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 12 graded outputs by 6 models. 50% were usable with at most a quick edit.

Reliably right

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
Makes the decisions clear100% pass
The final slide gives both decisions with their costs/benefits, an owner, and an 8 October date before Q4 renewals.
GPT-6 Astra · ChatGPT · A quarterly business review deck, in brand
A real 7-slide deck100% pass

Where it slips

Owns the problem25% pass
It owns the problem and recommends next steps, but does not state what the team has already done about the drop or ticket spike, and the recommendations lack explicit trade-offs.
GPT-6 Astra · ChatGPT · The CEO asks why a metric dropped
Addresses the actual decision54% 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
Avoids unsupported claims63% pass
It presents '1,100 customers expect it on 28 Oct' and 'not a saving we planned' as established facts when the source only suggests them.
Opus 5.5 · Claude · 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

You're the Staff PM for Ledgerly's Billing group. The quarterly business review with Lena (CEO) and the exec team is on 8 October; the deck goes out the day before as a pre-read. Using the material below, build the Q3 deck for Billing. The deck must: - Have at most 7 slides, each one 16:9. - Give every slide a headline that states its takeaway, not a topic (“NRR fell to 101% after the pricing change”, not “NRR”). - Report every OKR honestly, including the misses. - End with the decisions you need from the exec team, each with an owner and a date. - Use only Ledgerly's brand colours (below). Deliver it as a single self-contained HTML file: the slides stacked top to bottom, each exactly 1280 × 720 pixels, no external images, fonts or scripts, readable without JavaScript. Draw any charts in HTML or SVG. With the file, reply with a cover note to Lena of no more than 60 words.

Ledgerly brand coloursInk #1B2A41 (text and headlines). Teal #0F8B8D (the primary brand colour: accents, charts, on-track items). Amber #F2A541 (only for items that are at risk or missed; never decorative). Mist #E8EEF2 (backgrounds, table rules). White #FFFFFF. No other colours, no gradients. Body text at least 18px.

Q3 OKRs (Finance sheet, final)1. New ARR: target $2.40M, actual $2.61M (109%). 2. Net revenue retention: target 108%, actual 101.3% (Q2: 106.0%). 3. Activation: 45% of new sign-ups send a first invoice within 7 days; actual 38% (Q2: 36%). 4. Payments reliability: 99.95% target, actual 99.91% (one incident: 2 September, 4 hours of degraded payments during the card-fraud response).

What moved NRRPricing moved from per-seat to usage-based on 1 September. Three of the top 20 accounts downgraded in September (−$540k ARR combined), citing the new pricing. Harbor & Co ($310k ARR) churned to a competitor, citing no bulk invoicing. The Customer Success dashboard shows NRR at 102%, because it excludes churned customers under $5k ARR; Finance's 101.3% is the figure the board sees.

LaunchesBulk invoicing: promised for 12 October, now 26 October (23 October at best), because Payments paused the batch-send API during the card-fraud incident. The 14 October customer webinar (1,100 registered) has been moved to 28 October. AI-drafted support replies: live on the three non-billing queues since 21 September; median first-response time down 41%. Billing queue waits for updated pricing data and a refund-language block; eval re-run due 20 October.

CustomersNPS: 34 (Q2: 41). 62% of detractor comments mention the pricing change. 20 of the top 50 accounts renew in Q4, worth $2.1M ARR; Customer Success rates 7 of them ($900k) at risk, all citing pricing.

Team and budgetHires: 9 of 14 planned; two senior engineers left Payments in August (both regretted). Spend: 4% under budget, mostly the unfilled roles.

Decisions the exec team needs to make(a) Whether to hold the top-20 accounts on their old pricing for six months: Finance estimates it costs $420k of ARR this year; Customer Success thinks it protects most of the $900k at risk. (b) Whether to move two engineers from Growth to Payments until bulk invoicing ships, which the Payments lead says would secure 23 October; Growth would pause the activation experiments for six weeks.

Draft headline from the VP of Sales“Record quarter: best-ever new ARR, 109% of target.” (She'd like the deck to open with it.)

What a strong answer does

Opens with an honest headline for the quarter: new ARR beat target, but NRR fell to 101.3% against 108% after the pricing change, and activation and reliability missed. Does not lead with 'record quarter' on its own. Uses Finance's 101.3% (noting the CS dashboard's different definition if it mentions 102%). Shows each OKR with a plain status, amber only on the misses. Covers the NRR drivers (the three downgrades and Harbor & Co), the bulk-invoicing slip to 26 October (23 at best), NPS 41 to 34, and the $900k renewal risk. Closes with the two decisions, framed with their trade-offs, each with an owner and a date before the Q4 renewals. At most 7 slides, takeaway headlines, the brand palette only, and a short cover note.

