes them, and states they don't change the verdict.

Limits the downside of being wrongRightWrongRight
Haiku 5.5 · API

Describes kill switch, ramp with zero‑mismatch check, and review of refunds/complaints at one week, limiting downside if things go wrong.

Gemini 3.5 Flash-Lite · Gemini

Does not name a post-launch signal, threshold, or rollback plan; only mentions a '7-day post-fix stability review' without specifics.

Opus 5.5 · Claude

Specifies a kill switch (dietary mismatch or refund rate >10% above holdout over 3 days), control holdout, and region-by-region rollout.

Judges the launch against a baseline set nowRightWrongRight
Haiku 5.5 · API

The launch criteria themselves serve as the baseline, and specific review points (5 Nov for NW, 6 Nov for others) with conditions are set.

Gemini 3.5 Flash-Lite · Gemini

Does not name a success metric, its current baseline, or a review point with what each result would lead to.

Opus 5.5 · Claude

Names the success metrics (refund rate, dietary mismatches) and baseline (control holdout), with a continuous kill-switch trigger.

All mixed 2

Uses the supplied evidence correctlyMixedMixedMixed
Haiku 5.5 · API

Every factual statement about the current situation is drawn from the brief or the attached files, with no invented facts.

Gemini 3.5 Flash-Lite · Gemini

All factual claims are drawn directly from the supplied data and incident log, with no invented facts.

Opus 5.5 · Claude

All factual statements about the current situation are taken from the brief or derived correctly; the one unsupported claim is an interpretation, not a fact.

Gets the base of every number rightMixedMixedMixed
Haiku 5.5 · API

All percentages and rates (acceptance, refunds per 1 000) are computed from the correct totals, and basket figures are checked within region.

Gemini 3.5 Flash-Lite · Gemini

Refund rates are stated per 100 orders, which preserves the relative difference; all derived figures appear consistent with the data.

Opus 5.5 · Claude

All derived figures (acceptance, refunds per 1k, basket differences) are computed correctly from the right denominators.

All got right 6

Addresses the actual decisionRightRightRight
Haiku 5.5 · API

Commits to a clear, early call (no‑go for all on 2 Nov, conditional go for NW, hold others, no‑go for Scotland) and states what would change it.

Gemini 3.5 Flash-Lite · Gemini

Commits to a clear No-Go for 2 Nov, with conditions for staged rollout, and states what would change it (filter deployment, catalogue re-map).

Opus 5.5 · Claude

Commits to a clear no-go for 2 Nov, go with conditions for four regions from 5 Nov, and no-go for Scotland, with explicit conditions that would change the call.

Respects explicit constraintsRightRightRight
Haiku 5.5 · API

Respects length (under 900 words), includes the required region‑by‑region criteria table, and addresses the launch meeting audience.

Gemini 3.5 Flash-Lite · Gemini

Prose is under 900 words, includes a region-by-criterion table, and addresses the launch meeting as requested.

Opus 5.5 · Claude

Prose is under 900 words, includes the required region-by-criterion table, and is addressed to the launch meeting.

Produces the required deliverableRightRightRight
Haiku 5.5 · API

Delivers a complete recommendation with a criteria table, region‑by‑region calls, conditions, and actions for the meeting.

Gemini 3.5 Flash-Lite · Gemini

The recommendation is complete, in the right form for the meeting, and a PM could act on it with light edits.

Opus 5.5 · Claude

Delivers a complete recommendation with table, conditions, and clear prose usable by the meeting.

Checks every criterion, region by regionRightRightRight
Haiku 5.5 · API

The table checks all four criteria for each region with figures computed from the files (acceptance, refunds per 1,000, dietary incidents, basket).

Gemini 3.5 Flash-Lite · Gemini

The table checks all four launch criteria (acceptance, refunds vs control, dietary mismatches, basket value) for each region with figures from the files.

Opus 5.5 · Claude

Table checks all four criteria for each region with correct figures computed from the files.

