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

Experiment specification

Can the model design a test that could actually change the decision?

Measures the modelTask v1.0 · 2 casesDifficulty

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

From 9 graded outputs by 5 models. 56% were usable with at most a quick edit.

Reliably right

  1. A realistic plan that beats the freeze100% pass
    It converts 740 windows to about 9 days implicitly, explains that correlation and weekly cycles require a longer test (20 days, ~3 weeks), and provides dates that fit before 6 November with Porto notice given on 30 September.
    GPT-6.1 Sol · API · Batching deliveries before peak season
  2. Addresses the actual decision94% pass
    The output commits to a clear decision rule: launch only if all gates pass, otherwise do not launch, and an inconclusive result is not a pass.
    GPT-6.1 Sol · API · Batching deliveries before peak season
  3. Identifies material uncertainty92% pass
    It identifies unknowns like power under correlation, carryover, and city differences, and states that if power is insufficient the rollout will not proceed, resolving the uncertainty.
    GPT-6.1 Sol · API · Batching deliveries before peak season

Where it slips

  1. An unambiguous primary metric53% pass
    The spec names courier cost per order as the primary metric with a rationale, but it does not include a planned trust check such as a sample ratio check or verification that the arms receive the scheduled share of windows and similar order volumes.
    Sonnet 5.5 · API · Batching deliveries before peak season
  2. Guardrails with thresholds58% pass
    Guardrails are named but lack specific numeric thresholds; 'materially worse' and 'drops meaningfully' are too vague to block rollout unambiguously.
    Sonnet 5.5 · API · Showing the delivery fee up front
  3. Sized from the real traffic58% pass
    Sample size is correct, but the test stops when the enrolment target is reached rather than running fixed whole weeks.
    GPT-6.1 Sol · API · Showing the delivery fee up front

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 PM for checkout at Basketful. We want to test showing the delivery fee in the basket, instead of only at the last step of checkout. Write the experiment spec for Ravi, the growth engineer who'll build it, Ines, the analyst who'll read it out, and Chloe, our Head of Growth, who approves it. Keep it under 700 words. The team's draft plan is below. Fix what needs fixing.

About BasketfulAn online grocery service. Delivery costs £3.99, or is free on orders over £60. Today the fee first appears on the final checkout step.
Why we're testing it12% of support contacts last quarter were about delivery fees the customer hadn't expected. Design believes showing the fee early will build trust; Chloe worries it will put people off before they start checkout.
Traffic and baselinesAbout 40,000 different customers view their basket each week. About half of each week's basket viewers are new that week; the rest came back from earlier weeks. 8% of basket viewers place an order. Of customers who start checkout, 62% complete it. Average order value: £47.
Smallest change worth acting onChloe: “Anything smaller than half a percentage point on orders isn't worth arguing about.”
The team's draft planPrimary metric: checkout completion rate (orders ÷ checkout starts). Split: 50/50 by session. Duration: we'll check the dashboard every day and stop as soon as it's significant. Chloe's addition: “While we're at it, let's put the free-delivery-over-£60 banner in the variant too.”
What a strong answer does

A spec that tests one change and fixes the draft's three problems. The primary metric is orders per basket-viewing customer, not checkout completion rate: showing the fee earlier changes who starts checkout, so completion can rise while orders fall. Customers, not sessions, are randomised, so returning customers see one version. The free-delivery banner stays out of the variant (or gets its own test), or the result can't be attributed. The duration is fixed in advance, with no daily stopping: detecting 0.5pp on an 8% base needs roughly 47,000 customers per arm (about 95,000 in total), and because half of each week's viewers are returning customers, new customers accumulate at about 40,000, 60,000, 80,000 and 100,000 after four weeks, so it runs about four full weeks, not two and a half. Guardrails with thresholds: average order value, fee-related support contacts, and the checkout-start rate as a diagnostic. A sample ratio check before reading results. A decision rule written up front: ship if orders are no worse than 0.5pp down and fee contacts fall; don't ship if orders drop by 0.5pp or more; with a stated plan for an inconclusive result.

