Tasks / Product Craft

Make the launch call

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

Task type v1.2 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

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

From 16 graded outputs by 8 models. 69% were usable with at most a quick edit.

Reliably right

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

Where it slips

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

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

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).
Karri SaarinenLenny’s Podcast
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.
Todd JacksonLenny’s Podcast

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · Gets the base of every number right · and 5 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then compare up to three outputs, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

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

What the model was given6 items: The test, Launch criteria (agreed in the PRD), What people have said, Engineering, daily_metrics.csv, incidents.csv

The test1–28 October, five regions. In each region orders were split between control (the picker chooses substitutes, as today) and treatment (AI auto-substitutions). The split was not 50/50 everywhere: treatment got 30% of orders in London, 50% in the South East and North West, and 70% in the Midlands and Scotland.

Launch criteria (agreed in the PRD)1. Substitution acceptance (subs_accepted ÷ subs_offered) of at least 80% in treatment, in every region. 2. Refund requests per 1,000 orders in treatment no more than 10% above control, in every region. 3. Zero dietary or allergen mismatches (a substitute that breaks a gluten-free, vegan, vegetarian, nut-free, halal or kosher attribute). 4. Average basket value not lower in treatment than control.

What people have saidHead of Commercial: “Acceptance is up 15 points and refunds are within the guardrail overall. Every day we wait costs us Christmas.” Head of Analytics: “Treatment baskets are £4 smaller across the test. That worries me.” Ops director (Scotland): “The supplier change caused some noise, but it's settling down.”

EngineeringThe dietary-attribute hard filter (never substitute across those attributes) is merged and scheduled to deploy on 4 November after regression tests. Scotland's catalogue re-map for the new dairy supplier is in progress with no date. Rollout can be switched on region by region.

daily_metrics.csv283 rows · Download

date,region,arm,orders,items_ordered,items_out_of_stock,subs_offered,subs_accepted,refund_requests,refund_value_gbp,complaints,avg_basket_gbp
2026-10-01,London,control,1757,52273,2351,1953,1387,32,160.71,6,72.89
2026-10-01,London,treatment,692,20455,871,820,708,12,62.05,2,72.42
2026-10-01,South East,control,853,22846,996,833,593,16,74.49,3,65.62
2026-10-01,South East,treatment,928,25223,1166,1106,952,15,75.93,3,66.59
2026-10-01,Midlands,control,467,10468,465,383,268,8,34.57,1,54.92
2026-10-01,Midlands,treatment,1113,25136,1166,1087,947,21,106.35,3,55.33
…

incidents.csv13 rows · Download

date,region,arm,type,severity,summary,status
2026-10-02,London,treatment,wrong_size,low,"4-pint milk substituted with 1-pint, customer accepted at door",closed
2026-10-04,Midlands,treatment,dietary_mismatch,high,"Gluten-free sliced loaf substituted with standard white loaf; customer coeliac, noticed at home",closed: refund and apology
2026-10-06,South East,control,wrong_item,low,"Picker substituted oat milk with soya milk; customer rejected at door",closed
2026-10-07,North West,treatment,data_export,low,"Daily export job re-ran after a timeout on 8, 9 and 10 Oct; analytics team flagged possible duplicate rows",open
2026-10-09,London,treatment,price,medium,"Substitute priced higher than original; customer charged the difference against policy",closed: policy fix deployed 11 Oct
2026-10-12,Scotland,control,late_delivery,low,"Van breakdown, 14 orders late",closed
…

What a strong answer doesThe answer key the graders mark against

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

Critical failures (cap the score at 40)

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

Task

Staff-level · v1.12

The outputs

8 setups answered this brief.

Sonnet 5.5API95Usable with a quick edit GPT-6.1 SolAPI93Usable with a quick edit GPT-6 AstraChatGPT90Usable with a quick edit Haiku 5.5API90Usable with a quick edit Opus 5.5Claude88Usable with a quick edit GPT-6 LunaAPI78Usable with a quick edit Gemini 3.5 Flash-LiteGemini52Needs more work Gemini 3.8 FlashAPI40Critical failure

Haiku 5.5API

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)

Region	1. Acceptance (≥80%)	2. Refunds per 1,000 (≤10% above control)	3. Dietary breaks (zero)	4. Basket (not lower)	Call
London	86.7% 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 East	86.4% vs 71.0% ✔	17.5 vs 17.9 (−2%) ✔	1 high: vegan → dairy cheddar, 19 Oct ✘	£66.66 vs £66.20 ✔	No-go
Midlands	86.3% 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 West	86.3% vs 71.2% ✔	17.6 vs 18.5 (−5%) ✔	0 logged ✔	£57.52 vs £57.28 ✔	Go with conditions
Scotland	82.8% 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

