Humanity’s Last PRD
Latest drop: Gemini 4 Argon · 13 Oct
Tasks / Leadership

Write a performance review

Can the model write a review that is clear, fair and specific enough to change what the person does next?

Task type v1.1 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

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

From 18 graded outputs by 9 models. 56% were usable with at most a quick edit.

Reliably right

  1. Judges outcomes, not activity100% pass
    Every rating change and keep is justified by outcomes against goals, with shipping and activity treated as context, not the result.
    GPT-6 Astra · ChatGPT · Fourteen ratings, three managers
  2. Owns the manager's part100% pass
    Ana explicitly owns that she said he was on track in Q3 without discussing the metrics, and commits to reviewing goal metrics in future 1:1s.
    Gemini 4 Argon · API · Shipped a lot, moved nothing
  3. Respects explicit constraints97% pass
    The output is a pre-read under 1,100 words, includes the ratings to change and keep with evidence, and a note to Ravi, respecting all explicit constraints.
    GPT-6 Astra · ChatGPT · Fourteen ratings, three managers

Where it slips

  1. Sets next goals as outcomes, with support47% pass
    Goals are partly process-oriented (implement a mandatory process, create a framework) and lack specific support from Ana beyond a general offer.
    Gemini 3.5 Flash-Lite · Gemini · Shipped a lot, moved nothing
  2. Identifies material uncertainty51% pass
    The output does not name any specific unknowns that could change the rating or how they would be resolved.
    Opus 5.5 · Claude · Shipped a lot, moved nothing
  3. Catches the inflated team69% pass
    The output proposes lowering Ben from Exceeds to Meets, failing to keep Ben at Exceeds as required; Ben beat both outcome goals and should remain Exceeds.
    Opus 5.5 · Claude · Fourteen ratings, three managers

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

  1. Clear and direct, with care

    Does the review say plainly what needs to change, in words the person couldn't misread, while showing it is written to help them succeed?

    Passes when Every main point is stated directly, with the specific change expected and the support on offer.

    Kim ScottLenny’s Podcast

  2. Judges outcomes, not activity

    Does the review judge the person on the outcomes they achieved against their goals, rather than on how much they shipped or how busy they were?

    Passes when Rates against the goals' outcomes, with the evidence, and treats output as context, not the result.

    Product and growth leadersOn Lenny’s Podcast

  3. Names gaps you could see

    Is each area to improve described as specific, observable behaviour with what good would look like, rather than labels like 'be more strategic'?

    Passes when Every gap names what the person did or didn't do, in a specific situation, and what doing it well would look like.

    Product and growth leadersOn Lenny’s Podcast

  4. Weighs the whole period

    Does the review weigh the whole review period, rather than letting one recent event or one strong impression decide the verdict?

    Passes when Covers the whole period in proportion, and explicitly guards against a recent event or one impression dominating.

    Product and growth leadersOn Lenny’s Podcast

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · and 3 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then compare up to three outputs, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're the Director of Product at Northgate, and you're facilitating Thursday's calibration for 14 PMs across three managers (Ravi, Sara and Tom). Write the pre-read, in no more than 1,100 words: the ratings you'd change and why, each with the evidence, the ones you'd keep, and a short note to Ravi about his ratings. Use the guidance below.\n\nThe proposed ratings and evidence are attached.

What the model was given2 items: calibration_guidance.md, proposed_ratings.csv (one row per PM, as each manager submitted it)
calibration_guidance.md7 lines · Download
# Calibration guidance