Finds the Scotland breachRightRightRight
Haiku 5.5 · API

Finds Scotland's refund breach (30.2 vs 18.2, +66%), ties it to the supplier change, notes acceptance dip to 79%, and holds Scotland.

Gemini 3.5 Flash-Lite · Gemini

Identifies Scotland's refund breach (+117.9%), ties it to the 15 Oct supplier change, notes acceptance dip, and holds Scotland.

Opus 5.5 · Claude

Identifies Scotland's refund breach, ties it to the 15 Oct supplier change, notes acceptance dip, and holds Scotland.

Makes a clear callRightRightRight
Haiku 5.5 · API

Makes one clear call per region with dates (NW go 5 Nov, L/SE/Mid re‑decide 6 Nov, Scotland no‑go) and states what would change each call.

Gemini 3.5 Flash-Lite · Gemini

Makes a clear per-region call (No-Go for London, South East, Midlands, Scotland; Go with conditions for North West) with dates and conditions.

Opus 5.5 · Claude

Makes a clear per-region decision with dates and conditions, and says what would change it.

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 63% of 68 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Sonnet 5.5withAPI87.8100.02None
2Haiku 5.5withAPI82.5100.02None
3GPT-6.1 SolwithAPI86.095.82None
4Opus 5.5withClaude84.396.72None
5GPT-6 AstrawithChatGPT83.892.52None
6GPT-6 LunawithAPI74.880.82None
7Gemini 3.8 FlashwithAPI64.967.521 capped
8Gemini 3.5 Flash-LitewithGemini48.259.22None

What you get for the cost

Haiku 5.5 · API scores within 2 points of Sonnet 5.5 · API at about a tenth of the cost.

Task score against relative cost for each setup. The table below has the same figures.4050607080901001×3×10×30×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreSonnet 5.5 · API: task score 94, 37× the cheapestHaiku 5.5 · API: task score 92, 2.8× the cheapestGPT-6.1 Sol · API: task score 91, 12× the cheapestOpus 5.5 · Claude: task score 91, 59× the cheapest (1 of 2 outputs estimated)GPT-6 Astra · ChatGPT: task score 89, 46× the cheapest (1 of 2 outputs estimated)GPT-6 Luna · API: task score 77, the cheapestGemini 3.8 Flash · API: task score 55, 11× the cheapest, 1 critical failureGemini 3.5 Flash-Lite · Gemini: task score 52, 2.7× the cheapest (1 of 2 outputs estimated)Sonnet 5.5 · APIHaiku 5.5 · APIGPT-6.1 Sol · APIOpus 5.5 · ClaudeGPT-6 Astra · ChatGPTGPT-6 Luna · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and costCost partly estimated (app runs with no usage meter)At least one critical failure
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API771.0×
Gemini 3.5 Flash-Lite · Gemini522.7× (partly estimated)
Haiku 5.5 · API922.8×
Gemini 3.8 Flash · API5511×
GPT-6.1 Sol · API9112×
Sonnet 5.5 · API9437×
GPT-6 Astra · ChatGPT8946× (partly estimated)
Opus 5.5 · Claude9159× (partly estimated)

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

About the task

The PM job

Deciding whether a feature ships on the planned date, and on what conditions.

Why it matters

Launch meetings reward optimism. A good call checks each agreed criterion, weighs a real risk against a real gain, and says exactly what would change the answer. A weak one rubber-stamps the launch or blocks it on noise.

What good looks like

  • Checks each agreed criterion against the evidence
  • Makes one clear call, with conditions if needed
  • Separates risks that block launch from risks that can be managed
  • Says what would change the call
  • Says how to limit the damage if the call is wrong

Deliberately not measured

  • Rollout engineering detail
  • Project-plan formatting
Capability tested

Deciding against agreed launch criteria

The failure we’re looking for

Rubber-stamps a launch that misses an agreed bar, or blocks it on noise

Grading

Decision model and LLM judge, calibrated against a blind PM review

Variants

A launch memo from supplied evidence · Staff level: a regional call from an attached data workbook