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

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

Grades and run details

Decision model 79 · LLM judge 100

Decision model checks

failUses the supplied evidence correctly25%
passAddresses the actual decision72%
passRespects explicit constraints23%
passIdentifies material uncertainty85%
partialAvoids unsupported claims38%
passProduces the required deliverable61%
passChecks every criterion, region by region34%
passFinds the Scotland breach100%
passExplains the basket gap70%
passCatches the duplicate rows100%
passMakes a clear call85%
partialLimits the downside of being wrong95%
failGets the base of every number right48%
passJudges the launch against a baseline set now92%

Run

Run: #1
API response time: 1.9 min
Submitted: 8 Oct 2026

GPT-6 AstraChatGPT

Usable with a quick edit12 of 14 checks passed

The output is well-structured, catches duplicates, explains the mix artefact, and provides a clear call. A PM could use it with minor edits, such as adding a note that the duplicates do not change the result.

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.

Region	Acceptance T ≥80%	Refunds/1,000: T vs C; change ≤+10%	Dietary mismatches: zero	Basket T vs C; difference ≥£0	Decision for 2 Nov
London	86.68%: pass	17.44 vs 18.04; −3.33%: pass	1: fail	£72.70 vs £72.49; +£0.21: pass	No-go
South East	86.40%: pass	17.54 vs 17.89; −1.93%: pass	1: fail	£66.64 vs £66.20; +£0.44: pass	No-go
Midlands	86.35%: pass	17.48 vs 17.89; −2.25%: pass	1: fail	£55.66 vs £55.35; +£0.31: pass	No-go
North West	86.28%: pass	17.58 vs 18.47; −4.80%: pass	0 recorded: observed pass	£57.51 vs £57.29; +£0.22: pass	No-go: shared safety gap
Scotland	82.76%: pass over full test²	30.16 vs 18.20; +65.70%: fail	0 recorded: observed pass	£52.98 vs £52.62; +£0.36: pass	No-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

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.
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.
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.
4OtherFix itQuick edit
What we’d changeSay whether removing them changes any result. The brief asks for it.

Grades and run details

Decision model 86 · LLM judge 93

Decision model checks

passUses the supplied evidence correctly16%
passAddresses the actual decision93%
passRespects explicit constraints30%
passIdentifies material uncertainty87%
passAvoids unsupported claims28%
passProduces the required deliverable82%
passChecks every criterion, region by region67%
passFinds the Scotland breach100%
passExplains the basket gap98%
partialCatches the duplicate rows50%
passMakes a clear call68%
partialLimits the downside of being wrong62%
failGets the base of every number right22%
passJudges the launch against a baseline set now96%

Run

Run: #1
Time to output: 71 s
Submitted: 25 Sept 2026

Gemini 3.5 Flash-LiteGemini

Needs more than a quick edit7 of 14 checks passed

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

Output

Launch Recommendation: AI Auto-Substitutions

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

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

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

---

Regional Evaluation Table (1–28 October Test Data)

Region	Subs Acceptance ($\ge 80\%$)	Treatment Refunds vs Control ($\le +10\%$)	Dietary/Allergen Mismatches (Target: 0)	Basket Value Impact	Recommendation
:---	:---	:---	:---	:---	:---
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 Incidents	Control: £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

Hold Rollout on 2 November.
Deploy Dietary Hard Filter on 4 November as scheduled, and verify regression tests.
Complete Scotland Catalogue Re-map and clear open wrong-size substitution incidents.
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

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.
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.
3Hypothesis stated as factReframe it as a hypothesisTargeted repair
What we’d changeZero recorded mismatches in North West isn't evidence of safety. It runs the same unfiltered model as the regions that had them.

Grades and run details

Decision model 46 · LLM judge 60

Decision model checks

failUses the supplied evidence correctly50%
passAddresses the actual decision35%
passRespects explicit constraints11%
partialIdentifies material uncertainty64%
partialAvoids unsupported claims27%
passProduces the required deliverable27%
partialChecks every criterion, region by region66%
passFinds the Scotland breach96%
failExplains the basket gap52%
failCatches the duplicate rows99%
partialMakes a clear call82%
failLimits the downside of being wrong37%
failGets the base of every number right70%
partialJudges the launch against a baseline set now24%

Run

Run: #1
Time to output: 46 s
Submitted: 25 Sept 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 6

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 Astra · ChatGPT

All factual claims are taken directly from the brief or derived correctly from the supplied data.

