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

Analyse experiment results

Can the model separate evidence from speculation, identify decision-relevant uncertainty and recommend a sensible next action?

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. 83% were usable with at most a quick edit.

Reliably right

  1. Produces the required deliverable100% pass
    It is a usable decision memo for the named reader with a clear recommendation, reasoning, and next steps.
    GPT-6 Astra · ChatGPT · Conversion up, retention down
  2. Gets the base of every number right100% pass
    The refund increase and revenue difference are computed correctly, and the retention CI interpretation uses the correct 2pp threshold.
    GPT-6 Astra · ChatGPT · Conversion up, retention down
  3. Interprets power correctly100% pass
    Explains that the test was designed to detect ~3pp effects, so a smaller but worthwhile lift could still exist and remain undetected.
    GPT-6 Astra · ChatGPT · The underpowered onboarding test

Where it slips

  1. Trusts the data before reading it15% pass
    It does not explicitly check a pre-interpretation trust signal such as sample ratio or logging before relying on the lift; the denominator and maturity caveats come after interpreting.
    GPT-6.1 Sol · API · Conversion up, retention down
  2. Avoids unsupported claims60% pass
    It asserts the refund surge 'isn't noise' and that annual up-front billing means retention 'should rise' without supporting evidence or clear labeling.
    Sonnet 5.5 · API · Conversion up, retention down
  3. Uses the supplied evidence correctly69% pass
    Asserts that comparable per-arm ticket rates and exposure counts are unavailable, which is not in the supplied context and cannot be assumed.
    GPT-6 Astra · ChatGPT · The underpowered onboarding test

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

We tested a shorter onboarding checklist and the result came back 'not significant'. The team wants to call it a failure and move on. Write a short note to the onboarding team saying what we should conclude from this test and what we should do next. Keep it under 300 words.

ScenarioTrellis is a project-management tool for small teams. The shorter checklist cuts onboarding from nine steps to five. It is already built: shipping it to everyone would take about a day of engineering. Activation means creating a first project and inviting at least one teammate within seven days.
ReadoutActivation: A 31.2%, B 32.9% (+1.7pp, 95% CI −1.4 to +4.8). n = 3,960 per arm. The pre-registered minimum detectable effect was 3pp.
QualitativeSupport tickets tagged 'onboarding confusion' fell from 44 to 29 during the test.
Options on the tableRunning the test for another four weeks at current traffic would detect an effect of about 2pp.
What a strong answer does

Inconclusive, not a failure: the test could not detect effects smaller than ~3pp, and the point estimate is positive. Recommend a decision based on cost of shipping versus a longer test.

Critical failures (cap the score)
  • Concludes the change has no effect
Case

v1.6 · synthetic · null result, onboarding

GPT-6 AstraChatGPTUsable with a quick editGPT-6 LunaAPIUsable with a quick editGPT-6.1 SolAPIUsable with a quick editSonnet 5.5APIUsable with a quick editGemini 3.5 Flash-LiteGeminiUsable with a quick editOpus 5.5ClaudeNeeds more work
Sonnet 5.5API

Usable with a quick edit6 of 9 checks passed

The evidence and reasoning are solid; only light edits are needed to remove or label 'easy to reverse' and add a data-trust check.

Output

Subject: Shorter onboarding checklist: what the test tells us and what to do

Bottom line: This test didn't fail. It was inconclusive, and I recommend we ship the shorter checklist.

What the result says - Activation was 32.9% vs. 31.2% (+1.7pp). The 95% CI runs from −1.4 to +4.8pp, so "not significant" means we can't rule out zero. It doesn't mean there's no effect. - The data are compatible with a real gain of several points, and also with a small loss. The test was powered to detect 3pp, so a true effect of 1-2pp would probably look just like this. - Support tickets tagged "onboarding confusion" dropped from 44 to 29. The counts are small, so this isn't proof, but it points the same way as the activation estimate.

Why ship rather than keep testing - Shipping costs about a day of engineering, and it's easy to reverse. - The plausible downside is small (about −1.4pp at worst), and the point estimate and the ticket data both lean positive. - Four more weeks would detect ~2pp, but our observed effect is 1.7pp. We could easily wait a month and end up inconclusive again, while half of new users get the longer flow.

Next steps 1. Ship the five-step checklist to everyone. 2. Track seven-day activation against the pre-test baseline for the next 4-6 weeks. If it drops well below ~31%, we revert. 3. Keep tracking the onboarding-confusion tickets. 4. Record the test as "inconclusive, shipped on cost and risk grounds" rather than "failed," so nobody later cites it as evidence that shorter onboarding doesn't work.

If we were weighing a costly or hard-to-reverse change, I'd argue for the longer test. Here the cheaper move is to ship and monitor.

