Humanity’s Last PRD
Latest drop: Gemini 3.8 Flash · 1 Oct
Tasks / Operate

Write a stakeholder update

Can the model turn messy project status into an honest update that leads with what the reader needs to know or decide?

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. 43% were usable with at most a quick edit.

Reliably right

  1. Leads with the honest headline100% pass
    The first content slide headline pairs the new-ARR beat with the three missed OKRs and retention risk, not just the record quarter.
    GPT-6 Astra · ChatGPT · A quarterly business review deck, in brand
  2. A real 7-slide deck100% pass
  3. Answers the question asked96% pass
    The first line directly addresses whether the new navigation caused the drop.
    GPT-6.1 Sol · API · The CEO asks why a metric dropped

Where it slips

  1. Owns the problem23% pass
    It proposes future analysis and fixes but does not state any actions the team has already taken or offer trade-offs.
    GPT-6.1 Sol · API · The CEO asks why a metric dropped
  2. Addresses the actual decision41% pass
    It commits to not attributing the drop but does not say what result would change that answer or when Lena will get it.
    GPT-6 Luna · API · The CEO asks why a metric dropped
  3. Identifies material uncertainty55% pass
    The output does not name specific unknowns that could change the decisions or say how they would be resolved.
    GPT-6 Luna · API · A quarterly business review deck, in brand

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

Our CEO, Lena, posted in the leadership channel: “Why did weekly active teams drop 6% last week? Is it the new navigation?” You own the product area. Using the material below, write your reply to Lena, to post in the channel. Keep it under 200 words.

What the model was given4 items: Scenario, Metrics, Incident, Support
ScenarioTrellis is a project-management tool for small teams. The new navigation went to 100% of web users on Tuesday 15 September after two weeks in a 10% beta. The mobile apps did not change.
MetricsWeekly active teams: week of 7 September 18,420; week of 14 September 17,310 (−6.0%). Web only: −6.3%. Mobile only: −5.2%. Teams that were in the navigation beta: −1.1%. The same week last year, weekly active teams fell 4%, which we put down to the start of the school year.
IncidentOn Thursday 17 September, single sign-on failed for two hours. About 30% of teams use single sign-on.
SupportTickets mentioning navigation: 212 last week, up from 40. Most are some version of “where did X go?”
What a strong answer doesThe answer key the graders mark against

Answers directly that the cause is not established and the navigation is unlikely to be the main driver (mobile, which did not change, fell nearly as much, and the beta cohort barely moved), while taking the navigation support tickets seriously. Names the other candidates with their evidence (seasonality, the SSO outage), says what is being checked and when Lena will hear back.

Critical failures (cap the score at 40)
  • States a single cause as established fact
Case

Mid-level · v1.8 · synthetic · metrics, exec communication

The outputs

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

GPT-6.1 SolAPI91Usable with a quick editGPT-6 AstraChatGPT89Usable with a quick editGPT-6 LunaAPI86Usable with a quick editSonnet 5.5API59Needs more workOpus 5.5Claude59Needs more workGemini 3.5 Flash-LiteGemini43Needs more workGemini 3.8 FlashAPI30Needs more work
Gemini 3.8 FlashAPI

Needs more than a quick edit1 of 10 checks passed

A PM would need to re-check evidence and rewrite the causal claims, uncertainty, and next steps. The invented tooltip deployment and overconfident navigation conclusion are not quick wording fixes.

Output

Hi Lena — the new navigation isn't the primary driver of the 6% drop.

The data points to broader seasonal and technical factors:

  • Seasonality: We historically see a dip this week (-4% last year) due to the start of the school year.
  • Mobile dropped similarly: Mobile-only activity fell 5.2% despite receiving zero navigation changes (vs. -6.3% on web).
  • Beta teams held steady: Teams familiar with the new UI only dropped 1.1%.
  • SSO outage: Thursday’s 2-hour outage locked out access for up to 30% of our user base, contributing to the dip.

There is some expected initial friction: navigation support tickets rose from 40 to 212, primarily around discoverability (“where did X go?”).

Next steps: We are deploying targeted in-app tooltips this week to guide users to relocated features and expect activity to rebound as users adjust and seasonal trends normalize. I’ll share an updated cohort analysis on Friday.

Claims the judge couldn’t find in the brief

  • The new navigation isn't the primary driver of the 6% drop.
  • The data points to broader seasonal and technical factors.
  • We historically see a dip this week (-4% last year) due to the start of the school year.
  • Thursday’s 2-hour outage locked out access for up to 30% of our user base.
  • The SSO outage contributed to the dip.
  • There is some expected initial friction.
  • We are deploying targeted in-app tooltips this week.

Grades and run details

Decision model 55 · LLM judge 1 of 11 checks
Decision model checks
  • failUses the supplied evidence correctly34%
  • partialAddresses the actual decision74%
  • passRespects explicit constraints77%
  • failIdentifies material uncertainty36%
  • failAvoids unsupported claims25%
  • passProduces the required deliverable96%
  • passAnswers the question asked99%
  • partialSeparates effect from explanation32%
  • partialOwns the problem87%
  • passReady for the reader as delivered50%
Run
Run
#1
API response time
17 s
Submitted
1 Oct 2026
GPT-6 LunaAPI

Usable with a quick edit8 of 10 checks passed

The evidence and reasoning are correct; only small omissions like a timeline and already-taken actions would need a quick edit.

