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 18 graded outputs by 9 models. 67% were usable with at most a quick edit.

Reliably right

  1. Produces the required deliverable100% pass
    Provides a complete recommendation with table, reasoning, email plan, and next steps, usable by a product manager with light edits.
    Sonnet 5.5 · API · Offline, on older phones
  2. Finds the Scotland breach100% pass
    Finds Scotland's refund breach, ties it to the supplier change, notes the acceptance dip, and holds Scotland.
    GPT-6.1 Sol · API · Go/no-go for AI substitutions, from the rollout data
  3. Treats the overwrites as data loss100% pass
    Treats the Android 11 overwrites as a breach of the no-data-loss criterion and makes a fix a condition for that platform.
    Sonnet 5.5 · API · Offline, on older phones

Where it slips

  1. Judges the launch against a baseline set now51% 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
  2. Checks every criterion for every platform64% 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
  3. Gets the base of every number right67% pass
    The 80.5% user base proportion is miscalculated; correct proportion from the data is 79.5%.
    Gemini 4 Argon · API · 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 · v1.13

The outputs

9 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 82 · LLM judge 100
Decision model checks
  • failUses the supplied evidence correctly22%
  • passAddresses the actual decision72%
  • passRespects explicit constraints34%
  • passIdentifies material uncertainty100%
  • partialAvoids unsupported claims46%
  • passProduces the required deliverable56%
  • passChecks every criterion, region by region37%
  • passFinds the Scotland breach100%
  • passExplains the basket gap64%
  • passCatches the duplicate rows100%
  • passMakes a clear call85%
  • passLimits the downside of being wrong56%
  • failGets the base of every number right55%
  • passJudges the launch against a baseline set now28%
Run
Run
#1
API response time
1.9 min
Submitted
8 Oct 2026

Usable with a quick edit13 of 14 checks passed

The output is complete, well-structured, and directly actionable. A PM could use it with at most minor wording tweaks.

Output

Recommendation for 30 October: go with conditions in four regions; no-go in Scotland

Do not expand any region on Monday 2 November. London, South East, Midlands and North West are candidates for a gated rollout after the dietary hard filter is deployed and validated—not before its scheduled 4 November deployment. Scotland must additionally resolve and re-test its catalogue mapping.

Christmas urgency does not justify launching ahead of a known safety fix. The 9 November booking window should guide sequencing, not override the PRD.

Method and launch-criterion checks

I removed three exact duplicate North West treatment rows, dated 8–10 October, consistent with the export incident. This leaves 280 unique date–region–arm records.

Acceptance and refunds below use ratios of summed counts, not averages of daily rates. Basket values are order-weighted. T = treatment; C = control. Dietary counts are recorded treatment incidents; zero recorded incidents is not proof that none occurred.

RegionTreatment acceptance ≥80%Refunds/1,000 orders: T vs C; relative change ≤+10%Zero dietary/allergen mismatchesAverage basket: T vs C; difference ≥£0Recommendation
LondonPass: 86.7% (23,607/27,234)Pass: 17.44 vs 18.04; −3.3%Fail: 1, nut-free → hazelnuts, 23 OctPass: £72.70 vs £72.49; +£0.21Go with conditions; not 2 Nov
South EastPass: 86.4% (26,586/30,770)Pass: 17.54 vs 17.89; −1.9%Fail: 1, vegan → dairy, 19 OctPass: £66.64 vs £66.20; +£0.44Go with conditions; not 2 Nov
MidlandsPass: 86.3% (26,732/30,958)Pass: 17.48 vs 17.89; −2.3%Fail: 1, gluten-free → standard bread, 4 OctPass: £55.66 vs £55.35; +£0.31Go with conditions; not 2 Nov
North WestPass: 86.3% (18,153/21,039)Pass: 17.58 vs 18.47; −4.8%Pass on incident log: 0 recordedPass: £57.51 vs £57.29; +£0.22Go with conditions; not 2 Nov
ScotlandPass over 28 days: 82.8% (14,333/17,319)Fail: 30.16 vs 18.20; +65.7%Pass on incident log: 0 recordedPass: £52.98 vs £52.62; +£0.36No-go

What the data actually say

Commercial’s overall refund claim hides a regional failure. After deduplication, pooled refunds are 19.50 per 1,000 treatment orders versus 18.08 control: +7.9%, within the overall guardrail. But the PRD requires compliance in every region, and Scotland fails badly. Strong acceptance cannot compensate for dietary breaches.