- Ratings judge outcomes against goals first, then how the work was done. Shipping is not an outcome.
- Any rating other than Meets needs written evidence of outcomes.
- A Below rating needs documented earlier feedback on the gap.
- Expected spread across the org: most PMs Meets; Exceeds for clear outperformance on outcomes.
- The calibration facilitator proposes changes; managers can respond before ratings are final.
proposed_ratings.csv (one row per PM, as each manager submitted it)pm,manager,proposed_rating,goal_1,goal_1_result,goal_2,goal_2_result,evidence_cited_by_manager Aisha,Ravi,Greatly exceeds,Raise trial conversion 8% to 11%,8.4%,Ship self-serve billing,Shipped,Shipped 9 features; great energy; leadership loves her Ben,Ravi,Exceeds,Cut onboarding time 10 to 5 days,4.5 days,Raise activation 30% to 38%,39%,Hit both goals Chloe,Ravi,Exceeds,Grow API usage 20%,+4%,Launch partner portal,Shipped,Partner portal launched on time; strong specs Dev,Ravi,Exceeds,Reduce support tickets per account 25%,-27%,NPS from 31 to 40,41,Hit both goals; mentored two APMs Emma,Ravi,Exceeds,Expansion revenue +15%,+6%,Ship usage dashboard,Shipped,Very responsive to Sales; shipped 6 Sales requests Femi,Sara,Meets,Raise weekly retention 41% to 46%,46%,Cut churn of new accounts 20%,-22%,"Hit both goals, steady" Gita,Sara,Below,Raise invoices paid online 35% to 45%,47%,Launch reminders v2 by September,"Launched, rolled back after 4 days (support not briefed)",Reminders launch in September was a mess Hugo,Sara,Meets,Ship mobile app v2,Shipped late,Mobile weekly actives +20%,+3%,Delivered a hard project Ines,Tom,Exceeds,Cut checkout drop-off 30% to 22%,21%,Launch two payment methods,Shipped,"Hit both, specific evidence" Jon,Tom,Meets,Search success 60% to 70%,66%,Ship filters,Shipped,"Good progress, more to do" Kai,Tom,Meets,Reduce refund rate 4% to 3%,3.1%,Fraud losses -20%,-24%,Solid year Lena,Tom,Below,Grow marketplace listings 25%,+9%,Launch seller analytics,Not launched,"Missed both goals; feedback given in Q2 and Q3 1:1s, documented" Mo,Tom,Meets,Cut time to first sale for new sellers 21 to 14 days,15 days,Seller NPS +5,+6,"Nearly hit, good stakeholder work" Nora,Sara,Meets,Raise payment success 92% to 95%,95.4%,Cut payment support tickets 30%,-35%,"Quiet, delivered"
What a strong answer doesThe answer key the graders mark against

Judges each PM on outcomes against their goals, as the guidance says. Ravi rated all five of his PMs Exceeds or above, but only Ben and Dev hit both goals; Aisha (Greatly exceeds) missed her conversion goal (8.4% against 11%), Chloe missed API usage (+4% against +20%) and Emma missed expansion (+6% against +15%), and his evidence is output ('shipped 9 features', 'responsive to Sales'). Proposes Aisha, Chloe and Emma at Meets (or lower for Chloe, whose outcome goal was far off), keeps Ben and Dev at Exceeds. Gita (Sara, Below) beat her main goal (47% against 45%) and the Below rests on one September launch, with no documented feedback, which the guidance requires: Meets, with the launch-readiness gap documented now. Nora (Sara, Meets) beat both goals: a candidate for Exceeds. Lena (Tom, Below) has documented feedback and missed both goals: Below stands. Hugo shipped late and missed his outcome: Meets is generous but defensible, or Below needs documentation first. The note to Ravi is direct and respectful: rate on outcomes, not activity, with what evidence he'd need.

Critical failures (cap the score at 40)
  • Keeps Aisha at Greatly exceeds
Task

Staff-level · v1.1

The outputs

9 setups answered this brief.

GPT-6 AstraChatGPT98Usable with a quick editGPT-6.1 SolAPI98Usable with a quick editHaiku 5.5API98Usable with a quick editGemini 3.8 FlashAPI96Usable with a quick editGemini 4 ArgonAPI89Usable with a quick editOpus 5.5Claude82Needs more workGPT-6 LunaAPI79Needs more workSonnet 5.5API73Needs more workGemini 3.5 Flash-LiteGemini29Needs more work
Gemini 4 ArgonAPI

Usable with a quick edit11 of 13 checks passed

The output contains a minor factual error (claiming four PMs beat two pure outcome goals instead of three); that can be fixed with a quick edit, and the reasoning and decisions otherwise stand.

Output

To: Ravi, Sara, Tom From: Director of Product Subject: Pre-read: Thursday PM Calibration

Team,

Ahead of Thursday’s calibration for our 14 PMs, I’ve reviewed your submissions against our calibration guidance.

As a reminder, our standard is: 1. Ratings judge outcomes against goals first, then how the work was done. Shipping is not an outcome. 2. Any rating other than Meets requires written evidence of outcomes. 3. A Below rating requires documented earlier feedback on the gap. 4. Most PMs should land at Meets, with Exceeds reserved for clear outperformance on outcomes.

Right now, our proposed spread is top-heavy (1 Greatly Exceeds, 5 Exceeds, 6 Meets, 2 Below) and applies uneven bars across managers. Below are my proposed adjustments to ensure we are grading on a consistent, outcome-driven standard across Northgate. These are proposals—come ready on Thursday to respond with additional context or push back with evidence before we finalize.

