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

  1. 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?
  2. 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
  3. 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

  1. 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?
  2. 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
  3. 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.

  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.

  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.

  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.

  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.

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 2 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 Ana Ruiz, Group PM at Plotwise. Write Theo Brandt's annual review: the review Theo will read, with his strengths, what to work on, his overall rating on our scale, and his goals for the next half. No more than 800 words. What we know is below.

What the model was given6 items: Our rating scale, Theo's goals for the year, Results, Usage of what shipped, Peer feedback, Your 1:1 notes
Our rating scaleBelow expectations, Meets expectations, Exceeds expectations, Greatly exceeds expectations. Calibration asks for written evidence for any rating other than Meets.
Theo's goals for the year1. Raise the share of teams using Timeline view each week from 22% to 30%. 2. Cut the time new teams take to build their first plan from 3 days to 1.
ResultsTimeline weekly use: 22% to 23%. Time to first plan: 3.0 days to 2.6. Theo's squad shipped 14 features, 12 of them on time.
Usage of what shippedSix of the 14 features are used by fewer than 2% of teams. Five were requests from Sales; two of those were for a single customer each.
Peer feedbackEngineering lead: 'Theo keeps the team unblocked, his specs are clear, and he never misses a sprint review.' Designer: 'Great to work with. I wish we'd tested more before building; we shipped three things I'm not sure anyone uses.' Sales: 'Very responsive: shipped five of our asks.'
Your 1:1 notesMost 1:1s covered delivery and stakeholder updates. In the Q3 1:1, Theo asked whether he was on track for his goals. Your note says: 'Told him yes, great velocity.' You never discussed that the goal metrics weren't moving.
What a strong answer doesThe answer key the graders mark against

A review that rates Theo Meets expectations (not Exceeds): his execution and collaboration are genuinely strong, but neither goal moved meaningfully (23% against 30%; 2.6 days against 1), and six of 14 features are barely used. Says so clearly and kindly. Names specific, observable things to change: test before building (the designer's point), say no to single-customer requests, and judge work by the goal metrics. Owns the manager's part: Ana told him in Q3 he was on track, so this shouldn't land as a surprise without acknowledging that, and she commits to discussing the goal metrics in 1:1s. Sets next-half goals as outcomes, with the support on offer.

Critical failures (cap the score at 40)
  • Rates Theo Exceeds expectations or higher on the strength of what he shipped
Task

Mid-level · v1.0 · synthetic · B2B SaaS, feedback, management

The outputs

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

Needs more than a quick edit9 of 12 checks passed

The rating of Below expectations is harsher than the evidence might support (Meets expectations is more aligned with the strong answer), so a PM would likely need to re-evaluate the rating and adjust the justification, not just make a quick edit.

Output

Theo Brandt — Annual Review

Ana Ruiz, Group PM, Plotwise

Overall rating: Below expectations

Theo, you brought strong execution discipline, clear communication, and dependable partnership to the team this year. However, our two primary goals were to increase weekly Timeline use and shorten the time new teams take to build their first plan. We made only modest progress on both:

  • Weekly Timeline use: increased from 22% to 23%, against a goal of 30%.
  • Time to first plan: decreased from 3.0 days to 2.6 days, against a goal of 1 day.

Those results are the basis for this rating. Your squad shipped 14 features, 12 on time, but delivery volume and timeliness did not translate into the customer outcomes we committed to achieve. Six of those features are used by fewer than 2% of teams. Low adoption does not automatically make a feature unsuccessful, but it does require us to establish what value it created and whether it justified the investment.

I also owe you an acknowledgment of my part in this. In Q3, you explicitly asked whether you were on track, and I told you yes, citing great velocity. That was misleading: I should have distinguished delivery performance from progress against your goals. Our 1:1s focused too heavily on delivery and stakeholder updates, and I did not address the stalled metrics. You should not be hearing this assessment for the first time in an annual review. That management failure is mine; it does not erase the outcome gap, but it matters to how we understand the year and move forward.

Strengths

Reliable execution and engineering partnership. You keep the team unblocked and provide clear specs. The engineering lead specifically highlighted both, along with your consistent participation in sprint reviews. Shipping 12 of 14 features on time reflects real delivery discipline.

Strong collaboration and responsiveness. Design describes you as great to work with, and Sales values your responsiveness. You create productive working relationships and follow through on commitments. Those strengths are important foundations for effective product leadership.

What to work on

Shift from managing delivery to managing outcomes. The roadmap needs a clearer connection to Timeline adoption and first-plan speed. For each meaningful investment, define the customer problem, expected metric movement, and how we will assess success after launch. When the metrics are not moving, bring that into our discussions early and recommend a change in approach.

