Humanity’s Last PRD
Latest drop: Sonnet 5.5 · 30 Sept
Tasks / Operate

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?

Measures the modelTask v1.2 · 2 casesDifficulty

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

From 12 graded outputs by 6 models. 75% were usable with at most a quick edit.

Reliably right

  1. Finds the Scotland breach100% pass
    Clearly identifies Scotland's refund breach (66% above control, 143% after supplier change) and acceptance dip below 80%, correctly holds Scotland.
    Opus 5.5 · Claude · Go/no-go for AI substitutions, from the rollout data
  2. Respects explicit constraints98% pass
    The output is a launch recommendation, addresses the meeting, and stays within the 400-word limit (345 words).
    GPT-6 Astra · ChatGPT · Go/no-go for AI-drafted support replies
  3. Produces the required deliverable98% pass
    The recommendation is in the requested form, for the meeting, within the length, and includes necessary steps a PM could act on.
    GPT-6 Astra · ChatGPT · Go/no-go for AI-drafted support replies

Where it slips

  1. Limits the downside of being wrong35% pass
    No specific post-launch monitoring signal or threshold is named; only generic 'monitoring' and a kill switch are mentioned.
    GPT-6 Astra · ChatGPT · Go/no-go for AI-drafted support replies
  2. Catches the duplicate rows54% pass
    Finds and removes the duplicate rows but never states whether they change the results.
    GPT-6.1 Sol · API · Go/no-go for AI substitutions, from the rollout data
  3. Gets the base of every number right67% pass
    Some derived figures are off, including the pooled basket gap and the control-order share for London, and the basket base is stated as means of daily averages rather than order-weighted averages.
    Sonnet 5.5 · API · Go/no-go for AI substitutions, from the rollout data

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 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.

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 does

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)
  • Recommends any rollout before the dietary hard filter is deployed
  • Recommends rolling out to Scotland while its refund guardrail is breached
Case

v1.6 · synthetic · e-commerce, AI product, data analysis, staff-level

Opus 5.5ClaudeUsable with a quick editSonnet 5.5APIUsable with a quick editGPT-6.1 SolAPIUsable with a quick editGPT-6 AstraChatGPTUsable with a quick editGPT-6 LunaAPINeeds more workGemini 3.5 Flash-LiteGeminiNeeds more work
GPT-6.1 SolAPI

Usable with a quick edit10 of 13 checks passed

The recommendation, table, and calculations are solid; only minor wording additions to state duplicate impact and flag irreversible dietary harm are needed.

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.

Check by check

Got wrong · 2
  • Catches the duplicate rowsFinds and removes the duplicate rows but never states whether they change the results.
  • Limits the downside of being wrongGives monitoring and rollback thresholds but does not flag irreversible consequences such as customer-facing dietary substitutions or communications.
Mixed · 1
  • Gets the base of every number rightAll percentages, per-1,000 rates, and basket differences use correct summed or order-weighted bases.The two graders disagreed on this one.
Got right · 10
  • Uses the supplied evidence correctlyAll current-state numbers and facts trace to the brief, incidents.csv, or arithmetic from daily_metrics.csv.
  • Addresses the actual decisionCommits to a clear region-by-region go/no-go with dates and conditions for the launch meeting.
  • Respects explicit constraintsIncludes the required region-by-criterion table and keeps prose within the length while respecting the no-early-launch safety constraint.
  • Identifies material uncertaintyNames filter deployment/validation and Scotland catalogue remap as unresolved and ties go decisions to fresh evidence.
  • Avoids unsupported claimsCausal claims are hedged ('consistent with') and interpretations are labelled as composition effects or point estimates.
  • Produces the required deliverableProvides a usable recommendation, table, and release conditions that the reader could act on with light edits.
  • Checks every criterion, region by regionRegion-by-criterion table covers acceptance, refunds vs control, dietary incidents, and basket values with computed figures.
  • Finds the Scotland breachFinds Scotland's refund breach, ties it to the supplier change with post-15 Oct numbers, and holds Scotland.
  • Explains the basket gapExplains the pooled basket gap as a traffic-mix effect and shows all within-region treatment baskets positive.
  • Makes a clear callMakes a clear per-region call with dates and conditions, and Scotland is blocked.

Grades and run details

Decision model 85 · LLM judge 12 of 14 checks
Decision model checks
  • passUses the supplied evidence correctly26%
  • passAddresses the actual decision81%
  • passRespects explicit constraints41%
  • passIdentifies material uncertainty46%
  • passAvoids unsupported claims42%
  • passProduces the required deliverable79%
  • passChecks every criterion, region by region71%
  • passFinds the Scotland breach100%
  • passExplains the basket gap99%
  • partialCatches the duplicate rows49%
  • passMakes a clear call34%
  • partialLimits the downside of being wrong43%
  • failGets the base of every number right22%
Run
Run
#1
API response time
1.9 min
Submitted
29 Sept 2026
GPT-6 LunaAPI

Needs more than a quick edit9 of 13 checks passed

The refund figures are inaccurate, so a PM would need to re-check the data and recalculate before using the recommendation. The output is not usable as-is.

Output

Recommendation: No-go for the all-region rollout on 2 November

Do not enable auto-substitutions anywhere on 2 November: the dietary hard filter is not scheduled to deploy until 4 November, so the zero-mismatch safety criterion is not yet protected. After deployment and validation, North West is the only region I’d consider enabling first, with conditions. Hold the other regions for the reasons below.

Figures are for 1–28 October; refund rates are per 1,000 orders. Basket averages are order-weighted within each region. I excluded the three exact duplicate North West treatment rows on 8, 9 and 10 October.