---

Proposed Rating Changes (6 PMs)

1. Aisha (Ravi): Greatly Exceeds $\rightarrow$ Meets (or Below, pending discussion) * Evidence: Goal 1 (raise trial conversion from 8% to 11%) landed at 8.4%. Goal 2 (ship self-serve billing) was shipped. Cited evidence: "Shipped 9 features; great energy; leadership loves her." * Why change: Shipping 9 features and self-serve billing are outputs, not outcomes. On her sole outcome metric, she achieved less than 15% of the targeted lift (8.4% vs. 11%). While positive energy and leadership visibility are great, ratings judge outcomes first. Because she missed her primary outcome goal, she cannot be rated above Meets. (If documented feedback was given on the conversion miss, Below would be on the table, though I assume none exists given the proposed rating.)

2. Chloe (Ravi): Exceeds $\rightarrow$ Meets * Evidence: Goal 1 (grow API usage 20%) landed at +4%. Goal 2 (launch partner portal) shipped on time with strong specs. * Why change: Launching the portal and writing strong specs are outputs and craft inputs, not outcomes. On her primary business outcome, Chloe missed the 20% growth target by a wide margin (+4%). Missing the main outcome goal rules out Exceeds.

3. Emma (Ravi): Exceeds $\rightarrow$ Meets * Evidence: Goal 1 (expansion revenue +15%) landed at +6%. Goal 2 (ship usage dashboard) shipped. Cited evidence: "Very responsive to Sales; shipped 6 Sales requests." * Why change: Responsiveness to Sales and shipping requests/dashboards are outputs. Those outputs did not translate into the target outcome: expansion revenue grew 6% against a 15% goal. Without outperformance on outcomes, Meets is the ceiling.

4. Gita (Sara): Below $\rightarrow$ Meets * Evidence: Goal 1 (raise invoices paid online 35% to 45%) landed at 47%. Goal 2 (launch reminders v2 by Sept) launched, then rolled back after 4 days because support wasn't briefed. Cited evidence: "Reminders launch in September was a mess." * Why change: Two reasons against our guidance: 1. Outcomes first: Gita outperformed her primary outcome goal by 2 percentage points (47% vs. 45%). The botched launch of reminders v2 is a legitimate execution ("how") failure that prevented an Exceeds, but outcomes come first. 2. Process requirement: A Below rating requires documented earlier feedback on the gap. A single botched launch in September with no cited record of prior performance feedback does not meet the bar for Below.

5. Nora (Sara): Meets $\rightarrow$ Exceeds * Evidence: Goal 1 (raise payment success 92% to 95%) landed at 95.4%. Goal 2 (cut payment support tickets 30%) landed at -35%. Cited evidence: "Quiet, delivered." * Why change: Nora is one of only four PMs in the org who beat two pure outcome goals—and she beat her support ticket reduction goal substantially (-35% vs. -30%). Being "quiet" is a personality trait, not a performance deficit. We must reward actual business impact over volume/visibility. Sara, please bring a brief written summary of Nora’s outcome evidence to formalize this Exceeds.

6. Hugo (Sara): Meets $\rightarrow$ Hold at Meets (Flagged for discussion / Q1 plan) * Evidence: Goal 1 (ship mobile app v2) shipped late. Goal 2 (mobile weekly actives +20%) landed at +3%. Cited evidence: "Delivered a hard project." * Why flag: On outcomes alone (+3% vs +20% target, plus a late ship), Hugo’s performance looks like Below. However, because Sara has not cited documented earlier feedback on the gap, our guidance prevents moving him to Below in this cycle. He stays at Meets by rule, but Sara and I need to align on Thursday about setting clear, documented expectations with him immediately.

---

Ratings to Keep (8 PMs)

  • Ben (Ravi) – Keep at Exceeds: Beat both pure outcome goals (onboarding time cut to 4.5 days vs. 5-day goal; activation raised to 39% vs. 38% goal). Clear outperformance. (Note: Ravi, please expand the 3-word written evidence slightly for the final record.)
  • Dev (Ravi) – Keep at Exceeds: Beat both pure outcome goals (support tickets -27% vs. -25% goal; NPS 41 vs. 40 goal) and demonstrated strong "how" by mentoring two APMs.
  • Ines (Tom) – Keep at Exceeds: Outperformed her primary outcome goal (checkout drop-off cut to 21% vs. 22% target) and delivered her two payment methods with specific evidence.
  • Femi (Sara) – Keep at Meets: Hit Goal 1 on the nose (46% retention) and beat Goal 2 (-22% churn vs. -20%). Strong, steady year. Borderline for Exceeds, so worth a 2-minute check on Thursday compared to Ben/Dev/Nora, but Meets is defensible.
  • Jon (Tom) – Keep at Meets: Solid progress on search success (66% vs. 70% goal) and shipped filters. Textbook Meets.
  • Kai (Tom) – Keep at Meets: Nearly hit refund rate (3.1% vs. 3.0%) and beat fraud loss reduction (-24% vs. -20%). Balanced out to a solid Meets.
  • Mo (Tom) – Keep at Meets: Nearly hit time to first sale (15 days vs. 14) and beat Seller NPS (+6 vs. +5) with good stakeholder execution. Solid Meets.
  • Lena (Tom) – Keep at Below: Missed both outcome and output goals significantly (+9% listings vs. 25% goal; analytics not launched), and Tom has documented feedback from Q2 and Q3 1:1s. Meets all criteria for Below.

Revised Org Spread: 4 Exceeds (29%), 9 Meets (64%), 1 Below (7%). This matches our expected org distribution and applies a single standard to everyone.

---

A Quick Note to Ravi

Ravi—looking at your submissions as a group, you proposed every one of your 5 PMs for Exceeds or Greatly Exceeds. While Ben and Dev genuinely crushed their outcome targets and earned Exceeds, the cases for Aisha, Chloe, and Emma relied heavily on feature velocity ("shipped 9 features", "launched on time"), stakeholder likability ("leadership loves her", "responsive to Sales"), and effort, while glossing over major misses on their core outcome metrics (8.4% vs 11%, +4% vs +20%, +6% vs +15%).

Our guidance is explicit that shipping is not an outcome. If we reward PMs with Exceeds for shipping features that fail to move their target metrics, we incentivize feature factories over business impact—and we penalize PMs on Sara’s and Tom’s teams who actually moved the needle. For Thursday, if you want to push back on Aisha, Chloe, or Emma, please bring written evidence of outcomes they drove that aren't currently captured in the sheet. Otherwise, let's plan to calibrate them to Meets and coach them in Q1 on connecting their strong execution energy to measurable impact.

Claims the judge couldn’t find in the brief

  • Nora is one of only four PMs in the org who beat two pure outcome goals.

Grades and run details

Decision model 92 · LLM judge 86
Decision model checks
  • passUses the supplied evidence correctly10%
  • passAddresses the actual decision36%
  • passRespects explicit constraints40%
  • passIdentifies material uncertainty83%
  • partialAvoids unsupported claims53%
  • passProduces the required deliverable89%
  • passCatches the inflated team100%
  • passRescues Gita from one bad launch100%
  • passNotices who's underrated100%
  • passClear and direct, with care98%
  • passJudges outcomes, not activity100%
  • partialNames gaps you could see55%
  • passWeighs the whole period76%
Run
Run
#1
API response time
18 s
Submitted
13 Oct 2026
GPT-6 AstraChatGPT

Usable with a quick edit13 of 13 checks passed

The pre-read is evidence-based, complete, and directly actionable; a PM could use it with minor wording tweaks without rechecking facts or redoing reasoning.

Output

Thursday calibration pre-read

From: Director of Product, Northgate Scope: 14 PMs across Ravi, Sara and Tom

These are my proposed changes, not final decisions. Please respond before Thursday with missing outcome evidence, relevant goal context, or documented earlier feedback that could change the assessment.

Standard I’m applying

We rate outcomes against goals first, then how the work was done. Shipping, responsiveness, visibility and strong execution matter, but do not substitute for impact.

For Exceeds, I’m looking for clear outperformance—not simply delivery or attainment. On this evidence, exceeding both measurable outcome goals supports Exceeds; meeting one and modestly beating another generally supports Meets. A modest beat on one outcome plus a shipped deliverable is not enough by itself.

A Below requires both evidence of underperformance and documented earlier feedback. Where that feedback is absent from the packet, I am not proposing Below. That does not mean the missed goals are acceptable or should go unaddressed.