Gemini 3.5 Flash-Lite · Gemini

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

Identifies material uncertaintyRightRightWrong

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 Astra · ChatGPT

Names the dietary filter deployment, Scotland catalogue repair, and post-fix pilot results as unknowns, with conditions for reconsideration.

Gemini 3.5 Flash-Lite · Gemini

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

Explains the basket gapRightRightWrong

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 Astra · ChatGPT

Shows treatment baskets are higher in every region and explains the pooled gap as a mix effect from the uneven split.

Gemini 3.5 Flash-Lite · Gemini

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

Catches the duplicate rowsRightWrongWrong

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 Astra · ChatGPT

Output removes the duplicate North West rows but does not state whether they change the results, as the criterion requires.

Gemini 3.5 Flash-Lite · Gemini

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

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 Astra · ChatGPT

Specifies post-launch monitoring (daily regional), triggers (dietary mismatch, guardrail breach), and action (disable, pause).

Gemini 3.5 Flash-Lite · Gemini

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

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 Astra · ChatGPT

Names a review point (6 Nov), the criteria to recheck, and what each outcome leads to (expand or keep limited).

Gemini 3.5 Flash-Lite · Gemini

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

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 Astra · ChatGPT

All derived percentages, rates, and basket values check out against the supplied data with correct denominators.

Gemini 3.5 Flash-Lite · Gemini

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

All got right 7

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 Astra · ChatGPT

Commits to a clear no-go on 2 Nov, conditional phased rollout thereafter, and states what would change the call.

Gemini 3.5 Flash-Lite · Gemini

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

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 Astra · ChatGPT

Prose is under 900 words, includes the required region-by-region criteria table, and addresses the launch meeting.

Gemini 3.5 Flash-Lite · Gemini

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

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 Astra · ChatGPT

Interpretations like the mix effect and Scotland's mechanism are labelled as such; no confident claims go beyond the evidence.

Gemini 3.5 Flash-Lite · Gemini

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

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 Astra · ChatGPT

The recommendation is complete, in the right form for a senior PM, and could be acted on with light edits.

Gemini 3.5 Flash-Lite · Gemini

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

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 Astra · ChatGPT

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

Gemini 3.5 Flash-Lite · Gemini

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

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 Astra · ChatGPT

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

Gemini 3.5 Flash-Lite · Gemini

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

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 Astra · ChatGPT

Makes one clear call per region with dates and conditions, and says what would change it.

Gemini 3.5 Flash-Lite · Gemini

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

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated. Where our PM checked the graders by hand, they agreed on 63% of 68 checks.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	Sonnet 5.5withAPI	87.8	100.0	2	None
2	Haiku 5.5withAPI	82.5	100.0	2	None
3	GPT-6.1 SolwithAPI	86.0	95.8	2	None
4	Opus 5.5withClaude	84.3	96.7	2	None
5	GPT-6 AstrawithChatGPT	83.8	92.5	2	None
6	GPT-6 LunawithAPI	74.8	80.8	2	None
7	Gemini 3.8 FlashwithAPI	64.9	67.5	2	1 capped
8	Gemini 3.5 Flash-LitewithGemini	48.2	59.2	2	None

What you get for the cost

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

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
Setup	Task score	Relative cost (cheapest = 1)
GPT-6 Luna · API	77	1.0×
Gemini 3.5 Flash-Lite · Gemini	52	2.7× (partly estimated)
Haiku 5.5 · API	92	2.8×
Gemini 3.8 Flash · API	55	11×
GPT-6.1 Sol · API	91	12×
Sonnet 5.5 · API	94	37×
GPT-6 Astra · ChatGPT	89	46× (partly estimated)
Opus 5.5 · Claude	91	59× (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

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

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

Reliably right

Where it slips

How it’s graded

Limits the downside of being wrong

Judges the launch against a baseline set now

Plus our standard checks

The tasks

The outputs

Output

Launch recommendation: AI auto-substitutions, 2 November

Data notes

Criteria check (treatment vs control)

Region by region

Asks for the meeting

Grades and run details

Decision model checks

Run

Output

What a PM had to fix

Grades and run details

Decision model checks

Run

Output

Launch Recommendation: AI Auto-Substitutions

Regional Evaluation Table (1–28 October Test Data)

Region-by-Region Breakdown

Action Plan

What a PM had to fix

Grades and run details

Decision model checks

Run

Check by check

Where they differ 6

All mixed 1

All got right 7

Results

What you get for the cost

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured