Humanity’s Last PRD
Latest drop: Gemini 3.8 Flash · 1 Oct
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 14 graded outputs by 7 models. 71% were usable with at most a quick edit.

Reliably right

  1. 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
  2. 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
  3. Checks guardrails before declaring a winner100% pass
    It evaluates both the day-30 retention guardrail and the refund-request guardrail before making a call.
    GPT-6 Astra · ChatGPT · Conversion up, retention down

Where it slips

  1. Trusts the data before reading it21% 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 claims52% 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 correctly55% 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.

What the model was given4 items: Scenario, Readout, Qualitative, Options on the table
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 doesThe answer key the graders mark against

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 at 40)
  • Concludes the change has no effect
Case

Mid-level · v1.8 · synthetic · null result, onboarding

The outputs

7 setups answered this brief. Pick up to three to read side by side.

GPT-6 AstraChatGPT95Usable with a quick editGPT-6.1 SolAPI93Usable with a quick editGPT-6 LunaAPI90Usable with a quick editSonnet 5.5API78Usable with a quick editGemini 3.5 Flash-LiteGemini65Usable with a quick editOpus 5.5Claude63Needs more workGemini 3.8 FlashAPI43Needs more work
Gemini 3.8 FlashAPI

Needs more than a quick edit3 of 9 checks passed

A PM would need to re-check the evidence and add rollback or monitoring thresholds before acting. The note overstates certainty and omits what would change the ship decision.

Output

Team,

Before we write off the shorter checklist as a failure, let’s reframe what the data actually tells us.

“Not statistically significant” does not mean “no impact”—it means our test was underpowered to detect an effect smaller than 3pp. Here is the full picture:

  1. The directional signals are uniformly positive: Activation increased by +1.7pp (31.2% → 32.9%). While the 95% CI (−1.4pp to +4.8pp) crosses zero, it leans heavily positive. The downside risk is small, while the potential upside is meaningful.
  2. Clear reduction in user friction: Support tickets tagged “onboarding confusion” dropped by 34% (44 to 29). Even if activation were completely flat, cutting nearly half the steps improved user experience and reduced support burden.
  3. The cost/benefit is strongly asymmetric: Shipping this takes one day of engineering.

Recommendation: Ship the 5-step checklist to 100% and move on.

Running the test for another four weeks to hit an MDE of 2pp isn’t the best use of our time or traffic. We would burn a month of runway just to confirm what looks like a positive, low-risk change with virtually zero engineering cost.

Let’s bank the operational win (fewer support tickets, leaner UX, likely small activation lift) by rolling it out tomorrow. We can monitor activation metrics in production and direct our experimentation bandwidth toward bigger, higher-leverage bets for Trellis.

Claims the judge couldn’t find in the brief

  • The CI “leans heavily positive.”
  • The downside risk is small and the potential upside is meaningful.
  • Cutting nearly half the steps improved user experience and reduced support burden.
  • The change is positive and low-risk.
  • The engineering cost is virtually zero.
  • There is likely a small activation lift.

Grades and run details

Decision model 61 · LLM judge 3 of 10 checks
Decision model checks
  • failUses the supplied evidence correctly29%
  • passAddresses the actual decision30%
  • passRespects explicit constraints76%
  • partialIdentifies material uncertainty67%
  • failAvoids unsupported claims79%
  • passProduces the required deliverable87%
  • passInterprets power correctly92%
  • failTrusts the data before reading it98%
  • passGets the base of every number right62%
Run
Run
#1
API response time
11 s
Submitted
1 Oct 2026
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.

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 correctly34%
  • passAddresses the actual decision100%
  • passRespects explicit constraints63%
  • passIdentifies material uncertainty100%
  • partialAvoids unsupported claims30%
  • passProduces the required deliverable97%
  • passInterprets power correctly100%
  • failTrusts the data before reading it90%
  • passGets the base of every number right77%
Run
Run
#1
API response time
11 s
Submitted
29 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 4

Uses the supplied evidence correctlyWrongMixed
Gemini 3.8 Flash · API

It presents several unsupported interpretations and causal claims as facts, such as the CI leaning heavily positive, low risk, improved UX, and reduced support burden.

Sonnet 5.5 · API

It invents 'easy to reverse', which is not in the supplied context and is presented as fact.

Addresses the actual decisionMixedRight
Gemini 3.8 Flash · API

It commits to shipping, but does not state what result or condition would change that call.

Sonnet 5.5 · API

Commits to shipping the shorter checklist and says it would revert if activation drops well below ~31% or argue longer if the change were costly.

Identifies material uncertaintyWrongRight
Gemini 3.8 Flash · API

It notes the CI crosses zero but does not name the unknowns that would change the decision or how they would be resolved.

Sonnet 5.5 · API

Names effect-size uncertainty and small support-ticket counts, and says 4-6 week monitoring would resolve or trigger revert.

Produces the required deliverableMixedRight
Gemini 3.8 Flash · API

Although it is a note within length, it is not safely usable because the recommendation lacks decision thresholds and overstates the evidence.

Sonnet 5.5 · API

It is a complete, actionable note to the onboarding team within length.

All got wrong 2

Avoids unsupported claimsWrongWrong
Gemini 3.8 Flash · API

It treats friction reduction, UX improvement, support-burden reduction, low risk, and likely activation lift as established rather than labelled hypotheses.

Sonnet 5.5 · API

Presents 'easy to reverse' as established fact without support; other inferences are hedged.

Trusts the data before reading itWrongWrong
Gemini 3.8 Flash · API

It does not check trust signals such as sample ratio, exposure, logging, or novelty before interpreting the result.

Sonnet 5.5 · API

Does not check any trust signal such as sample ratio or logging before interpreting results.

All got right 3

Respects explicit constraintsRightRight
Gemini 3.8 Flash · API

It is a short note to the onboarding team and is under 300 words.

Sonnet 5.5 · API

Respects the form, reader, and under-300-word limit.

Interprets power correctlyRightRight
Gemini 3.8 Flash · API

It correctly says the test was underpowered for effects smaller than about 3pp and that the result is not evidence of no effect.

Sonnet 5.5 · API

Explains MDE 3pp, the CI, and that a true 1-2pp effect would likely be missed.

Gets the base of every number rightRightRight
Gemini 3.8 Flash · API

The activation difference, CI, ticket percentage, and step reduction are computed from the correct bases.

Sonnet 5.5 · API

All cited percentages and differences match the supplied data, and ticket base is clear as tagged tickets.

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 AstraChatGPT100.095.02None
2GPT-6.1 SolAPI94.790.52None
3GPT-6 LunaAPI91.990.52None
4Sonnet 5.5API81.775.92None
5Opus 5.5Claude73.657.32None
6Gemini 3.5 Flash-LiteGemini60.357.721 capped
7Gemini 3.8 FlashAPI55.624.121 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