Check by check

Got wrong · 2
  • Avoids unsupported claimsPresents 'easy to reverse' as established fact without support; other inferences are hedged.
  • Trusts the data before reading itDoes not check any trust signal such as sample ratio or logging before interpreting results.
Mixed · 1
  • Uses the supplied evidence correctlyIt invents 'easy to reverse', which is not in the supplied context and is presented as fact.The two graders disagreed on this one.
Got right · 6
  • Addresses the actual decisionCommits to shipping the shorter checklist and says it would revert if activation drops well below ~31% or argue longer if the change were costly.
  • Respects explicit constraintsRespects the form, reader, and under-300-word limit.
  • Identifies material uncertaintyNames effect-size uncertainty and small support-ticket counts, and says 4-6 week monitoring would resolve or trigger revert.
  • Produces the required deliverableIt is a complete, actionable note to the onboarding team within length.
  • Interprets power correctlyExplains MDE 3pp, the CI, and that a true 1-2pp effect would likely be missed.
  • Gets the base of every number rightAll cited percentages and differences match the supplied data, and ticket base is clear as tagged tickets.

Claims the judge couldn’t find in the brief

  • Shipping is easy to reverse.

Grades and run details

Decision model 83 · LLM judge 7 of 10 checks
Decision model checks
  • passUses the supplied evidence correctly39%
  • passAddresses the actual decision99%
  • passRespects explicit constraints58%
  • passIdentifies material uncertainty100%
  • partialAvoids unsupported claims27%
  • passProduces the required deliverable97%
  • passInterprets power correctly100%
  • failTrusts the data before reading it94%
  • passGets the base of every number right80%
Run
Run
#1
API response time
11 s
Submitted
29 Sept 2026
GPT-6 LunaAPI

Usable with a quick edit8 of 9 checks passed

The core reasoning and use of evidence are sound, and the only meaningful omissions are explicit trust checks and the original 3pp MDE, which could be added in a quick edit. No rechecking or reanalysis is required.

Output

Team—

This is inconclusive, not a failure. Activation was 1.7 percentage points higher with the shorter checklist, but the 95% CI (−1.4 to +4.8 points) includes no effect and possible harm—as well as a benefit of at least 3 points. The test wasn’t precise enough to settle the question.

I recommend running the test for another four weeks. That should let us assess effects around 2 points, which this change may plausibly deliver. Before extending, agree on what activation gain would justify rollout and what activation decline would stop it. Track onboarding-confusion tickets too, but compare rates per user: the raw drop from 44 to 29 is encouraging, not conclusive.

After the extension, decide using both activation and confusion—not a “significant/not significant” label alone.

Check by check

Got wrong · 1
  • Trusts the data before reading itIt does not explicitly check any trust signal such as sample ratio, exposure or logging quality, or novelty before interpreting the activation result.
Got right · 8
  • Uses the supplied evidence correctlyEvery stated fact or figure is drawn correctly from the supplied readout/options, and no current-situation facts are invented.
  • Addresses the actual decisionIt unambiguously answers that the result is inconclusive, not a failure, and says the next step is a four-week extension with pre-agreed thresholds before deciding.
  • Respects explicit constraintsThe output is a note to the onboarding team, stays well under 300 words, and respects the requested short-note form.
  • Identifies material uncertaintyIt names the unresolved true activation effect and support-ticket rate issue, bounds the activation uncertainty with the CI, and says extension plus pre-agreed thresholds would resolve it.
  • Avoids unsupported claimsInterpretive claims such as 'not precise enough' and 'encouraging, not conclusive' are appropriately hedged and follow from the supplied data.
  • Produces the required deliverableThe requested note is present, actionable, and complete enough for the onboarding team to proceed.
  • Interprets power correctlyIt communicates that the original test could not distinguish the observed 1.7pp effect from no effect or harm, and that more data would allow detection around 2pp.
  • Gets the base of every number rightThe activation figures and differences are computed from the right values, and the output flags that the ticket drop should be compared per user rather than as a raw count.

Grades and run details

Decision model 89 · LLM judge 9 of 10 checks
Decision model checks
  • passUses the supplied evidence correctly79%
  • passAddresses the actual decision97%
  • passRespects explicit constraints95%
  • passIdentifies material uncertainty98%
  • passAvoids unsupported claims47%
  • passProduces the required deliverable66%
  • passInterprets power correctly86%
  • failTrusts the data before reading it79%
  • passGets the base of every number right95%
Run
Run
#1
API response time
9 s
Submitted
29 Sept 2026

Results

Every setup we’ve tested on this task, across all cases and repeats, graded on the current checklist. Calibrated: the graders match our PM on 88% of checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstraChatGPT94.795.02None
2GPT-6 LunaAPI89.490.52None
3GPT-6.1 SolAPI89.490.52None
4Sonnet 5.5API81.775.92None
5Opus 5.5Claude76.157.32None
6Gemini 3.5 Flash-LiteGemini62.857.721 capped

The PM job

Reading a test readout and deciding what to do next.

Why it matters

Experiment readouts are where false confidence is cheapest to produce and most expensive to act on. A model that declares a winner on conversion while retention quietly falls will ship the wrong thing.

What good looks like

  • Checks the result can be trusted before interpreting it
  • Reports effects with their uncertainty
  • Checks guardrail metrics before declaring a winner
  • Separates what the data shows from plausible explanations
  • Recommends a next step proportionate to the evidence

Deliberately not measured

  • Re-running the statistics from raw data
  • Chart production
Capability tested

Interpreting results within their limits

The failure we’re looking for

Claims causality, ignores guardrails or recommends arbitrary testing

Grading

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