Critical failures (cap the score)
  • Uses checkout completion rate (orders ÷ checkout starts) as the primary metric
  • Bundles the free-delivery banner into the variant
Case

v1.0 · synthetic · B2C, ecommerce, pricing, metric choice

Sonnet 5.5APINeeds more workGPT-6 LunaAPIUsable with a quick editGPT-6.1 SolAPIUsable with a quick editOpus 5.5ClaudeNeeds more workGemini 3.5 Flash-LiteGeminiNeeds more work
GPT-6 LunaAPI

Usable with a quick edit9 of 12 checks passed

The core reasoning and evidence are correct; a quick edit to add a sample ratio check, guardrail thresholds and a one-line explanation for no peeking would make it fully ready.

Output

Basket delivery-fee experiment

For: Ravi (build), Ines (analysis), Chloe (approval) Decision requested: Approve a test of showing the existing delivery fee in the basket.

Hypothesis and scope

Showing the delivery cost before checkout will reduce surprise and fee-related support contacts, without materially reducing orders.

  • Control: Current experience; delivery fee first appears on the final checkout step.
  • Variant: Show the applicable fee in the basket: “Delivery £3.99” below the free-delivery threshold, or “Free delivery” when the basket qualifies. Keep the existing “free on orders over £60” rule and wording.
  • Change nothing else. Do not add the free-delivery banner: that would test a second intervention and make the result hard to interpret.

Build and assignment — Ravi

Randomise 50/50 by customer, not session, and keep each customer in the same arm across visits and devices where identity is available. Stratify assignment by new versus returning basket viewers. Exclude staff and test accounts. Log assignment, basket value/fee shown, basket views, checkout starts, orders, order value, and relevant support contacts.

Measurement — Ines

Primary metric: Basket-viewer conversion: percentage of assigned customers who place at least one order within 7 days of their first eligible basket view. Count each customer once, in their assigned arm (intention-to-treat). This captures any deterrence before checkout; checkout completion alone would miss it.

Guardrails / secondary measures: Checkout starts per basket viewer; checkout completion (orders ÷ checkout starts); average order value and revenue per assigned customer; delivery-fee-related support contacts per assigned customer. Support contacts may be too sparse for a definitive test, so treat them as directional, not a substitute for the primary outcome.

Sample, duration, and analysis

Baseline basket-viewer conversion is 8%. Power the test to detect a 0.5 percentage-point absolute difference (Chloe’s smallest worthwhile order effect), with 80% power and a two-sided 5% significance level. This requires approximately 95,000 unique customers total (about 47,500 per arm), assuming independent customer-level outcomes.

There are about 40,000 basket viewers weekly, but many return across weeks. Run for at least four full weeks and until the sample target is reached; then allow 7 days for outcomes to mature before reading results. If the target is not reached, extend in full weeks. Ines should confirm the final sample-size calculation and report actual enrolment.

Do not stop early for significance or check daily results to make a stopping decision. Monitor only for implementation or customer-harm issues. Analyse the primary metric at the customer level, with a 95% confidence interval and the pre-specified two-sided test.

Decision

Recommend rollout if the variant improves basket-viewer conversion by at least 0.5 percentage points, the result is statistically significant, and guardrails show no material harm. Do not roll out if there is significant harm. If results are inconclusive, report that clearly; do not interpret “not significant” as proof of no effect.

