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 17 graded outputs by 7 models. 59% 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 uncertainty43% 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?
Catches the inflated team58% 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
Notices who's underrated67% pass
Nora is not mentioned, so the output does not notice she is underrated.
Gemini 3.5 Flash-Lite · Gemini · 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

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

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

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

PM	Proposed change	Evidence and rationale
Aisha — Ravi	Greatly exceeds → Meets	Trial 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 — Ravi	Exceeds → Meets	API 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 — Ravi	Exceeds → Meets	Expansion 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 — Sara	Below → Meets	Online 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 — Tom	Exceeds → Meets	Checkout 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 — Sara	Meets → Exceeds	Payment 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

PM	Rating	Evidence and rationale
Ben — Ravi	Exceeds	Onboarding fell from 10 to 4.5 days, beating 5 days; activation reached 39%, beating 38% from 30%. Both outcome goals were exceeded.
Dev — Ravi	Exceeds	Support 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 — Sara	Meets	Weekly 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 — Sara	Meets, pending feedback review	Mobile 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 — Tom	Meets	Search 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 — Tom	Meets	Refund rate reached 3.1%, narrowly missing 3% from 4%; fraud losses fell 24%, beating 20%. Mixed attainment supports Meets.
Lena — Tom	Below	Listings 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 — Tom	Meets	Time 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 100 · LLM judge 14 of 14 checks

Decision model checks

passUses the supplied evidence correctly83%
passAddresses the actual decision87%
passRespects explicit constraints46%
passIdentifies material uncertainty88%
passAvoids unsupported claims64%
passProduces the required deliverable85%
passCatches the inflated team100%
passRescues Gita from one bad launch100%
passNotices who's underrated100%
passClear and direct, with care98%
passJudges outcomes, not activity100%
passNames gaps you could see73%
passWeighs the whole period79%

Run

Run: #1
Time to output: 32 s
Submitted: 5 Oct 2026

Check by check

Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Got right 13

Uses the supplied evidence correctlyRight

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.

Addresses the actual decisionRight

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

Respects explicit constraintsRight

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.

Identifies material uncertaintyRight

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.

Avoids unsupported claimsRight

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

Produces the required deliverableRight

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

Catches the inflated teamRight

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.

Rescues Gita from one bad launchRight

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.

Notices who's underratedRight

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

Clear and direct, with careRight

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.

Judges outcomes, not activityRight

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

Names gaps you could seeRight

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

Weighs the whole periodRight

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

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Provisional The checklist is still being calibrated against our PM.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6 AstrawithChatGPT	95.8	94.9	3	None
2	Opus 5.5withClaude	93.2	90.1	3	None
3	Sonnet 5.5withAPI	95.8	84.6	2	None
4	GPT-6.1 SolwithAPI	97.9	84.6	2	None
5	GPT-6 LunawithAPI	95.8	84.6	2	None
6	Gemini 3.8 FlashwithAPI	83.3	57.7	2	None
7	Gemini 3.5 Flash-LitewithGemini	73.2	58.6	3	None

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