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?

Measures the modelTask type v1.1 · 3 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

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

From 19 graded outputs by 7 models. 58% were usable with at most a quick edit.

Reliably right

Judges outcomes, not activity100% pass
The rating is based on outcomes against goals (two exceeded, one failed launch) rather than activity or output volume.
GPT-6 Astra · ChatGPT · A PIP, or a bad month?
Owns the manager's part100% pass
Ana plainly acknowledges her Q3 mistake and commits to reviewing goal metrics first in every monthly 1:1.
Opus 5.5 · Claude · Shipped a lot, moved nothing
Holds the PIP to the policy and the record100% pass
It explicitly shows that no earlier documented feedback exists on the gaps Ines named, so a PIP would violate policy, and proposes documented feedback first.
GPT-6 Astra · ChatGPT · A PIP, or a bad month?

Where it slips

Identifies material uncertainty47% pass
Does not explicitly name the specific unknowns that could change the decision (e.g., whether Lukas improves launch readiness); only says to reassess after the relaunch.
GPT-6 Luna · API · A PIP, or a bad month?
Notices who's underrated65% pass
It does not propose Exceeds or ask Sara to justify Meets despite Nora beating both goals; it merely keeps Meets, missing the underrating.
GPT-6 Luna · API · Fourteen ratings, three managers
Catches the inflated team75% 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

The checks come from what the best product leaders have said about doing this job well on Lenny’s Podcast. Each one names the guest it comes from: follow a name to the idea on the Lenny’s Podcast wiki.

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 · Performance reviews
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.
Lenny’s PodcastThe wiki on Performance reviews
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.
Lenny’s PodcastThe wiki on Performance reviews
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.
Lenny’s PodcastThe wiki on Performance reviews

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 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 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 · v1.0 · synthetic · management, calibration, data files, staff-level

The outputs

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

GPT-6 AstraChatGPT100Usable with a quick edit GPT-6.1 SolAPI100Usable with a quick edit Opus 5.5Claude88Needs more work GPT-6 LunaAPI82Needs more work Gemini 3.5 Flash-LiteGemini38Needs more work

Gemini 3.5 Flash-LiteGemini

Needs more than a quick edit2 of 13 checks passed

The output is incomplete, missing the keep list and Ravi note, and cannot be used without redoing the reasoning and filling major gaps.

Output

To: Ravi, Sara, Tom From: Director of Product Subject: Pre-Read: Thursday PM Calibration (Northgate)

Team,

Ahead of our calibration session this Thursday, I have reviewed the proposed ratings, outcomes, and evidence for all 14 PMs across our org.

Per our calibration guidance, we evaluate PMs first on outcomes against goals, not on activity. Shipping features, being responsive to sales, or having great energy are inputs, not results. Furthermore, any rating other than "Meets" requires rigorous, quantifiable evidence of goal achievement, and "Below" ratings require documented prior feedback.

Below is my proposed adjustment of ratings, the rationale and evidence for changes, and the list of ratings we are keeping as submitted.

---

Part 1: Proposed Rating Changes & Rationale

#### 1. Aisha (Manager: Ravi) * Proposed by Manager: Greatly Exceeds * My Proposed Rating: Below * Evidence & Rationale: * Goal 1: Raise trial conversion from 8% to 11% $\rightarrow$ Result: 8.4% (Missed by a wide margin). * Goal 2: Ship self-serve billing $\rightarrow$ Result: Shipped (Violation of guidance: "Shipping is not an outcome"). * Rationale: Aisha missed her primary core metric goal entirely (gaining only 0.4% against a 3% target) and fell back on "shipping" and "great energy" for her second goal. Per our framework, failing the core quantitative goal while relying on output means this is a Below performance. Ravi, we will need to document the performance gap and prior feedback for Aisha in this cycle.

#### 2. Chloe (Manager: Ravi) * Proposed by Manager: Exceeds * My Proposed Rating: Meets * Evidence & Rationale: * Goal 1: Grow API usage 20% $\rightarrow$ Result: +4% (Severe miss). * Goal 2: Launch partner portal $\rightarrow$ Result: Shipped on time

Grades and run details

Decision model 65 · LLM judge 2 of 14 checks

Decision model checks

passUses the supplied evidence correctly70%
passAddresses the actual decision28%
partialRespects explicit constraints20%
failIdentifies material uncertainty88%
passAvoids unsupported claims33%
partialProduces the required deliverable75%
partialCatches the inflated team95%
failRescues Gita from one bad launch83%
failNotices who's underrated95%
passClear and direct, with care55%
passJudges outcomes, not activity98%
passNames gaps you could see69%
passWeighs the whole period21%

Run

Run: #1
Time to output: 3 s
Submitted: 5 Oct 2026

GPT-6 LunaAPI

Needs more than a quick edit11 of 13 checks passed

Nora's rating is not correctly addressed; a PM would need to re-evaluate her case for Exceeds. The output also lacks observable behavior gaps, though that is less critical for a calibration pre-read.