RegionTreatment acceptance (≥80%)Refunds / 1,000 orders: treatment vs control (treatment ≤10% higher)Dietary/allergen mismatches (must be zero)Average basket: treatment vs control (must not be lower)Decision
------:---:------:---
London~86% Pass~17.9 vs ~18.4; Pass1; Fail — nut-containing bar substituted for nut-free item~£72.8 vs ~£72.5; PassNo-go
South East~85% Pass~17.8 vs ~17.9; Pass1; Fail — dairy cheese substituted for vegan cheese~£66.6 vs ~£66.3; PassNo-go
Midlands~86% Pass~17.6 vs ~18.0; Pass1; Fail — standard loaf substituted for gluten-free loaf~£55.6 vs ~£55.4; PassNo-go
North West~86% Pass~17.8 vs ~18.8; Pass0 observed; Pass in test~£57.5 vs ~£57.3; PassGo with conditions, after filter validation
Scotland~79% Fail1~29.8 vs ~18.4; Fail — about 62% higher0 observed; Pass in test~£53.0 vs ~£52.6; PassNo-go

Conditions and rationale

  • North West: After the hard filter deploys, pass regression tests across all six protected attributes (gluten-free, vegan, vegetarian, nut-free, halal and kosher); then enable regionally with close monitoring and a rollback switch. The export duplicates should also be fixed or guarded against before relying on daily reporting.
  • London, South East and Midlands: Do not launch until the filter is deployed and the dietary-mismatch failures are investigated and validated as fixed. Their good acceptance, refund and basket figures do not offset a safety-criterion failure.
  • Scotland: Hold until the supplier catalogue re-map is complete and verified, and a fresh test meets both acceptance and refund guardrails. The sharp rise in treatment refunds and lower acceptance coincide with the dairy supplier/catalogue change; the issue is open, with no completion date.

The pooled “£4 smaller” basket claim should not drive this decision: treatment exposure differed by region, and baskets vary substantially by region. Within each region, the treatment average is slightly higher in these data. Likewise, an overall acceptance or refund result cannot override region-level launch gates. The 9 November booking window is important, but it does not justify launching before the safety control is live.

What a PM had to fix

  1. 1Invented evidenceVerify or remove the claimTargeted repair

    What we’d change82.76%: Pass over 1–28 October. However, acceptance fell to 79.08% over 15–28 October after the supplier change, below the launch threshold.

Check by check

Got wrong · 2
  • Limits the downside of being wrongDoes not name a specific post-launch signal, threshold, or trigger for rollback; only says 'close monitoring and a rollback switch' without specifics.
  • Gets the base of every number rightMultiple refund-per-1000 figures are miscalculated (e.g., London treatment 17.9 vs 17.4, London control 18.4 vs 18.0), so the base numbers are wrong.
Mixed · 2
  • Uses the supplied evidence correctlySeveral refund-per-1000 figures are incorrect (e.g., London treatment 17.9 vs actual 17.4, London control 18.4 vs 18.0), so the output does not use the supplied evidence correctly.The two graders disagreed on this one.
  • Checks every criterion, region by regionThe table checks each criterion per region but the refund figures are incorrect, so it does not present correct figures from the files.The two graders disagreed on this one.
Got right · 9
  • Addresses the actual decisionCommits to a clear no-go for all-region on 2 Nov, then conditional go for North West after filter, with conditions and what would change it.
  • Respects explicit constraintsIncludes the required region-by-region table, prose under 900 words, and addresses the launch meeting.
  • Identifies material uncertaintyNames the filter deployment, duplicate fix, and Scotland catalogue re-map as unknowns that could change the decision.
  • Avoids unsupported claimsCausal claims like the supplier change coinciding with refunds are presented as observations, not unsupported facts.
  • Produces the required deliverableProvides a complete recommendation with table, region-by-region, usable for the meeting.
  • Finds the Scotland breachIdentifies Scotland's refund breach, ties it to the supplier change, notes acceptance dip, and holds Scotland.
  • Explains the basket gapExplains the pooled basket gap as a mix effect, noting treatment baskets are equal or higher within each region.
  • Catches the duplicate rowsCatches the duplicate North West rows, excludes them, and recommends fixing the export job.
  • Makes a clear callMakes a clear call per region with dates and conditions, and says what would change it.

Claims the judge couldn’t find in the brief

  • London treatment refunds per 1,000 orders is ~17.9.
  • London control refunds per 1,000 orders is ~18.4.
  • South East treatment refunds per 1,000 orders is ~17.8.
  • Midlands treatment refunds per 1,000 orders is ~17.6.
  • North West treatment refunds per 1,000 orders is ~17.8 (after excluding duplicates).
  • Scotland control refunds per 1,000 orders is ~18.4.

Grades and run details

Decision model 77 · LLM judge 9 of 14 checks
Decision model checks
  • passUses the supplied evidence correctly26%
  • passAddresses the actual decision46%
  • passRespects explicit constraints61%
  • partialIdentifies material uncertainty46%
  • passAvoids unsupported claims39%
  • passProduces the required deliverable64%
  • passChecks every criterion, region by region31%
  • passFinds the Scotland breach91%
  • passExplains the basket gap84%
  • partialCatches the duplicate rows59%
  • partialMakes a clear call70%
  • partialLimits the downside of being wrong97%
  • failGets the base of every number right31%
Run
Run
#1
API response time
44 s
Submitted
29 Sept 2026

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 86% of checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1Sonnet 5.5API93.788.32None
2GPT-6 AstraChatGPT89.888.32None
3GPT-6.1 SolAPI87.388.32None
4Opus 5.5Claude80.486.42None
5GPT-6 LunaAPI86.077.62None
6Gemini 3.5 Flash-LiteGemini53.142.22None

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