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 AstraChatGPT

Usable with a quick edit10 of 13 checks passed

The output is fully reasoned, includes the required table and conditions, and could be used with a quick edit to remove the one unsupported sentence.

Output

Recommendation for 30 October: no-go in every region on 2 November. Conditional phased rollout thereafter in London, South East, Midlands and North West. Scotland remains blocked pending catalogue repair and fresh evidence.

The dietary filter is scheduled for 4 November, two days after the proposed launch. Three treatment substitutions breached dietary requirements, including gluten-free bread replaced with standard bread and nut-free bars replaced with hazelnut bars. The shared system needs this control before expansion, including in regions with no recorded mismatch.

I would pause existing auto-substitution exposure and return to picker selection until the filter is deployed and verified.

Regional checks, 1–28 October

I removed three exact duplicate North West treatment rows dated 8–10 October4, consistent with the export incident, leaving 280 unique region–arm–day records. Acceptance and refund rates use summed numerators and denominators. Basket values are weighted by orders.

T = treatment; C = control. Refund changes are relative changes in requests per 1,000 orders.

RegionAcceptance T ≥80%Refunds/1,000: T vs C; change ≤+10%Dietary mismatches: zeroBasket T vs C; difference ≥£0Decision for 2 Nov
London86.68%: pass17.44 vs 18.04; −3.33%: pass1: fail£72.70 vs £72.49; +£0.21: passNo-go
South East86.40%: pass17.54 vs 17.89; −1.93%: pass1: fail£66.64 vs £66.20; +£0.44: passNo-go
Midlands86.35%: pass17.48 vs 17.89; −2.25%: pass1: fail£55.66 vs £55.35; +£0.31: passNo-go
North West86.28%: pass17.58 vs 18.47; −4.80%: pass0 recorded: observed pass£57.51 vs £57.29; +£0.22: passNo-go: shared safety gap
Scotland82.76%: pass over full test230.16 vs 18.20; +65.70%: fail0 recorded: observed pass£52.98 vs £52.62; +£0.36: passNo-go: safety and catalogue

Zero recorded incidents does not establish that an unprotected region is safe. Refunding a customer closes the complaint, not the underlying failure.

The aggregate claims conceal different problems.

Commercial’s acceptance claim is broadly correct: pooled acceptance rose from 71.20% to 85.93%, a 14.74 percentage-point improvement. Pooled refunds increased 7.86%, inside the overall guardrail. But the agreed criterion applies to every region. Scotland fails substantially.

Analytics’ £4 concern is also numerically correct but misleading as a treatment comparison: pooled baskets are £61.09 in treatment versus £65.15 in control. Treatment contains proportionally fewer high-value London orders and more lower-value Midlands and Scotland orders because allocation varies by region. Within every region, treatment baskets are higher across the full test. Applying the same combined regional order mix to both arms gives £63.26 versus £62.96, approximately £0.30 higher in treatment. This resolves the composition distortion; it does not establish a statistically reliable basket uplift.

Scotland is not settling down.

After the supplier switch on 15 October, treatment acceptance fell from 86.34% to 79.08%, below the launch threshold. Refunds rose from 17.25 to 43.25 per 1,000 orders, versus 17.79 in concurrent control, approximately 143% higher.

The final seven days still show 79.17% acceptance and 43.64 refunds per 1,000. The full-period acceptance pass masks a persistent failure in the current catalogue. The open pack-size mapping incident provides a plausible mechanism, and the repair has no date.

Conditions for reconsideration

  • Engineering and QA, by 4 November: deploy and verify the dietary filter across all six protected attributes. Replay the three incident cases, test missing or conflicting attributes and confirm unsafe or unverified candidates cannot be automatically selected. A merged change is insufficient.
  • PM, Analytics and regional Ops, review on 6 November:3 assess a limited post-fix pilot in London, South East, Midlands and North West, retaining randomised controls. Recheck every regional criterion, audit dietary compliance and verify London’s pricing fix. Expand only where the evidence supports it; insufficient evidence means continued limited exposure.1
  • Scotland Ops and catalogue Engineering, owner and repair date agreed on 30 October: complete and validate the re-map before restarting a controlled pilot. Judge readiness on post-repair results, not the earlier healthy fortnight.
  • During any restart: immediately disable auto-substitutions for a dietary mismatch and pause affected regions for guardrail breaches, with daily regional monitoring.

The 9 November booking window is a commercial deadline. It supplies no evidence that either unresolved defect will be fixed in time.

What a PM had to fix

  1. 1Decision deferredMake the callTargeted repair

    What we’d changeGive Commercial something to plan around: say whether the four clean regions can go by 9 November, set a threshold for pausing a region, and give Scotland a Christmas fallback.

  2. 2Contradiction missedSurface the contradictionQuick edit

    What we’d changeShow Scotland failing acceptance since the supplier switch in the table, as the text does, rather than passing.

  3. 3Test or gate too weakTighten the testQuick edit

    What we’d changeA 6 November review would have only a day or two of post-filter data. Allow enough to judge.

  4. 4OtherFix itQuick edit

    What we’d changeSay whether removing them changes any result. The brief asks for it.