Output

Thursday calibration pre-read

Proposed changes below are for discussion, not final ratings. We’ll assess outcomes against goals first; shipping, effort, and stakeholder praise are not outcomes by themselves. Managers can share additional evidence before ratings are finalized.

Ratings I’d change

Aisha — Greatly exceeds → Meets. Trial conversion reached 8.4% against an 11% goal. Self-serve billing shipped, but shipping is not an outcome, and the cited nine features, energy, and leadership praise don’t establish goal outperformance.
Chloe — Exceeds → Meets. API usage grew 4% against a 20% goal. The partner portal shipped, but no result from the launch is cited; strong specs and on-time delivery don’t establish Exceeds.
Emma — Exceeds → Meets. Expansion revenue grew 6% against a 15% goal. The dashboard shipped, and six Sales requests were delivered, but neither is evidence of the targeted revenue outcome or clear outperformance.
Ines — Exceeds → Meets. Checkout drop-off reached 21% against a 22% goal, while two payment methods shipped. The checkout result beat its target by one point; for the payment-method goal, no outcome beyond shipping is cited. The evidence supports a solid year, not clear outperformance on outcomes.
Gita — Below → Meets on the current record. Online invoices reached 47% against a 45% goal. Reminders v2 launched but was rolled back after four days because Support had not been briefed. That is a serious rollout issue, but the record includes no earlier documented feedback on the gap, as required for Below. Please share any such documentation before calibration closes.

Ratings I’d keep

Ben — Exceeds. Onboarding fell to 4.5 days against a 5-day goal, and activation reached 39% against 38%: both measured outcomes surpassed target.
Dev — Exceeds. Support tickets fell 27% against a 25% goal, and NPS reached 41 against 40. Both outcomes beat their goals; mentoring two APMs is additional contribution.
Femi — Meets. Weekly retention reached its 46% goal, and new-account churn fell 22% against a 20% goal.
Hugo — Meets, with a material concern to discuss. The app shipped late and mobile weekly actives rose 3% against a 20% goal. The evidence shows a missed outcome; no earlier documented feedback is cited to support Below.
Jon — Meets. Search success reached 66% against 70%, and filters shipped. This is progress, not goal attainment; the record does not establish a case for a non-Meets rating.
Kai — Meets. Fraud losses fell 24% against a 20% goal; refund rate was 3.1% against a 3% goal. Mixed results support Meets.
Lena — Below. Listings grew 9% against a 25% goal, and seller analytics did not launch. Earlier feedback on the gap is documented in Q2 and Q3 1:1s, meeting the Below requirement.
Mo — Meets. Time to first sale was 15 days against a 14-day goal; seller NPS rose 6 points against a 5-point goal. One goal narrowly missed, the other was exceeded.
Nora — Meets. Payment success reached 95.4% against a 95% goal, and support tickets fell 35% against a 30% goal. Both outcomes beat target, but the evidence supports Meets rather than clear outperformance.

Note to Ravi

Ravi, several of your proposed high ratings cite launches, feature volume, responsiveness, or praise alongside missed outcome goals. For calibration, please anchor any Exceeds or Greatly Exceeds case in clear outcome outperformance; shipping alone is not enough. Please bring any additional outcome evidence you believe changes these recommendations.

Grades and run details

Decision model 92 · LLM judge 11 of 14 checks

Decision model checks

passUses the supplied evidence correctly87%
passAddresses the actual decision52%
passRespects explicit constraints39%
partialIdentifies material uncertainty31%
passAvoids unsupported claims69%
passProduces the required deliverable62%
passCatches the inflated team100%
passRescues Gita from one bad launch100%
partialNotices who's underrated41%
passClear and direct, with care85%
passJudges outcomes, not activity97%
passNames gaps you could see78%
passWeighs the whole period64%

Run

Run: #1
API response time: 25 s
Submitted: 5 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.