Ratings I propose changing

PMProposed changeEvidence and rationale
Aisha — RaviGreatly exceeds → MeetsTrial conversion rose from 8% to 8.4%, against 11%. Self-serve billing shipped, but no resulting customer or business impact is supplied. Nine features, energy and leadership enthusiasm do not establish exceptional outcomes. The conversion miss is substantial; however, the packet contains no documented earlier feedback supporting Below.
Chloe — RaviExceeds → MeetsAPI usage grew 4%, against 20%. The partner portal launched on time, and strong specifications support execution quality, not outcome outperformance. There is no written evidence supporting Exceeds or documented earlier feedback supporting Below.
Emma — RaviExceeds → MeetsExpansion revenue grew 6%, against 15%. Shipping the dashboard and six Sales requests, plus responsiveness to Sales, do not offset the revenue shortfall without evidence of impact. No documented earlier feedback is supplied to support Below.
Gita — SaraBelow → MeetsOnline invoice payment reached 47%, above the 45% target from a 35% baseline. Reminders v2 was rolled back after four days because Support was not briefed—a meaningful execution failure. We should account for that failure, but not let it erase the payment outcome. The packet also lacks the documented earlier feedback required for Below. Please bring any evidence of rollback impact and prior feedback.
Ines — TomExceeds → MeetsCheckout drop-off fell from 30% to 21%, against 22%: a strong result, modestly above target. Two payment methods shipped, but shipment is not a second outcome. On the supplied evidence, this is solid attainment rather than clear overall outperformance. Incremental adoption, conversion or revenue evidence could change the assessment.
Nora — SaraMeets → ExceedsPayment success reached 95.4%, against 95% from 92%; payment-support tickets fell 35%, against 30%. She exceeded both measurable outcome goals. That warrants the same recognition as comparable performance elsewhere. “Quiet” is not a reason to discount demonstrated impact.

Ratings I propose keeping

PMRatingEvidence and rationale
Ben — RaviExceedsOnboarding fell from 10 to 4.5 days, beating 5 days; activation reached 39%, beating 38% from 30%. Both outcome goals were exceeded.
Dev — RaviExceedsSupport tickets per account fell 27%, against 25%; NPS rose from 31 to 41, against 40. Both outcome goals were exceeded. Mentoring two APMs reinforces the assessment of how he worked; it is not the basis for the rating.
Femi — SaraMeetsWeekly retention reached 46%, exactly on target from 41%; new-account churn fell 22%, against 20%. Strong delivery against goals, with a modest beat on one—not enough evidence of clear overall outperformance.
Hugo — SaraMeets, pending feedback reviewMobile weekly actives grew 3%, against 20%, and app v2 shipped late. “A hard project” does not establish successful outcomes. This is a material performance concern, but the packet lacks documented earlier feedback needed for Below.
Jon — TomMeetsSearch success improved from 60% to 66%, short of 70%. Filters shipped, but no incremental impact is given. There is neither evidence for Exceeds nor documented earlier feedback supporting Below. The remaining outcome gap needs explicit follow-up.
Kai — TomMeetsRefund rate reached 3.1%, narrowly missing 3% from 4%; fraud losses fell 24%, beating 20%. Mixed attainment supports Meets.
Lena — TomBelowListings grew 9%, against 25%, and seller analytics did not launch. The packet documents feedback in Q2 and Q3 1:1s. Both the performance evidence and earlier-feedback requirement are present.
Mo — TomMeetsTime to first sale fell from 21 to 15 days, narrowly missing 14 days; seller NPS increased 6 points, against 5. Mixed attainment and good stakeholder work support Meets.

Note to Ravi

Ravi, all five of your submissions are above Meets, but the evidence supports that distinction for Ben and Dev—not Aisha, Chloe or Emma. In those three cases, the write-ups emphasize shipping, responsiveness or visibility despite substantial outcome misses.

Please revise those rationales around goals, actual results and attributable impact, and bring any missing evidence or earlier feedback. This is not a quota correction: strong results should earn high ratings regardless of manager. We need the same evidentiary bar across teams.

Proposed distribution: 10 Meets, 3 Exceeds, 1 Below; no Greatly exceeds. This is a consequence of the individual assessments, not a forced curve.

Grades and run details

Decision model 96 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly80%
  • passAddresses the actual decision74%
  • passRespects explicit constraints56%
  • passIdentifies material uncertainty88%
  • passAvoids unsupported claims67%
  • passProduces the required deliverable89%
  • passCatches the inflated team100%
  • passRescues Gita from one bad launch100%
  • passNotices who's underrated100%
  • passClear and direct, with care100%
  • passJudges outcomes, not activity100%
  • partialNames gaps you could see56%
  • passWeighs the whole period82%
Run
Run
#1
Time to output
32 s
Submitted
5 Oct 2026
GPT-6.1 SolAPI

Usable with a quick edit13 of 13 checks passed

The pre-read is well-structured, uses evidence correctly, and makes clear proposals. A PM could use it with at most light editing for tone or emphasis, without re-checking facts or redoing reasoning.

Output

Thursday calibration pre-read

From: Director of Product, Northgate Scope: 14 PMs across Ravi, Sara and Tom Status: Facilitator proposals—not final ratings. Managers should respond with additional evidence before we finalize.

Calibration approach

We will assess outcomes against goals first, then how the work was done. Shipping, responsiveness, strong specifications and leadership visibility are relevant context, but do not substitute for customer or business outcomes.

