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 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
Sonnet 5.5API

Usable with a quick edit11 of 13 checks passed

A PM could use it with light edits, mainly correcting minor arithmetic and removing the unsupported incident-reporting inference. The core decision, table, and conditions are actionable.

Output

Recommendation: AI auto-substitutions, launch meeting 30 October

Decision: no-go for Monday 2 November. Go with conditions from 4 November for London, South East, Midlands and North West. Scotland stays on picker substitutions until it re-passes.

Criteria by region

Figures are recomputed from the daily rows. Treatment (T) and control (C) are pooled over the 28 days.

Region1. Acceptance (T) ≥80%2. Refunds per 1,000 orders, T vs C (≤ +10%)3. Dietary mismatches (0)4. Basket T vs C (not lower)Verdict
London86.7% ✅17.4 vs 18.0 (−3%) ✅1 (nut allergen, 23 Oct, open) ❌£72.72 vs £72.48 (+£0.24) ✅Conditional
South East86.4% ✅17.5 vs 17.9 (−2%) ✅1 (vegan cheese, 19 Oct) ❌£66.66 vs £66.20 (+£0.46) ✅Conditional
Midlands86.3% ✅17.5 vs 17.9 (−2%) ✅1 (gluten-free, 4 Oct) ❌£55.66 vs £55.35 (+£0.31) ✅Conditional
North West86.3% ✅17.6 vs 18.5 (−5%) ✅0 logged ✅£57.52 vs £57.28 (+£0.24) ✅Conditional
Scotland82.8% overall, 79.1% since 15 Oct ❌30.2 vs 18.2 (+66%); since 15 Oct 43.3 vs 17.8 (+143%) ❌0 logged ✅£52.98 vs £52.62 (+£0.36) ✅No-go

Baskets are means of daily averages. The differences are small and I have not tested them for significance, but the point estimate is positive everywhere.

Checking what people have said

  • Head of Commercial. The pooled numbers are right: acceptance is 85.9% against 71.2% (+14.7 points), and refunds are 19.5 against 18.1 per 1,000 (+8%). But the pooled refund figure hides Scotland, which is failing badly. Four regions are 2–5% better than control on refunds.
  • Head of Analytics. The £4 gap is real in the pooled data (£61.10 vs £65.15) but it is a mix effect, not a treatment effect. London, with the highest baskets (about £72), was 42% of control orders but only 18% of treatment orders. The Midlands and Scotland (about £53–56) were 18% of control but 42% of treatment. Within every region, treatment baskets are £0.24–£0.46 higher. Criterion 4 passes. The uneven split did not bias the regional comparisons.
  • Ops director (Scotland). The data does not support "settling down". Before the supplier switch on 15 October, Scotland treatment was healthy (86.3% acceptance, 17.3 refunds per 1,000). From 15 October to 28 October, acceptance has been flat at 78–81%, and daily refunds have been 25–33 against a pre-switch level of 10–14. The cause is the open incident: the new supplier's pack sizes are mapped to the wrong product family, so the model makes wrong-size dairy substitutions. That is a catalogue defect with no fix date, not noise. Scotland control is unaffected.

Why the date moves

Criterion 3 fails in three regions. The three mismatches include a nut-allergy case and a coeliac case. The hard filter that prevents them deploys on 4 November. Until it is live, every region has the same exposure. North West and Scotland show zero only because none were reported, and the incidents come from customer complaints, so the true rate is unknown. I would not switch on a known allergen risk two days before the fix.

Waiting is cheap. The booking window opens on 9 November, not 2 November. Deploying the filter on 4 November and switching regions on 4–5 November leaves several days before the window. Only Scotland has no route to launch before then.

Conditions for the four regions

  1. The filter deploys on 4 November and passes regression tests. Before any region goes live, it is tested in production against the three incident SKUs (gluten-free loaf, vegan cheese, nut-free bar) and the other attributes: vegetarian, halal and kosher. The London incident is still open with the filter "in review", so confirm the filter would have blocked it.
  2. Regions are switched on region by region, North West and South East first, then London and the Midlands. This gives a day of live data before the larger volumes go on.
  3. Any dietary or allergen mismatch after launch switches that region back to picker substitution immediately, pending review. That includes any mismatch in the first week.
  4. Daily monitoring of acceptance (80% floor) and refunds (10% guardrail) per region against a small held-back control. Delegate the switch-on decision so it does not need another meeting.

Scotland

Keep Scotland on picker substitutions. Retest only after the catalogue re-map ships, on a small treatment share. It should show at least three consecutive days at 80% or better acceptance, and refunds within 10% of control, before it goes to full volume. Engineering has no date, so Scotland should not be in the Christmas plan and should be treated as upside.

Data notes

  • North West treatment has three duplicated rows (8, 9 and 10 October), matching the open export-job incident. I removed them. This restores the intended 50% split, and the North West rates barely move either way.
  • Incident logs are a floor on dietary failures, not a measure of them.
  • This is 28 days of data in a low-volume region (Scotland control is about 265 orders a day). Even so, the Scotland gap is far outside noise.

Check by check

Got wrong · 1
  • Gets the base of every number rightSome 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.
Mixed · 1
  • Avoids unsupported claimsIt asserts that zero logged dietary incidents in North West and Scotland are only because none were reported and that incidents come from customer complaints, which is not established by the supplied evidence.The two graders disagreed on this one.
Got right · 11
  • Uses the supplied evidence correctlyThe output's factual claims are drawn from the brief and supplied CSVs, with only minor rounding and one unsupported inference about incident reporting.
  • Addresses the actual decisionIt commits early to no-go on 2 November, conditional go from 4 November for four regions, hold Scotland, and states what would change the call.
  • Respects explicit constraintsIt provides a region-by-region recommendation with a launch-criteria table and keeps prose under 900 words.
  • Identifies material uncertaintyIt names material unknowns such as filter deployment, Scotland re-map timing, duplicate handling, and incident under-reporting, and gives monitoring/rollback conditions.
  • Produces the required deliverableThe requested recommendation and table are present, usable for the launch meeting, and within the length limit.
  • Checks every criterion, region by regionIt checks all four launch criteria for each region with figures computed from the files, including the duplicate correction.
  • Finds the Scotland breachIt identifies Scotland's refund breach, ties it to the 15 October supplier change, notes the acceptance dip, and holds Scotland.
  • Explains the basket gapIt compares baskets within each region and explains the pooled gap as an artefact of the uneven regional split.
  • Catches the duplicate rowsIt finds the duplicated North West treatment rows, removes them, and says the result barely changes.
  • Makes a clear callIt gives one clear call per region with dates, conditions, and triggers for change.
  • Limits the downside of being wrongIt limits downside with staged switch-on, daily monitoring, immediate rollback on any dietary mismatch, and Scotland held out.

Grades and run details

Decision model 92 · LLM judge 12 of 14 checks
Decision model checks
  • passUses the supplied evidence correctly14%
  • passAddresses the actual decision99%
  • passRespects explicit constraints29%
  • passIdentifies material uncertainty90%
  • passAvoids unsupported claims23%
  • passProduces the required deliverable80%
  • passChecks every criterion, region by region55%
  • passFinds the Scotland breach100%
  • passExplains the basket gap99%
  • passCatches the duplicate rows100%
  • passMakes a clear call84%
  • passLimits the downside of being wrong72%
  • failGets the base of every number right36%
Run
Run
#1
API response time
2.3 min
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