Check by check

Got wrong · 2
  • An unambiguous primary metricIt names one primary metric with a rationale but omits a planned trust check such as a sample ratio check.
  • Guardrails with thresholdsGuardrail metrics are listed but no specific thresholds (e.g., maximum acceptable drop in AOV) are given to block rollout.
Mixed · 1
  • Fixes the duration up frontIt sets a fixed duration but does not explain why daily peeking inflates false positives, only instructs not to do it.The two graders disagreed on this one.
Got right · 9
  • Uses the supplied evidence correctlyEvery statement about the current situation is taken directly from the supplied context or follows by arithmetic.
  • Addresses the actual decisionThe output clearly requests approval of the test and provides a complete spec for Chloe to decide, with a post-test decision rule.
  • Respects explicit constraintsThe output fixes all three problems in the draft, stays under 700 words, and is addressed to the three named readers.
  • Identifies material uncertaintyIt notes support contacts may be too sparse for a definitive test and that inconclusive results are possible, with a plan for each.
  • Avoids unsupported claimsThe hypothesis is clearly labelled, and no interpretations are presented as established fact.
  • Produces the required deliverableThe spec is complete, under 700 words, and directly usable by Ravi, Ines and Chloe.
  • Tests one change at a timeIt explicitly keeps the free-delivery banner out of the variant and explains that adding it would make the result uninterpretable.
  • Decision rule written before the testIt maps rollout, no rollout and inconclusive results to actions, with thresholds of 0.5pp improvement and statistical significance.
  • Sized from the real trafficSample size is calculated from the 8% baseline and 0.5pp effect, and the four-week duration accounts for returning visitors and whole-week cycles.

Grades and run details

Decision model 79 · LLM judge 10 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly12%
  • passAddresses the actual decision62%
  • partialRespects explicit constraints28%
  • passIdentifies material uncertainty35%
  • passAvoids unsupported claims66%
  • passProduces the required deliverable62%
  • passTests one change at a time95%
  • passFixes the duration up front56%
  • partialAn unambiguous primary metric83%
  • partialDecision rule written before the test28%
  • partialSized from the real traffic24%
  • partialGuardrails with thresholds98%
Run
Run
#1
API response time
22 s
Submitted
30 Sept 2026
Gemini 3.5 Flash-LiteGemini
Critical failure: Bundles the free-delivery banner into the variant. Score capped at 40.

Needs more than a quick edit1 of 12 checks passed

The spec bundles the banner, uses an inadequate duration, and lacks a decision rule and guardrail thresholds, so a PM would need to redo core parts.

Output

To: Chloe (Head of Growth), Ravi (Growth Engineer), Ines (Data Analyst) From: PM, Checkout Subject: Experiment Spec: Early Delivery Fee Display in Basket

Team, here is the polished spec for our upcoming checkout experiment. I’ve incorporated Chloe’s threshold, fixed a few methodological traps in the draft, and scoped this for a clean, reliable read.

---

1. Objective & Hypothesis

  • Current State: Delivery fee (£3.99, free over £60) is only revealed on the final checkout step. 12% of last quarter’s support contacts were fee-related surprises.
  • Hypothesis: Showing the delivery fee (and progress to free delivery) directly in the basket increases customer trust and sets clear expectations upfront.
  • Expected Impact: While Chloe rightly worries this might cause some upfront drop-off among sub-£60 baskets, we believe the resulting higher-intent checkout starts will lift overall conversion and reduce cart abandonment due to "sticker shock."

---

2. Variant Definition

  • Control (A): Existing basket experience. Delivery fee hidden until final checkout step.
  • Variant (B):
  • Displays the delivery fee clearly in the basket summary.
  • Note on scope: Per Chloe’s suggestion, we are bundling the "free delivery over £60" progress banner into Variant B. Since the fee and the threshold are conceptually tied, showing one without the context of the other would skew the test.

---

3. Traffic Allocation & Unit of Randomization

  • Unit of Randomization: User-level (logged-in state or persistent cookie). Correction from draft: Session-level splitting would cause a jarring experience if a user refreshes or returns and sees fluctuating fee visibility.
  • Split: 50/50 (Control / Variant).
  • Audience: All customers viewing the basket (~40,000 unique users/week, split roughly 50/50 between new and returning).