Exceeds requires written evidence of clear outperformance, not simply completing planned work. Below requires both evidence of underperformance and documented earlier feedback about the gap. Missing feedback does not make weak performance satisfactory; it does mean we cannot support Below on the submitted record.

Ratings I propose changing

PMProposed changeEvidence and rationale
Aisha — RaviGreatly exceeds → MeetsTrial conversion reached 8.4% against an 11% goal, from an 8% baseline. Self-serve billing shipped, but no resulting business or customer impact is supplied. Nine features, energy and leadership enthusiasm do not establish exceptional outcomes. The conversion miss is substantial; without documented earlier feedback, the record also does not support Below.
Chloe — RaviExceeds → MeetsAPI usage grew 4% against a 20% goal. The partner portal launched on time, but its adoption or impact is not documented. Strong specifications and timely delivery do not offset the outcome shortfall sufficiently to support Exceeds.
Emma — RaviExceeds → MeetsExpansion revenue grew 6% against a 15% goal. The usage dashboard and six Sales requests shipped, but there is no evidence that they delivered additional outcomes. Responsiveness to Sales is positive working behavior, not evidence of exceeding the revenue goal.
Gita — SaraBelow → MeetsOnline invoice payment reached 47% against a 45% goal, from 35%—a strong outcome. Reminders v2 was rolled back after four days because Support was not briefed, a material execution and coordination failure. However, the submission contains no documented earlier feedback on that gap, which is required for Below. Meets recognizes the mixed record; it does not excuse the launch failure.
Ines — TomExceeds → MeetsCheckout drop-off fell to 21% against a 22% goal, from 30%, and two payment methods shipped. This is successful delivery with some outcome overperformance. On the current record, however, modest overdelivery on checkout plus a launch does not establish clear overall outperformance. Adoption, incremental conversion or revenue from the payment methods could strengthen the case for Exceeds.
Nora — SaraMeets → ExceedsPayment success reached 95.4% against a 95% goal, from 92%; payment support tickets fell 35% against a 30% reduction goal. Both measured outcomes surpassed their targets. This is comparable to the outcome-based cases supporting Ben and Dev. “Quiet” should neither diminish nor enhance the rating.

Ratings I propose keeping

PMKeepEvidence and rationale
Ben — RaviExceedsOnboarding fell to 4.5 days against a five-day goal, from ten; activation reached 39% against 38%, from 30%. Both customer outcomes exceeded their targets.
Dev — RaviExceedsSupport tickets per account fell 27% against a 25% reduction goal; NPS reached 41 against 40, from 31. Both outcomes surpassed targets. Mentoring two APMs strengthens the assessment of how the work was done, but is not the primary justification.
Femi — SaraMeetsWeekly retention reached 46%, exactly the goal, from 41%; new-account churn fell 22% against a 20% reduction goal. Strong goal attainment, with some overdelivery, supports Meets rather than a clear-outperformance rating.
Hugo — SaraMeetsMobile v2 shipped late, and mobile weekly actives grew only 3% against a 20% goal. “Delivered a hard project” does not establish outcome success. This is a significant concern, but no documented earlier feedback is supplied to support Below.
Jon — TomMeetsSearch success improved from 60% to 66% against a 70% goal; filters shipped. Meaningful progress, but the principal outcome remained short of target.
Kai — TomMeetsRefund rate reached 3.1% against a 3% goal, from 4%; fraud losses fell 24% against a 20% reduction goal. One near miss and one exceeded outcome support Meets overall.
Lena — TomBelowListings grew 9% against a 25% goal, and seller analytics did not launch. The submission explicitly cites documented Q2 and Q3 1:1 feedback. Both the performance gap and earlier-feedback requirement are evidenced.
Mo — TomMeetsTime to first sale fell to 15 days against a 14-day goal, from 21; seller NPS rose six points against five. Near attainment on one outcome and overdelivery on the other support Meets. Stakeholder work is positive supporting context.

Note to Ravi

Your submission rates all five PMs above Meets, but three cases rely heavily on shipping, responsiveness or visibility despite substantial misses on their stated outcome goals. Please revisit Aisha, Chloe and Emma using the same outcome standard that supports Ben and Dev. If you have additional attributable impact or documented earlier feedback, bring it before Thursday. We are seeking consistent judgments, not enforcing a quota.

Proposed spread: 10 Meets, three Exceeds and one Below.