Validate demand before committing engineering capacity. Design’s concern that we should have tested more, together with the low usage of several shipped features, points to a need for stronger discovery. Use prototypes, customer conversations, and lightweight experiments to test the riskiest assumptions before building.

Make prioritization trade-offs explicit. Five shipped features were Sales requests, including two for individual customers. Those requests may have legitimate business value, but responsiveness alone is not a sufficient reason to prioritize them. Assess their customer reach, strategic or commercial value, cost, and opportunity cost—and make clear when an exception is worth taking.

Goals for the next half

  1. Increase weekly Timeline use from 23% to 27%. Identify the main adoption barriers, test proposed solutions, and prioritize the interventions with the strongest evidence.
  2. Reduce time to first plan from 2.6 days to 1.8 days. Diagnose where new teams stall and measure whether each onboarding improvement reduces that friction.
  3. Strengthen investment decisions. For every substantial roadmap item, document the problem, supporting evidence, intended outcome, and success measure before committing to build. Review results within six weeks of launch and decide whether to iterate, expand, or stop.

We will confirm metric definitions and tracking at the start of the half, then review progress monthly. I will give you explicit feedback on whether you are on track, help resolve prioritization conflicts, and avoid treating velocity as a substitute for impact.

You have a strong execution foundation. The next step is to apply that strength to fewer, better-supported bets that measurably improve customers’ experience.

Grades and run details

Decision model 96 · LLM judge 9 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly78%
  • passAddresses the actual decision49%
  • passRespects explicit constraints3%
  • partialIdentifies material uncertainty56%
  • passAvoids unsupported claims47%
  • passProduces the required deliverable80%
  • passOwns the manager's part100%
  • passSets next goals as outcomes, with support99%
  • passClear and direct, with care93%
  • passJudges outcomes, not activity99%
  • passNames gaps you could see92%
  • passWeighs the whole period80%
Run
Run
#1
API response time
31 s
Submitted
5 Oct 2026

Needs more than a quick edit10 of 12 checks passed

The rating of Below expectations is a significant judgment call that a PM would likely need to re-evaluate against the evidence and the strong answer's suggested Meets rating, requiring more than a quick edit.

Output

Theo,

You bring strong execution and partnership to the team. The engineering lead called out that you keep work unblocked, write clear specs, and consistently show up for sprint reviews. Your squad shipped 14 features, with 12 delivered on time, and Sales described you as very responsive. Those are meaningful strengths.

Overall rating: Below expectations

This rating reflects the gap between the outcomes we set and the outcomes delivered. Timeline weekly use increased from 22% to 23%, against a goal of 30%. Time to first plan improved from 3.0 days to 2.6, against a goal of 1 day. Delivery was strong, but the intended customer outcomes moved only modestly.

There is also a signal to examine in the shipped work: six of the 14 features are used by fewer than 2% of teams. Five were Sales requests, including two for a single customer each. Low usage does not automatically mean a feature was the wrong investment—some work serves a specific strategic need—but we need clearer evidence and rationale for those choices. Your designer also wished we had tested more before building, and questioned whether three shipped items were useful. Taken together, this points to an opportunity to strengthen discovery, prioritization, and post-launch learning—not simply to ship more.

I also need to own a miss on my part. In our Q3 1:1, you asked whether you were on track, and I told you yes based on velocity. I should have discussed the goal metrics and the gap then. I didn’t, and that deprived you of a timely chance to adjust. I’m sorry. Going forward, I’ll make our 1:1s include progress against outcomes, not just delivery and stakeholder updates.

What to work on

  • Start with the user problem and intended outcome; use research and testing to check the problem and proposed solution before committing to build.
  • Balance stakeholder responsiveness with product judgment. For Sales requests, make the target customer, broader strategic value, and trade-offs explicit.
  • Define success measures before launch, then review adoption and learn from the results. Treat low usage as a prompt to investigate, not as a verdict by itself.

Goals for the next half

  1. Raise Timeline weekly use from 23% to 26% by the end of the half. Identify the biggest adoption barriers and review usage trends regularly.
  2. Reduce time to first plan from 2.6 days to 2.0 days. Instrument the activation journey, find the main points of friction, and prioritize improvements against them.
  3. For each substantial feature bet, document the target user, problem evidence, expected outcome, and success measure before development. Validate larger bets with target teams before building, and review adoption after launch so we can iterate or change course.

I believe your execution strengths give you a solid base for this next step: making sure the team’s delivery translates into measurable customer value.