---

4. Metrics

  • Primary Metric (Guardrail for Business Value):
  • Order Conversion Rate: Completed Orders ÷ Total Basket Views.
  • (Correction from draft: The draft proposed "Checkout Completion Rate (Orders ÷ Checkout Starts)." That misses top-of-funnel drop-off. If Variant B scares people away before checkout, measuring only checkout-starts will falsely make the variant look better. We must measure from the basket view).
  • Secondary Metrics:
  • Checkout Start Rate: Checkout Starts ÷ Basket Views.
  • Checkout Completion Rate: Orders ÷ Checkout Starts (to isolate downstream behavior).
  • Average Order Value (AOV): To monitor if the £60 threshold banner successfully drives basket-building behavior (baseline: £47).
  • Support Metric:
  • Volume of delivery-fee-related support tickets (Ines, let’s pull this baseline post-launch).

---

5. Success Criteria & Statistical Guardrails

  • Minimum Detectable Effect (MDE): Per Chloe, we care about a ≥ +0.5 percentage point absolute change in the Primary Metric (Order Conversion Rate from basket view, baseline ~8%).
  • Duration & Peeking:
  • Correction from draft: Do not stop the test the moment p < 0.05. Early peeking yields false positives.
  • We will run the test for a fixed duration of 2 full weeks to capture day-of-week and new vs. returning user cycles (~80,000 total unique users), yielding adequate statistical power for our MDE.

---

Next Steps:

  • Ravi: Let’s sync on tracking implementation for the basket-level fee and banner. Target build completion: [Insert Date].
  • Ines: Please set up the tracking dashboard, ensuring we segment by New vs. Returning users and Basket Value (<£60 vs. >£60).
  • Chloe: Good to approve?

Check by check

Got wrong · 10
  • Uses the supplied evidence correctlyThe claim that 2 weeks yields ~80,000 unique users and adequate power is not supported by the brief; the brief's traffic and returning-user dynamics do not support that sample size or power.
  • Respects explicit constraintsThe brief says 'Fix what needs fixing' and the draft bundles the free-delivery banner; the output keeps it bundled, failing to fix that problem.
  • Identifies material uncertaintyThe output does not name unknowns that could change the decision, nor does it specify what would happen if the result is inconclusive.
  • Avoids unsupported claimsThe output presents the 2-week duration as yielding adequate power as fact, without labelling it as an assumption, and the evidence does not support it.
  • Produces the required deliverableThe spec is missing a decision rule, guardrail thresholds, proper sample-size justification, and a sample ratio check, so it is not usable as is.
  • Tests one change at a timeThe spec bundles the free-delivery banner into the variant, arguing it is conceptually tied, instead of testing the fee change alone.
  • An unambiguous primary metricThe spec names one primary metric but does not include a trust check such as a sample ratio check.
  • Decision rule written before the testNo decision rule is stated; the spec does not map outcomes (including inconclusive) to actions with thresholds.
  • Sized from the real trafficThe 2-week duration is not worked out from the traffic and MDE; the required sample size is much larger given the 8% baseline and 0.5pp effect.
  • Guardrails with thresholdsGuardrail metrics are listed but no thresholds are set that would block a rollout.
Mixed · 1
  • Addresses the actual decisionThe output commits to a specific experiment design and asks Chloe for approval, which is the deliverable requested.The two graders disagreed on this one.
Got right · 1
  • Fixes the duration up frontThe spec replaces 'stop as soon as significant' with a fixed 2-week duration and explains that early peeking yields false positives.

Claims the judge couldn’t find in the brief

  • 2 full weeks yields ~80,000 total unique users and adequate statistical power for the MDE.

Grades and run details

Decision model 33 · LLM judge 2 of 13 checks
Decision model checks
  • failUses the supplied evidence correctly18%
  • failAddresses the actual decision66%
  • failRespects explicit constraints73%
  • partialIdentifies material uncertainty76%
  • partialAvoids unsupported claims29%
  • partialProduces the required deliverable73%
  • failTests one change at a time99%
  • partialFixes the duration up front81%
  • partialAn unambiguous primary metric84%
  • partialDecision rule written before the test44%
  • partialSized from the real traffic77%
  • partialGuardrails with thresholds44%
Run
Run
#1
Time to output
5 s
Submitted
30 Sept 2026
Opus 5.5Claude

Needs more than a quick edit8 of 12 checks passed

The spec exceeds the 700-word limit and lacks guardrail thresholds, so a PM would need to trim it and add those thresholds before use.