Grades and run details

Decision model 96 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly72%
  • passAddresses the actual decision56%
  • passRespects explicit constraints59%
  • passIdentifies material uncertainty72%
  • passAvoids unsupported claims64%
  • passProduces the required deliverable91%
  • passCatches the inflated team100%
  • passRescues Gita from one bad launch100%
  • passNotices who's underrated100%
  • passClear and direct, with care98%
  • passJudges outcomes, not activity100%
  • partialNames gaps you could see40%
  • passWeighs the whole period80%
Run
Run
#1
API response time
37 s
Submitted
6 Oct 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 2

Uses the supplied evidence correctlyMixedRightRight
Gemini 4 Argon · API

The claim that Nora is one of only four PMs who beat two pure outcome goals is not supported by the data; only three PMs (Ben, Dev, Nora) beat both outcome goals.

GPT-6 Astra · ChatGPT

All factual claims about current performance, numbers, and events are taken directly from the supplied proposed_ratings.csv or calibration_guidance.md, with no invented facts.

GPT-6.1 Sol · API

Every factual claim about current performance, results, and documented feedback is taken directly from the supplied proposed_ratings.csv or follows by arithmetic.

Avoids unsupported claimsWrongRightRight
Gemini 4 Argon · API

The statement that Nora is one of only four PMs who beat two pure outcome goals is presented as fact but is an overcount unsupported by the evidence.

GPT-6 Astra · ChatGPT

Interpretations and standards are clearly presented as such, and no cause, forecast, or motivation is stated as fact without support.

GPT-6.1 Sol · API

Interpretations and judgments are clearly presented as the facilitator's proposals, not as established facts, and no unsupported causal claims are made.

All got right 11

Addresses the actual decisionRightRightRight
Gemini 4 Argon · API

Commits clearly to rating changes and keeps, with conditions for pushback (Ravi bringing outcome evidence), and frames the call for the Director.

GPT-6 Astra · ChatGPT

The output commits to specific rating changes and keeps, with evidence, and states that missing outcome evidence or documented feedback could change the assessment.

GPT-6.1 Sol · API

The pre-read commits to specific rating changes and keeps, and states that additional evidence from managers could change the ratings before finalization.

Respects explicit constraintsRightRightRight
Gemini 4 Argon · API

The pre-read addresses Ravi, Sara, and Tom, covers changes with evidence, keeps, and a note to Ravi, and stays within 1,100 words.

GPT-6 Astra · ChatGPT

The output is a pre-read under 1,100 words, includes the ratings to change and keep with evidence, and a note to Ravi, respecting all explicit constraints.

GPT-6.1 Sol · API

The output is a pre-read under 1,100 words, includes the ratings to change and keep with evidence, and a short note to Ravi, as requested.

Identifies material uncertaintyRightRightRight
Gemini 4 Argon · API

Flags the need for documented feedback to move Hugo to Below, asks for written evidence for Nora's Exceeds, notes the Femi borderline, and tells Ravi what evidence would change his ratings.

GPT-6 Astra · ChatGPT

It identifies missing documented feedback for several PMs, asks for rollback impact and prior feedback for Gita, and notes that incremental evidence could change Ines's rating.

GPT-6.1 Sol · API

It names unknowns (e.g., missing adoption/revenue data for Ines, possible additional evidence from Ravi) and says how they would be resolved or change the call.

Produces the required deliverableRightRightRight
Gemini 4 Argon · API

The output is a complete pre-read in the required role, addressing all requested elements, and a PM could act on it with minor edits.

GPT-6 Astra · ChatGPT

The pre-read is complete, in the requested format, within the word limit, and directly usable by the managers with at most light edits.

GPT-6.1 Sol · API

The pre-read is complete, in the right form for the Director of Product, within the word limit, and usable as-is for the calibration meeting.

Catches the inflated teamRightRightRight
Gemini 4 Argon · API

Names Aisha, Chloe, and Emma with their missed outcome goals and proposes lowering them to Meets, while keeping Ben and Dev at Exceeds.

GPT-6 Astra · ChatGPT

It identifies that Aisha, Chloe, and Emma missed outcome goals and proposes lowering them to Meets, while keeping Ben and Dev at Exceeds, and calls out Ravi's emphasis on output.

GPT-6.1 Sol · API

The pre-read names Aisha, Chloe, and Emma with their missed outcome goals and proposes lowering them to Meets, while keeping Ben and Dev at Exceeds.

Rescues Gita from one bad launchRightRightRight
Gemini 4 Argon · API

Raises Gita to Meets citing her main goal beat (47%) and that the Below rested on a single launch without documented earlier feedback.

GPT-6 Astra · ChatGPT

It raises Gita to Meets because she beat her main goal, notes the Below rested on one launch without required documented feedback, and asks for the launch gap to be documented.

GPT-6.1 Sol · API

It raises Gita to Meets, noting she beat her main goal and the Below rating rests on one launch without the required documented earlier feedback, and implies the gap should be documented now.