Analytics’ £4 basket concern is real in the pooled arithmetic, but not within regions. Pooled baskets are £61.09 treatment versus £65.15 control (−£4.06). Treatment disproportionately includes lower-basket Midlands and Scotland orders, while control disproportionately includes higher-basket London orders. Every within-region comparison is positive. This is a composition effect, not evidence here that AI reduces basket value. These are point estimates; the daily averages alone do not establish statistical certainty.

Scotland is not settling down. Following the supplier switch on 15 October:

  • Acceptance falls from 86.3% on 1–14 October to 79.1% on 15–28 October, below the launch threshold.
  • Treatment refunds rise from 17.25 to 43.25 per 1,000 orders. Post-switch control is 17.79, making treatment 143.1% worse.
  • The final week still has 79.2% acceptance and 43.64 refunds per 1,000.

The open pack-size mapping incident is consistent with this sustained deterioration. The full-period acceptance pass masks the current operating state.

Conditions and release plan

For London, South East, Midlands and North West, require:

  1. Deployed, validated safety protection: successful regression tests across gluten-free, vegan, vegetarian, nut-free, halal and kosher attributes, including missing/incorrect metadata and the three observed failures. “Merged” is not “protected.”
  2. Fresh post-fix evidence: a controlled validation window with zero dietary mismatches and all regional numerical criteria met, with explicit launch sign-off. Refunds and apologies do not erase historical criterion failures.
  3. Staged regional expansion: monitor acceptance, refunds and dietary incidents daily; stop immediately for any dietary mismatch and roll back for guardrail breaches. Reconcile the North West export before reporting.

For Scotland, also require a completed catalogue re-map, pack-size validation and fresh post-remediation evidence. With no completion date and continuing deterioration, make no Christmas rollout commitment.

Meeting decision: reject the all-region 2 November launch; approve a gated path for four regions, while keeping Scotland blocked.

Grades and run details

Decision model 93 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly19%
  • passAddresses the actual decision77%
  • passRespects explicit constraints45%
  • passIdentifies material uncertainty98%
  • passAvoids unsupported claims22%
  • passProduces the required deliverable83%
  • passChecks every criterion, region by region70%
  • passFinds the Scotland breach100%
  • passExplains the basket gap100%
  • passCatches the duplicate rows94%
  • passMakes a clear call40%
  • passLimits the downside of being wrong95%
  • failGets the base of every number right28%
  • passJudges the launch against a baseline set now14%
Run
Run
#1
API response time
1.9 min
Submitted
29 Sept 2026

Needs more than a quick edit10 of 14 checks passed

While the core analysis and regional decisions are solid and well-supported, the output misses a post-launch monitoring plan (signal, threshold, trigger for rollback) and a defined success baseline with a review point, which a PM would need to add before taking this to a launch meeting.

Output

Executive Recommendation

  • Monday, 2 November: NO-GO across all regions. We cannot launch while failing Criterion 3 (zero dietary/allergen mismatches). During the test, the AI model caused three high-severity safety incidents—substituting standard bread for gluten-free (coeliac customer), dairy cheese for vegan, and hazelnut bars for nut-free (child with nut allergy). The engineering hard filter to prevent cross-attribute substitution does not deploy until Wednesday, 4 November. A 48-hour delay is non-negotiable for customer safety.
  • Thursday, 5 November: GO WITH CONDITIONS for London, South East, Midlands, and North West. Once the dietary hard filter is deployed on 4 November and verified in production, these four regions pass all launch criteria and can roll out to 100% four days ahead of the 9 November Christmas booking window.
  • Scotland: NO-GO indefinitely. Keep Scotland on 100% picker-led control until the dairy catalogue re-map is completed and verified via a clean 7-day re-test.