Check by check

Got wrong · 1
  • Catches the duplicate rowsFinds and removes the duplicate North West rows but does not explicitly state whether they change the overall assessment.
Mixed · 2
  • Avoids unsupported claimsPresents 'Refunding a customer closes the complaint, not the underlying failure' as fact without support from the supplied context.The two graders disagreed on this one.
  • Gets the base of every number rightAll derived percentages and rates use correct numerators and denominators, and basket values are weighted by orders.The two graders disagreed on this one.
Got right · 10
  • Uses the supplied evidence correctlyAll factual statements about the current situation are supported by the brief or correct arithmetic; no invented facts.
  • Addresses the actual decisionCommits to a clear no-go on 2 Nov, then conditional rollout by region, and states what would change the call.
  • Respects explicit constraintsRespects the requested form, reader, word limit, and uses the data rather than stakeholder summaries.
  • Identifies material uncertaintyNames the unknowns (filter delivery, Scotland catalogue repair, post-fix evidence) and says what would change the call.
  • Produces the required deliverableA complete recommendation with a criteria table, under 900 words, usable for the launch meeting.
  • Checks every criterion, region by regionTable checks every criterion for each region using figures computed from the attached files.
  • Finds the Scotland breachIdentifies the Scotland refund breach, ties it to the 15 October supplier change, notes acceptance dip, and blocks Scotland.
  • Explains the basket gapShows treatment baskets are higher within every region and explains the pooled gap as a mix artefact.
  • Makes a clear callOne clear call per region, with conditions and dates, and says what would change it.
  • Limits the downside of being wrongProposes daily regional monitoring, a trigger (dietary mismatch or guardrail breach) and action (disable, pause).

Claims the judge couldn’t find in the brief

  • Refunding a customer closes the complaint, not the underlying failure.

Grades and run details

Decision model 85 · LLM judge 12 of 14 checks
Decision model checks
  • passUses the supplied evidence correctly18%
  • passAddresses the actual decision96%
  • passRespects explicit constraints29%
  • passIdentifies material uncertainty87%
  • passAvoids unsupported claims51%
  • passProduces the required deliverable80%
  • passChecks every criterion, region by region61%
  • passFinds the Scotland breach100%
  • passExplains the basket gap99%
  • partialCatches the duplicate rows31%
  • passMakes a clear call75%
  • partialLimits the downside of being wrong61%
  • failGets the base of every number right28%
Run
Run
#1
Time to output
71 s
Submitted
25 Sept 2026
Gemini 3.5 Flash-LiteGemini

Needs more than a quick edit6 of 13 checks passed

It fails to address the known duplicate data rows, doesn't specify monitoring triggers or rollback criteria, and makes a causal claim about Scotland without hedging, so a PM would need to add those 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.

Check by check

Got wrong · 5
  • Uses the supplied evidence correctlyThe output calculates rates from the raw daily_metrics.csv without acknowledging or removing the duplicate North West treatment rows flagged in incidents.csv, so the claimed rates for North West (and potentially others if the duplication affects summaries) are not fully reliable.
  • Identifies material uncertaintyDoes not explicitly name the unknowns that could change the call (e.g., what if the dietary filter slips past 9 November, or if Scotland’s refunds don’t recover after the catalogue fix) or state the thresholds that would reverse the decision.
  • Avoids unsupported claimsStates as fact that Scotland’s refund spike was 'driven by the dairy supplier switch where pack sizes were mismapped' without labelling it as a hypothesis, even though the data only shows a correlation.
  • Catches the duplicate rowsDoes not mention or handle the duplicate North West treatment rows on 8–10 October that the incident log flags, so the quality check is absent.
  • Limits the downside of being wrongWhile it mentions a 7‑day post‑fix stability review, it does not specify a signal (e.g., refund rate threshold) or a rollback trigger, and does not flag irreversible actions.
Mixed · 2
  • Explains the basket gapShows treatment basket values are equal or higher in every region and states the aggregated £4 drop concern is debunked, satisfying the Simpson’s paradox explanation.The two graders disagreed on this one.
  • Gets the base of every number rightRates are computed as sums over the period (acceptance = total accepted / total offered, refund rate = total refunds / total orders), so denominators are consistent and the relative comparisons are valid.The two graders disagreed on this one.
Got right · 6
  • Addresses the actual decisionCommits to a clear No‑Go for 2 November, then a conditional Go by region for 9 November after the filter deploys, and states Scotland’s hold conditions.
  • Respects explicit constraintsProse is well under 900 words, addressed to the launch meeting, and includes a region‑by‑region table with the four criteria.
  • Produces the required deliverableProvides a complete, table‑based recommendation with a region‑by‑region call, within the word limit, that the launch meeting could act on.
  • Checks every criterion, region by regionThe table checks acceptance, refunds vs control, dietary incidents and basket value for each of the five regions, with figures drawn from the test data.
  • Finds the Scotland breachIdentifies Scotland’s refund breach (+117.9%), links it to the post‑15 October supplier change, notes the acceptance drop to 77%, and holds Scotland.
  • Makes a clear callGives a clear decision per region (No‑Go for Scotland, conditional Go for others from 9 Nov) with dates and the conditions required to proceed.

Grades and run details

Decision model 46 · LLM judge 8 of 14 checks
Decision model checks
  • failUses the supplied evidence correctly32%
  • passAddresses the actual decision35%
  • passRespects explicit constraints9%
  • partialIdentifies material uncertainty65%
  • partialAvoids unsupported claims12%
  • passProduces the required deliverable40%
  • partialChecks every criterion, region by region63%
  • passFinds the Scotland breach97%
  • failExplains the basket gap58%
  • failCatches the duplicate rows100%
  • partialMakes a clear call80%
  • failLimits the downside of being wrong43%
  • failGets the base of every number right75%
Run
Run
#1
Time to output
46 s
Submitted
25 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