Notices who's underratedRightRightRight
Gemini 4 Argon · API

Proposes Nora to Exceeds with evidence that she beat both outcome goals (95.4% payment success, -35% tickets).

GPT-6 Astra · ChatGPT

It notices Nora beat both outcome goals and proposes raising her from Meets to Exceeds with the specific evidence.

GPT-6.1 Sol · API

It raises Nora to Exceeds, citing that she beat both outcome goals, and notes that 'quiet' should not affect the rating.

Clear and direct, with careRightRightRight
Gemini 4 Argon · API

States clearly what needs to change (e.g., 'shipping is not an outcome', 'bring written evidence of outcomes'), and the note to Ravi is direct, respectful, and focused on helping.

GPT-6 Astra · ChatGPT

The note to Ravi is direct, specific about what needs to change, and framed to help him succeed; the whole pre-read is clear and actionable.

GPT-6.1 Sol · API

The note to Ravi is direct and respectful, stating exactly what needs to change and what evidence would help, and the whole pre-read is clear and actionable.

Judges outcomes, not activityRightRightRight
Gemini 4 Argon · API

Judges each PM on outcomes against goals first, treating outputs like 'shipped 9 features' as context, not the result.

GPT-6 Astra · ChatGPT

Every rating change and keep is justified by outcomes against goals, with shipping and activity treated as context, not the result.

GPT-6.1 Sol · API

Every rating is justified by outcomes against goals, with shipping and activity treated as context, not the result.

Names gaps you could seeRightRightRight
Gemini 4 Argon · API

Gaps are named with specific metrics (e.g., 8.4% vs 11%, +6% vs +15%) and what good looks like is described as connecting execution to measurable impact.

GPT-6 Astra · ChatGPT

Gaps are described as specific, observable results (e.g., rollback due to unbriefed support, late shipment, missed targets) rather than vague labels.

GPT-6.1 Sol · API

Gaps are described as specific, observable results (e.g., 'trial conversion reached 8.4% against an 11% goal', 'reminders v2 rolled back because support was not briefed').

Weighs the whole periodRightRightRight
Gemini 4 Argon · API

Covers full-year goal results for every PM, does not let Gita's one botched launch dominate, and uses whole-period outcomes to override impressions.

GPT-6 Astra · ChatGPT

The assessment weighs the full year's goal results and explicitly guards against letting the recent rollback erase Gita's overall outcome.

GPT-6.1 Sol · API

The pre-read weighs the full year's goals and results for each PM, and for Gita it explicitly balances the strong main goal with the launch failure, avoiding recency bias.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstraChatGPT91.892.32None
2Gemini 4 ArgonAPI83.792.92None
3GPT-6.1 SolAPI91.884.62None
4Haiku 5.5API83.588.52None
5Opus 5.5Claude84.085.22None
6Gemini 3.8 FlashAPI83.776.92None
7GPT-6 LunaAPI86.177.72None
8Sonnet 5.5API80.074.22None
9Gemini 3.5 Flash-LiteGemini62.749.52None

What you get for the cost

Haiku 5.5 · API scores within 3 points of GPT-6 Astra · ChatGPT at about a fortieth of the cost.

Task score against relative cost for each setup. The table below has the same figures.50607080901001×3×10×30×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Astra · ChatGPT: task score 90, 70× the cheapestGemini 4 Argon · API: task score 89, 11× the cheapestGPT-6.1 Sol · API: task score 87, 13× the cheapestHaiku 5.5 · API: task score 87, 1.7× the cheapestOpus 5.5 · Claude: task score 83, 61× the cheapestGemini 3.8 Flash · API: task score 79, 12× the cheapestGPT-6 Luna · API: task score 79, the cheapestSonnet 5.5 · API: task score 76, 31× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 56, 1.9× the cheapest (1 of 2 outputs estimated)GPT-6 Astra · ChatGPTGemini 4 Argon · APIGPT-6.1 Sol · APIHaiku 5.5 · APIOpus 5.5 · ClaudeGemini 3.8 Flash · APIGPT-6 Luna · APISonnet 5.5 · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and costCost partly estimated (app runs with no usage meter)

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

The PM job

Reviewing the performance of a PM you manage.

Why it matters

Reviews fail by being kind and vague, or by judging a whole year on its worst month. Either way, the person doesn't know what to change.

What good looks like

  • Clear and direct, with care
  • Judges outcomes, not activity
  • Names gaps in observable terms
  • Weighs the whole period, not the last month

Deliberately not measured

  • HR policy compliance beyond what the brief supplies
Capability tested

Feedback and judgement

The failure we’re looking for

Vague, diplomatic feedback, or a verdict driven by one recent event

Grading

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