Output

Lena, we can’t yet attribute the 6% drop to the new navigation. The timing and jump in navigation-related tickets (212, up from 40) make discoverability a real concern, but teams in the two-week beta were down only 1.1%. The decline also appears on mobile (−5.2%), which didn’t change, so navigation alone is unlikely to explain it.

Two other factors may contribute: WAU fell 4% in the same week last year, around the start of the school year, and SSO was unavailable for two hours on Thursday for teams using it (about 30%).

I’d treat the navigation as a usability issue to address, not yet the proven cause of the overall decline. We’ll compare affected and unaffected cohorts, check the SSO impact, and investigate where users are getting stuck; meanwhile, we’ll prioritize fixes for the “where did X go?” feedback.

Grades and run details

Decision model 90 · LLM judge 9 of 11 checks
Decision model checks
  • passUses the supplied evidence correctly69%
  • partialAddresses the actual decision29%
  • passRespects explicit constraints32%
  • passIdentifies material uncertainty62%
  • passAvoids unsupported claims67%
  • passProduces the required deliverable53%
  • passAnswers the question asked90%
  • passSeparates effect from explanation84%
  • partialOwns the problem92%
  • passReady for the reader as delivered91%
Run
Run
#1
API response time
6 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 7

Uses the supplied evidence correctlyWrongRight
Gemini 3.8 Flash · API

It invents or overstates several current-situation facts, including user-base lockout, causal contribution, historical seasonality, and an ongoing tooltip deployment.

GPT-6 Luna · API

All named facts, figures, and quotes match the supplied context with no invented current-state information.

Respects explicit constraintsMixedRight
Gemini 3.8 Flash · API

Although it is addressed to Lena and under 200 words, it violates the supplied-material constraint by adding unsupported current actions and causal claims.

GPT-6 Luna · API

It is a channel reply to Lena, under 200 words, and uses the supplied material.

Identifies material uncertaintyWrongRight
Gemini 3.8 Flash · API

It does not name the material unknowns or explain how the Friday cohort analysis would change the call.

GPT-6 Luna · API

It names the unknown cause, bounds navigation with beta and mobile data, and states it will compare cohorts and check SSO impact.

Avoids unsupported claimsWrongRight
Gemini 3.8 Flash · API

It presents navigation not being primary, SSO contributing, and expected rebound as established or near-established rather than labelled hypotheses.

GPT-6 Luna · API

Causal statements are hedged as concerns or possibilities and are tied to supplied evidence.

Produces the required deliverableMixedRight
Gemini 3.8 Flash · API

The reply is not usable as-is because it overstates causality, omits the required uncertainty framing, and includes invented actions.

GPT-6 Luna · API

The required reply is present, complete, under 200 words, and usable as a leadership-channel update.

Separates effect from explanationWrongRight
Gemini 3.8 Flash · API

Possible causes are presented as data-backed factors rather than labelled hypotheses with evidence and contradictions.

GPT-6 Luna · API

Navigation, seasonality, and SSO are treated as possible explanations, each with supporting or contradicting evidence.

Ready for the reader as deliveredMixedRight
Gemini 3.8 Flash · API

It includes invented current actions and unsupported causal statements, so it should not go to Lena without substantive correction.

GPT-6 Luna · API

It is written entirely for Lena with no placeholders, unexplained terms, or invented owners or dates.

All got wrong 2

Addresses the actual decisionWrongWrong
Gemini 3.8 Flash · API

It commits early that navigation is not the primary driver but does not say what result or condition would change that answer.

GPT-6 Luna · API

It commits to not attributing the drop but does not say what result would change that answer or when Lena will get it.

Owns the problemWrongWrong
Gemini 3.8 Flash · API

It proposes tooltips and an analysis but does not provide already-taken actions with a recommended option and trade-off for each material problem.

GPT-6 Luna · API

It recommends next steps but does not state what the team has already done about the reported problems or offer trade-offs.

All got right 1

Answers the question askedRightRight
Gemini 3.8 Flash · API

The first line directly answers whether the new navigation caused the drop.

GPT-6 Luna · API

The first line directly answers that the drop cannot yet be attributed to the new navigation.

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 80% of checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstraChatGPT86.787.12None
2GPT-6.1 SolAPI75.883.02None
3GPT-6 LunaAPI73.882.62None
4Opus 5.5Claude80.451.92None
5Sonnet 5.5API68.864.42None
6Gemini 3.5 Flash-LiteGemini62.139.02None
7Gemini 3.8 FlashAPI44.837.92None

The PM job

Writing the weekly update, or the reply to an executive's question, that the team's credibility rests on.

Why it matters

Updates are where bad news gets softened. A fluent update that buries a slipped date or presents a guess as the cause does more harm than no update: leadership decides on it, and trust goes when the truth comes out.

What good looks like

  • Leads with the news and any decision needed
  • States dates, numbers and causes plainly, including bad news
  • Separates what is known from what is suspected
  • Brings a recommendation and what's been tried, not just the problem
  • Fits the reader and the length asked for

Deliberately not measured

  • Tone and formatting preferences
  • Writing style beyond clarity
Capability tested

Honest, decision-first status communication

The failure we’re looking for

Spin: burying or softening the bad news, or presenting a guess as fact

Grading

Decision model, LLM judge and a browser check (for the deck), calibrated against a blind PM review

Variants

A short written update · Staff level: a QBR deck in brand colours