Critical failures (cap the score)

Reports the NRR, activation or reliability OKR as met or on track
Opens the deck with the 'record quarter' headline and omits the retention drop from the opening

Case

v1.5 · synthetic · exec communication, slides, brand, staff-level

GPT-6 AstraChatGPTUsable with a quick edit GPT-6 LunaAPIUsable with a quick edit Sonnet 5.5APINeeds more work GPT-6.1 SolAPIUsable with a quick edit Opus 5.5ClaudeNeeds more work Gemini 3.5 Flash-LiteGeminiNeeds more work

Gemini 3.5 Flash-LiteGemini

Needs more than a quick edit6 of 13 checks passed

It needs evidence re-checking to correct the Q1/Q3 error and remove unsupported causes, not just a quick wording edit.

Output

Open the deckThe file the model delivered, in a new tab

Cover Note to Lena Lena, here is the Q3 Billing pre-read deck for our Oct 8 review. While New ARR hit a record $2.61M (109%), the Q1 pricing transition pulled NRR down to 101.3%¹ and created $900k in Q4 renewal risk. We need two critical decisions on Oct 9³ regarding account pricing holds and engineering reallocation to protect Q4. See you on the 8th.

What a PM had to fix

1Invented evidenceVerify or remove the claimSubstantial rework
Source: What moved NRRPricing moved from per-seat to usage-based on 1 September.
What we’d changeThe pricing change was on 1 September, not in Q1. Correct the dates and launch status, and remove the causes the material doesn't give.
2Invented evidenceVerify or remove the claimTargeted repair
What we’d changeSlide 3 blames the activation miss on delayed bulk invoicing and pricing confusion. The material gives no cause, and the miss needs what's been tried and a way forward.
3Invented evidenceVerify or remove the claimQuick edit
Source: BriefThe quarterly business review with Lena (CEO) and the exec team is on 8 October
What we’d changeThe review is on 8 October, so that's when the decisions are needed.

Check by check

Got wrong · 5

Uses the supplied evidence correctlyIt includes unsupported current-state claims such as 'Q1 pricing transition', new ARR drivers, activation causes, and budget being driven entirely by unfilled roles.
Addresses the actual decisionIt presents the two exec decisions with trade-offs but does not commit to a clear recommendation or state what would change the call.
Identifies material uncertaintyIt does not name the specific unknowns that could change the decisions or say how they would be resolved.
Avoids unsupported claimsIt presents hypotheses as established fact, including causes for new ARR and activation and the degree of budget underspend.
Owns the problemIt reports NRR, activation, and reliability misses but does not say what the team has already done about them or recommend a way forward beyond the two exec decisions.

Mixed · 2

Respects explicit constraintsIt stays within 7 slides, uses takeaway headlines, ends with decisions with owners and dates, and the cover note is within 60 words.The two graders disagreed on this one.
Ready for the reader as deliveredIt contains the incorrect 'Q1 pricing transition', unsupported causes, and an unmarked proposed 2026 date, so it is not ready as delivered.The two graders disagreed on this one.

Got right · 6

Produces the required deliverableThe requested HTML deck and cover note are present, complete, and usable as a pre-read for the named reader.
Leads with the honest headlineThe first content slide opens with the record new ARR but immediately pairs it with the retention drop/headwinds, so it does not omit the NRR miss.
Makes the decisions clearIt ends with both required decisions, each with trade-offs, an owner, and a date before Q4 renewals.
Headlines state the takeawayEvery slide headline states a takeaway rather than a topic label.
A real 7-slide deck
Uses only the brand colours

Claims the judge couldn’t find in the brief

The cover note says the Q1 pricing transition pulled NRR down.
New ARR was driven by strong outbound momentum and enterprise expansion before the pricing transition.
Activation missed due to delayed bulk invoicing and pricing transition confusion/onboarding friction.

Grades and run details

Decision model 69 · LLM judge 5 of 12 checks

Decision model checks

partialUses the supplied evidence correctly18%
failAddresses the actual decision88%
failRespects explicit constraints32%
partialIdentifies material uncertainty46%
partialAvoids unsupported claims46%
passProduces the required deliverable47%
passLeads with the honest headline82%
passMakes the decisions clear100%
passHeadlines state the takeaway73%
passA real 7-slide deckby hand100%
passUses only the brand coloursby hand100%
partialOwns the problem57%
passReady for the reader as delivered13%

Artefacts

Deck (html)

Run

Run: #1
Time to output: 20 s
Submitted: 27 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 86% of checks.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6 AstrawithChatGPT	87.3	87.1	2	None
2	GPT-6 LunawithAPI	81.0	82.6	2	None
3	GPT-6.1 SolwithAPI	77.7	83.0	2	None
4	Sonnet 5.5withAPI	78.5	64.4	2	None
5	Opus 5.5withClaude	80.4	51.9	2	None
6	Gemini 3.5 Flash-LitewithGemini	62.1	39.0	2	None

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