Output

Experiment Spec: Delivery Fee Shown in Basket

Owner: Checkout PM · Build: Ravi · Analysis: Ines · Approver: Chloe

Why we're testing this

Today the £3.99 delivery fee (free over £60) first appears on the final checkout step. Last quarter, 12% of support contacts were about unexpected delivery fees. Design expects that showing the fee earlier will build trust. Chloe's concern is that it will put people off before they start checkout. This test tells us which effect is bigger.

Hypothesis

Showing the delivery fee in the basket will change the share of basket viewers who place an order. We don't assume a direction. The test is two-sided because either outcome is plausible.

Variants

  • Control: Current experience. The fee appears only at the final checkout step.
  • Variant: The basket shows a "Delivery: £3.99" line, or "Free" when the basket is over £60. The same fee still appears at checkout.

Change from draft: The free-delivery-over-£60 banner is removed from this test. If both changes ship together, we can't tell which one caused any result. The banner could also raise orders while the fee line lowers them, so the two effects would cancel and we'd learn nothing. We'll test the banner as a follow-up once this result is in.

Primary metric

Order conversion: the share of customers who view their basket during the test and go on to place at least one order.

Change from draft: The draft used checkout completion (orders ÷ checkout starts). That metric would be misleading here. If the variant puts off fee-sensitive customers before checkout, the people who remain in checkout are the ones already willing to pay. Completion would then rise even if total orders fell. Order conversion counts everyone who saw the basket, so it catches losses at either stage.

Secondary and guardrail metrics

  • Checkout start rate and checkout completion rate. These are diagnostic only and show where any change happens.
  • Average order value. Seeing the fee may push some customers over £60.
  • Revenue per basket viewer. This includes delivery fee income.
  • Fee-related support contacts per 1,000 orders. This is the problem we set out to fix.

Randomisation

  • Unit: customer (logged-in account ID). The draft split by session, which needs to change. About half of each week's basket viewers are returning customers. With a session split, they would see the fee in the basket on one visit and not on the next. That would contaminate both groups and confuse customers.
  • Split: 50/50, sticky for the whole test.
  • Ravi: Please log the assignment when a customer first views their basket, and confirm that logged-out traffic is either excluded or bucketed consistently.

Sample size and duration

  • Assumptions: baseline 8% order conversion, two-sided test at α = 0.05, 80% power.
  • Minimum effect we want to detect: 0.5 percentage points (8.0% → 8.5%). We're reading Chloe's threshold as absolute points on order conversion. That is roughly a 6% relative change.
  • Customers needed: about 47,500 per arm, or about 95,000 total.

How long that takes: About 40,000 customers view their basket in week 1, then about 20,000 new customers each week after. That gives roughly 40k, 60k, 80k and 100k cumulative customers. The test therefore runs for 4 full weeks.

Caveat for Ines: The 8% baseline is per basket viewer per week. Over a 4-week window, each customer has more chances to order, so the per-customer baseline will be higher. At 12%, for example, we'd need about 135,000 customers, which means about 6 weeks. Before launch, Ines will calculate the 4-week per-customer baseline from historical data. The final duration will be fixed from that number and recorded here.

Stopping rule

Change from draft: We won't stop the test as soon as the dashboard shows significance. If we check every day and stop on the first significant result, the false-positive rate climbs well above 5%. The dashboard will often show early "wins" that aren't real.

  • The test runs for the pre-set duration, in full weeks only.
  • The dashboard can be watched for bugs and sample-ratio mismatch. Nobody makes ship decisions from it mid-test.
  • Only reason to stop early: a guardrail failure. That means a broken experience, a sample-ratio mismatch (p < 0.001), or order conversion down by more than 1.5 points.