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Data Hygiene & Addressing Stakeholder Summaries

Before reviewing the criteria table, we must correct three data misconceptions circulating ahead of this meeting:

  1. Duplicate Rows (North West): As flagged in `incidents.csv` (7 Oct), an export timeout duplicated North West treatment rows on 8, 9, and 10 October. All figures below are deduplicated (removing 2,194 phantom orders). Note: Criterion conclusions for North West hold either way, though deduplicating slightly raises its refund rate from 17.56 to 17.59 per 1,000.
  2. The "£4 Basket Drop" is Simpson’s Paradox (Head of Analytics): Pooled across the test, treatment average basket value (£60.15) is indeed £3.93 lower than control (£64.08). However, this is a mathematical artefact of our unequal regional split: we allocated 70% of orders to treatment in our lowest-basket regions (Scotland £53, Midlands £55) and only 30% in our highest-basket region (London £72). Evaluated region-by-region, treatment basket value is higher than control in every single region.
  3. Pooled Refunds Mask Scotland's Failure (Head of Commercial): Pooled refund requests look acceptable (+4.5% vs. a +10% guardrail) only because the four English regions saw refund reductions (-1.5% to -7.8%). This masks a +78.3% refund explosion in Scotland. Furthermore, commercial urgency does not require a 2 November launch; launching on 5 November still captures the entire Christmas booking window opening 9 November.

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Launch Criteria Performance by Region (1–28 Oct, Deduplicated)

Region (Trt Split)C1: Sub Acceptance (Target: ≥80.0%)C2: Refunds / 1k Orders (Target: ≤ +10.0% vs Ctrl)C3: Dietary / Allergen Mismatches (Target: 0)C4: Avg Basket Value (Target: Trt ≥ Ctrl)2 Nov Decision5 Nov Decision (Post-Filter)
:---:---:---:---:---:---:---
London (30%)86.78% (23,314 / 26,866) Ctrl: 70.85% \PASS17.46 vs 18.21 Ctrl (-4.1%) 369/21,136 vs 890/48,874 \PASS1 High (23 Oct: Nut allergy) FAIL£72.64 vs £72.49 (+£0.15) Simple: £72.65 vs £72.49 \PASSNO-GOGO
South East (50%)86.37% (26,548 / 30,735) Ctrl: 70.69% \PASS17.27 vs 17.85 Ctrl (-3.3%) 462/26,756 vs 473/26,498 \PASS1 High (19 Oct: Vegan/dairy) FAIL£66.64 vs £66.26 (+£0.38) Simple: £66.66 vs £66.27 \PASSNO-GOGO
Midlands (70%)86.33% (26,669 / 30,891) Ctrl: 70.89% \PASS17.56 vs 17.83 Ctrl (-1.5%) 544/30,975 vs 231/12,953 \PASS1 High (4 Oct: Coeliac/gluten) FAIL£55.68 vs £55.39 (+£0.29) Simple: £55.66 vs £55.39 \PASSNO-GOGO
North West (50%)*86.52% (18,327 / 21,182) Ctrl: 71.36% \PASS17.59 vs 19.08 Ctrl (-7.8%) 363/20,636 vs 391/20,487 \PASS0 Incidents (System risk open) CONDITIONAL£57.52 vs £57.33 (+£0.19) Simple: £57.53 vs £57.32 \PASSNO-GOGO
Scotland (70%)82.45% (14,508 / 17,596) Ctrl: 71.15% \PASS* (78.7% post-15 Oct)31.71 vs 17.79 Ctrl (+78.3%) 579/18,257 vs 140/7,871 \FAIL0 Incidents (System risk open) CONDITIONAL£52.97 vs £52.66 (+£0.31) Simple: £52.96 vs £52.65 \PASSNO-GONO-GO
Pooled Total (Distorted)85.99% vs 70.91% Ctrl (+15.08 pp)19.68 vs 18.23 Ctrl (+7.9% dedup / +4.5% raw)3 High-Severity Incidents£60.15 vs £64.08 Ctrl (-£3.93 Simpson's Paradox)NO-GOPARTIAL GO

\Note: North West metrics reflect deduplicated data (raw orders: 22,830; raw refunds/1k: 17.56; raw weighted basket: £57.50). Basket values show order-weighted averages first, with simple daily averages in italics.*

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Regional Rationale & Conditions

#### 1. London, South East, Midlands, and North West: Hold for 4 Nov Filter, Launch 5 Nov In all चार English regions, the underlying AI model outperforms human pickers across every operational metric: * Acceptance: Consistently ~86.5% across all four regions (+15.5 points over control), well above the 80% threshold. * Refunds: Lower than control in every region (ranging from -1.5% in the Midlands to -7.8% in the North West). Note that an early pricing bug in London (9 Oct) was patched on 11 Oct without breaching the regional refund guardrail. * Baskets: Up £0.15 to £0.38 per order over control. * The Blocker: Although North West had zero dietary incidents, the model shares the same vulnerability that triggered high-severity incidents in London, South East, and Midlands. Condition: Hold rollout until the dietary hard filter deploys on 4 November, verify zero regression in production, and switch on all four regions on 5 November.

#### 2. Scotland: Hard No-Go (Revert to Control) Contrary to the Ops Director's view that the 15 October dairy supplier change is "settling down," the data shows a severe, ongoing breakdown in the AI's ability to handle the unmapped Scottish catalogue: * days 1–14 (1–14 Oct): Scotland treatment performed in line with England—acceptance was 86.44% (7,976 / 9,227) and refund requests were 17.46 per 1,000 (162 / 9,280), beating control (18.22). * Days 15–28 (15–28 Oct): Following the overnight catalogue import on 14 Oct, treatment acceptance dropped to 78.05% (6,532 / 8,369)—failing Criterion 1—and never recovered (sitting at 77.76% on 28 Oct). Worse, treatment refund requests more than doubled to 46.45 per 1,000 (417 / 8,977, vs. 17.39 in control), driving a +78.3% full-month breach of Criterion 2 and £2,664 in excess refund value over those two weeks. Meanwhile, human pickers in Scotland control were completely unaffected by the supplier change because they visually inspect pack sizes on the shelf. * Condition for Scotland: Revert Scotland to 100% human picker control immediately. Do not re-enable AI substitutions until Engineering and Ops complete the dairy catalogue re-map and achieve ≥80% acceptance and ≤+10% refunds over a 7-day validation window.

Grades and run details

Decision model 68 · LLM judge 80
Decision model checks
  • failUses the supplied evidence correctly22%
  • passAddresses the actual decision55%
  • partialRespects explicit constraints40%
  • passIdentifies material uncertainty88%
  • partialAvoids unsupported claims31%
  • passProduces the required deliverable52%
  • passChecks every criterion, region by region58%
  • passFinds the Scotland breach100%
  • passExplains the basket gap100%
  • passCatches the duplicate rows100%
  • passMakes a clear call33%
  • partialLimits the downside of being wrong23%
  • failGets the base of every number right48%
  • failJudges the launch against a baseline set now22%
Run
Run
#1
API response time
23 s
Submitted
13 Oct 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 3

Uses the supplied evidence correctlyMixedRightMixed
Haiku 5.5 · API

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

GPT-6.1 Sol · API

All factual claims about the current situation are taken directly from the supplied data or derived correctly from it.

Gemini 4 Argon · API

Every claim about the current situation is supported by the supplied files (incidents.csv, daily_metrics.csv, provided context) or correct arithmetic based on them.

Limits the downside of being wrongRightRightWrong
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.

GPT-6.1 Sol · API

Specifies post-launch monitoring (daily acceptance, refunds, dietary incidents), stop/rollback triggers, and reconciliation of the North West export.

Gemini 4 Argon · API

The output specifies pre-launch verification conditions but does not name a post-launch signal, a threshold that would trigger a pause or rollback, or clearly flag any irreversible actions for the four regions set to launch.

Judges the launch against a baseline set nowRightRightWrong
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.

GPT-6.1 Sol · API

Sets the launch criteria as the baseline and requires a controlled validation window with explicit sign-off before expansion.

Gemini 4 Argon · API

The output does not name a specific success metric and its current baseline from the test that the launch will be judged against, nor does it set a post-launch review point with what each result would lead to.

All mixed 1

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.

GPT-6.1 Sol · API

States that ratios use summed counts and baskets are order-weighted; all derived figures check out against the data.

Gemini 4 Argon · API

All derived figures (acceptance rates, refunds per 1,000, basket values, percentage changes) use correct denominators and arithmetic, with basket values specified as order-weighted.

All got right 10

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.

GPT-6.1 Sol · API

Commits to a clear go-with-conditions for four regions and no-go for Scotland, with explicit conditions and what would change the call.

Gemini 4 Argon · API

The output gives a clear NO-GO for all regions on 2 November, a GO with conditions for four regions on 5 November, and a NO-GO for Scotland, directly addressing the launch meeting's decision and stating what would change each 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.

GPT-6.1 Sol · API

Respects the word limit, includes the required table, addresses the meeting, and works from the data files.

Gemini 4 Argon · API

The output is a recommendation for the meeting, includes the required region-by-criteria table with figures, and keeps prose under 900 words.

Identifies material uncertaintyRightRightRight
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.

GPT-6.1 Sol · API

Identifies the need for post-fix validation, the open catalogue re-map, and notes that point estimates alone do not establish statistical certainty.