Grades and run details

Decision model 96 · LLM judge 10 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly81%
  • passAddresses the actual decision51%
  • passRespects explicit constraints11%
  • partialIdentifies material uncertainty39%
  • passAvoids unsupported claims67%
  • passProduces the required deliverable90%
  • passOwns the manager's part100%
  • passSets next goals as outcomes, with support56%
  • passClear and direct, with care88%
  • passJudges outcomes, not activity96%
  • passNames gaps you could see84%
  • passWeighs the whole period76%
Run
Run
#1
API response time
19 s
Submitted
5 Oct 2026

Usable with a quick edit10 of 12 checks passed

The review is well-structured and evidence-based; a quick edit to add specific support for goals would make it fully ready.

Output

To: Theo Brandt From: Ana Ruiz, Group PM Date: Annual Review Rating: Meets expectations

*

Hi Theo,

It’s time for your annual review. First, I want to acknowledge your hard work and dedication over the past year. You are an exceptionally reliable Product Manager, and your squad’s execution has been rock-solid.

Strengths

Your delivery engine is second to none. As your engineering lead noted, your specs are clear, you keep the team unblocked, and you never miss a sprint review. Shifting 14 features in a year—with 12 delivered strictly on time—is an impressive output that demonstrates strong project management and technical collaboration.

You are also a fantastic cross-functional partner. Sales loves working with you because you are responsive and action-oriented, and our design lead appreciates your steady communication and positive team dynamic. You cultivate a smooth, friction-free environment for your squad, which is a rare and valuable skill.

Areas for Improvement

While your output and velocity are stellar, we need to evolve how we measure success. Our primary focus this past year was on driving core product metrics, and we fell short of our targets: * Timeline View Adoption: Moved only from 22% to 23% (against a target of 30%). * Time to First Plan: Improved marginally from 3.0 days to 2.6 days (against a target of 1.0).

Additionally, six of the 14 features we shipped are currently used by fewer than 2% of teams. Five of these were direct requests from Sales, including two built for single customers.

As a PM, your role isn't just to say "yes" to stakeholders and push code across the finish line; it’s to interrogate the why, protect our roadmap, and obsess over the impact of what we build. Relying too heavily on outbound feature requests without validating their broader utility led us to invest engineering cycles in low-impact work.

I also want to own my part in this: looking back at our 1:1 notes, I praised your high velocity when you asked if you were on track, and I failed to course-correct early enough regarding the stagnant metric goals. Moving forward, we will be much sharper about tying your weekly execution directly to our key results.

Overall Rating

Meets expectations.

Your execution, reliability, and stakeholder management are top-tier. To reach the next level, you need to shift your mindset from output (features shipped on time) to outcomes (metrics moved and customer value realized).

*

Goals for the Next Half

To help bridge that gap, your goals for the next H are focused on strategic impact and validation:

  1. Improve Core Activation Metrics: Reduce the time new teams take to build their first plan from 2.6 days to 1.5 days, focusing on self-serve friction reduction rather than custom feature additions.
  2. Increase Feature ROI & Validation: Implement a mandatory pre-build discovery and validation process for all major initiatives. Ensure that 100% of shipped features have a defined success metric and achieve at least 15% adoption within 30 days of launch.
  3. Stakeholder Alignment Strategy: Partner with Sales leadership to create a structured framework for evaluating custom requests, ensuring we prioritize scalable product improvements over single-customer concessions without sacrificing our partnership.

Thank you for everything you do for Plotwise, Theo. I’m looking forward to supporting you as you tackle these new challenges in the year ahead.

Best, Ana

Grades and run details

Decision model 83 · LLM judge 11 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly60%
  • passAddresses the actual decision78%
  • passRespects explicit constraints9%
  • failIdentifies material uncertainty97%
  • partialAvoids unsupported claims54%
  • passProduces the required deliverable95%
  • passOwns the manager's part92%
  • partialSets next goals as outcomes, with support32%
  • passClear and direct, with care85%
  • passJudges outcomes, not activity45%
  • passNames gaps you could see69%
  • passWeighs the whole period75%
Run
Run
#1
Time to output
5 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.

Where they differ 2

Addresses the actual decisionMixedMixedRight
GPT-6.1 Sol · API

The output does not explicitly state what result or condition would change the rating.

GPT-6 Luna · API

The output commits to a rating but does not state what result or condition would change that rating.

Gemini 3.5 Flash-Lite · Gemini

Commits clearly to a rating of Meets expectations, with strengths and areas for improvement.

Sets next goals as outcomes, with supportMixedRightWrong
GPT-6.1 Sol · API

The third goal ('Strengthen investment decisions') is a process goal without a baseline or target, not an outcome goal.

GPT-6 Luna · API

The goals are outcome-based with baselines and targets, and the earlier commitment to include progress against outcomes in 1:1s provides specific support.

Gemini 3.5 Flash-Lite · Gemini

Goals are partly process-oriented (implement a mandatory process, create a framework) and lack specific support from Ana beyond a general offer.