Decision rules

The confidence interval (CI) below is the interval around the difference in order conversion between the variant and control.

Result on order conversionDecision
Up by a significant amountShip the variant
Down by a significant amountDon't ship. Look into other ways to reduce the support contacts
Not significant, and the CI excludes −0.5 pointsShip. We lose no meaningful number of orders and gain trust and fewer support contacts
Not significant, and the CI includes −0.5 pointsTreat as inconclusive. Chloe decides whether to extend the test or drop the change

Sign-off

  • ☐ Ravi: build plus assignment logging
  • ☐ Ines: baseline check and final duration
  • ☐ Chloe: approval

Check by check

Got wrong · 1
  • Guardrails with thresholdsGuardrail metrics are listed but no thresholds are given for average order value, revenue, or support contacts to block rollout; only order conversion has a stop threshold.
Mixed · 3
  • Uses the supplied evidence correctlyAll claims about the current situation are directly from the brief or derived by correct arithmetic.The two graders disagreed on this one.
  • Respects explicit constraintsThe output is approximately 780 words, exceeding the 'under 700 words' limit.The two graders disagreed on this one.
  • Produces the required deliverableThe spec is over the 700-word limit, so it does not fully meet the requested form.The two graders disagreed on this one.
Got right · 8
  • Addresses the actual decisionThe spec commits to a clear decision framework with rules for shipping, not shipping, and inconclusive results, framed for Chloe.
  • Identifies material uncertaintyIt identifies the uncertain per-customer baseline over 4 weeks and the inconclusive outcome, with a plan to resolve the baseline.
  • Avoids unsupported claimsHypotheses and forecasts are clearly labelled, and no confident claims go beyond the supplied evidence.
  • Tests one change at a timeThe free-delivery banner is removed from the variant and the spec explains why bundling would make results uninterpretable.
  • Fixes the duration up frontIt sets a fixed 4-week duration and explains that daily peeking inflates false positives.
  • An unambiguous primary metricOrder conversion is the single primary metric with a clear rationale, and a sample-ratio mismatch check is included.
  • Decision rule written before the testAll outcomes (significant up, significant down, non-significant with CI excluding -0.5pp, non-significant with CI including -0.5pp) map to stated actions.
  • Sized from the real trafficSample size is calculated from the 8% baseline and 0.5pp effect, duration follows from traffic accumulation, and it runs full weeks.

Grades and run details

Decision model 79 · LLM judge 9 of 13 checks
Decision model checks
  • failUses the supplied evidence correctly3%
  • passAddresses the actual decision71%
  • passRespects explicit constraints30%
  • passIdentifies material uncertainty92%
  • passAvoids unsupported claims45%
  • passProduces the required deliverable59%
  • passTests one change at a time100%
  • passFixes the duration up front98%
  • partialAn unambiguous primary metric41%
  • passDecision rule written before the test94%
  • partialSized from the real traffic34%
  • partialGuardrails with thresholds88%
Run
Run
#1
Time to output
31 s
Submitted
30 Sept 2026

Results

Every setup we’ve tested on this task, across all cases and repeats, graded on the current checklist. Provisional The checklist is still being calibrated against our PM.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6.1 SolAPI85.488.52None
2Sonnet 5.5API87.588.52None
3Opus 5.5Claude79.276.92None
4GPT-6 LunaAPI81.373.12None
5Gemini 3.5 Flash-LiteGemini33.315.411 capped

The PM job

Specifying an A/B test before running it.

Why it matters

Most tests are underpowered, or measure a metric nobody agrees is good.

What good looks like

  • One primary metric everyone agrees on the direction of
  • Decision rule stated up front
  • Guardrails
  • Power considered

Deliberately not measured

    Capability tested

    Test design

    The failure we’re looking for

    A test with no decision attached

    Grading

    Decision model and LLM judge, calibrated against a blind PM review