Gemini 4 Argon · API

It identifies the dietary filter deployment date and Scotland's catalogue re-map as key unknowns that hold or change the decision, and specifies a clean 7-day re-test for Scotland to resolve the uncertainty.

Avoids unsupported claimsRightRightRight
Haiku 5.5 · API

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

GPT-6.1 Sol · API

Interpretations like the pack-size mapping being consistent with deterioration are labelled as such, and no unsupported causal claims are presented as fact.

Gemini 4 Argon · API

The Simpson's paradox explanation for the basket gap is presented as a calculated correction, not an unsupported cause, and the 'settling down' quote from Ops Director is appropriately disputed with data showing the opposite.

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.

GPT-6.1 Sol · API

Produces a complete recommendation with a table, under 900 words, usable by the meeting with light edits.

Gemini 4 Argon · API

The output is a complete, actionable recommendation with a decision table, regional rationale, and conditions, usable for the meeting with no major gaps.

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).

GPT-6.1 Sol · API

Table checks all four launch criteria region by region with correct figures computed from the files.

Gemini 4 Argon · API

The recommendation tests all four launch criteria (sub acceptance, refunds vs control, dietary mismatches, basket value) region by region in a table 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.

GPT-6.1 Sol · API

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

Gemini 4 Argon · API

It identifies Scotland's refund breach (+78.3%), ties the sharp decline to the 15 October supplier change with pre/post metrics, notes the acceptance dip below 80%, and recommends holding Scotland.

Explains the basket gapRightRightRight
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.

GPT-6.1 Sol · API

Shows within-region baskets are equal or higher and explains the pooled gap as a composition effect from the uneven split.

Gemini 4 Argon · API

It explicitly names Simpson's Paradox and demonstrates that treatment basket values are higher than control in every region, explaining the pooled gap as an uneven-split artifact.

Catches the duplicate rowsRightRightRight
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.

GPT-6.1 Sol · API

Catches the duplicate North West treatment rows, removes them, and uses deduplicated figures.

Gemini 4 Argon · API

It catches the duplicate North West treatment rows for 8–10 October, states they were removed (2,194 orders), and notes the criterion conclusions for North West hold either way.

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.

GPT-6.1 Sol · API

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

Gemini 4 Argon · API

It makes one clear call per region with specific dates (NO-GO 2 Nov, GO with conditions 5 Nov for four regions, NO-GO indefinitely for Scotland) and states the conditions that would change each.

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 62% of 68 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Sonnet 5.5withAPI92.4100.02None
2GPT-6.1 SolwithAPI91.995.82None
3Haiku 5.5withAPI84.3100.02None
4GPT-6 AstrawithChatGPT91.992.52None
5Opus 5.5withClaude84.396.72None
6GPT-6 LunawithAPI80.280.82None
7Gemini 4 ArgonwithAPI70.373.32None
8Gemini 3.5 Flash-LitewithGemini55.059.22None
9Gemini 3.8 FlashwithAPI64.467.521 capped

What you get for the cost

Haiku 5.5 · API scores within 3 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 96, 37× the cheapestGPT-6.1 Sol · API: task score 94, 12× the cheapestHaiku 5.5 · API: task score 93, 2.8× the cheapestGPT-6 Astra · ChatGPT: task score 93, 46× the cheapest (1 of 2 outputs estimated)Opus 5.5 · Claude: task score 91, 59× the cheapest (1 of 2 outputs estimated)GPT-6 Luna · API: task score 79, the cheapestGemini 4 Argon · API: task score 71, 14× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 55, 2.7× the cheapest (1 of 2 outputs estimated)Gemini 3.8 Flash · API: task score 54, 11× the cheapest, 1 critical failureSonnet 5.5 · APIGPT-6.1 Sol · APIHaiku 5.5 · APIGPT-6 Astra · ChatGPTOpus 5.5 · ClaudeGPT-6 Luna · APIGemini 4 Argon · APIGemini 3.5 Flash-Lite · GeminiGemini 3.8 Flash · API
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 · API791.0×
Gemini 3.5 Flash-Lite · Gemini552.7× (partly estimated)
Haiku 5.5 · API932.8×
Gemini 3.8 Flash · API5411×
GPT-6.1 Sol · API9412×
Gemini 4 Argon · API7114×
Sonnet 5.5 · API9637×
GPT-6 Astra · ChatGPT9346× (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