All got wrong 1

Identifies material uncertaintyWrongWrongWrong
GPT-6.1 Sol · API

The output does not name any unknowns that could change the decision or how they would be resolved.

GPT-6 Luna · API

The output does not name specific unknowns that could change the rating decision or say how they would be resolved.

Gemini 3.5 Flash-Lite · Gemini

Does not name any unknowns that could change the rating or how they would be resolved.

All got right 9

Uses the supplied evidence correctlyRightRightRight
GPT-6.1 Sol · API

All statements about the current situation are directly from the brief or supplied context.

GPT-6 Luna · API

All factual claims about the current situation are directly supported by the supplied context.

Gemini 3.5 Flash-Lite · Gemini

All factual statements about the current situation are correctly taken from the brief and supplied context.

Respects explicit constraintsRightRightRight
GPT-6.1 Sol · API

The review is under 800 words, addresses Theo, and includes all required sections.

GPT-6 Luna · API

The review is within 800 words, addresses Theo, includes all required sections, and uses the rating scale with evidence for a non-Meets rating.

Gemini 3.5 Flash-Lite · Gemini

The review is under 800 words, includes all required elements, and is addressed to Theo.

Avoids unsupported claimsRightRightRight
GPT-6.1 Sol · API

Interpretations are presented as suggestions or conclusions from the evidence, not as unsupported facts.

GPT-6 Luna · API

Interpretations and conclusions are clearly based on the evidence and not presented as established fact without support.

Gemini 3.5 Flash-Lite · Gemini

The causal claim is supported by the evidence of low usage, Sales requests, and designer's feedback about testing.

Produces the required deliverableRightRightRight
GPT-6.1 Sol · API

The review includes strengths, areas to work on, an overall rating, and next-half goals, and is usable as is.

GPT-6 Luna · API

The annual review is complete, in the right form, for the right reader, and within the word limit.

Gemini 3.5 Flash-Lite · Gemini

The output is a complete annual review with strengths, areas to improve, rating, and goals, within the word limit.

Owns the manager's partRightRightRight
GPT-6.1 Sol · API

Ana acknowledges telling Theo he was on track in Q3, admits the management failure, and commits to giving explicit feedback and avoiding velocity as a substitute for impact.

GPT-6 Luna · API

Ana explicitly acknowledges telling Theo he was on track in Q3 without discussing the goal gap, apologizes, and commits to reviewing outcomes in future 1:1s.

Gemini 3.5 Flash-Lite · Gemini

Acknowledges that Ana praised velocity and didn't course-correct, and commits to tying execution to key results.

Clear and direct, with careRightRightRight
GPT-6.1 Sol · API

The review states plainly what needs to change, with specific behaviors and the support on offer.

GPT-6 Luna · API

The review is direct, kind, and states specific changes expected, with the manager's support clearly offered.

Gemini 3.5 Flash-Lite · Gemini

Directly states the need to shift from output to outcomes, validate before building, and not just say yes to Sales.

Judges outcomes, not activityRightRightRight
GPT-6.1 Sol · API

The rating is based on the outcome metrics against the goals, not on the number of features shipped.

GPT-6 Luna · API

The rating is explicitly based on the gap between goal outcomes and actual results, not on the number of features shipped.

Gemini 3.5 Flash-Lite · Gemini

Rating is based on goal metrics not moving and low feature usage, not on features shipped.

Names gaps you could seeRightRightRight
GPT-6.1 Sol · API

Each area to improve describes specific, observable behaviors (e.g., bring stalled metrics into discussions, use prototypes to test assumptions, assess customer reach and opportunity cost).

GPT-6 Luna · API

Each area to improve describes specific, observable behaviors (e.g., test before building, document target user and success measures) and what good looks like.

Gemini 3.5 Flash-Lite · Gemini

Gaps are described as relying on outbound requests without validation, and what good looks like is interrogating the why and obsessing over impact.

Weighs the whole periodRightRightRight
GPT-6.1 Sol · API

The review references the full year, including Q3 1:1 and the entire set of shipped features, without letting one event dominate.

GPT-6 Luna · API

The review covers the full year, referencing Q3 1:1, full-year metrics, and the entire set of shipped features, without over-weighting any single event.

Gemini 3.5 Flash-Lite · Gemini

Covers the full year, references Q3 1:1, and doesn't let one event dominate.

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 · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT95.894.93None
2Opus 5.5withClaude93.290.13None
3Sonnet 5.5withAPI95.884.62None
4GPT-6.1 SolwithAPI97.984.62None
5GPT-6 LunawithAPI95.884.62None
6Gemini 3.8 FlashwithAPI83.357.72None
7Gemini 3.5 Flash-LitewithGemini73.258.63None

About the task

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