Tasks / Leadership

Hire a PM

Can the model design a hiring process that finds the right PM, and make the call on real candidates from the evidence?

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. 79% were usable with at most a quick edit.

Reliably right

  1. Produces the required deliverable100% pass
    Complete recommendation memo with evidence, risks, and actionable next steps, usable as is.
    GPT-6 Luna · API · Two finalists, one Group PM role
  2. Judges on evidence, not presence100% pass
    Judges on concrete evidence (metric moved, team credit, learning from failure) and treats impressions like presence as weak signals.
    GPT-6 Luna · API · Two finalists, one Group PM role
  3. Tests what the last hire failed at100% pass
    The loop includes an influence simulation with the Head of Sales and a scorecard dimension on influence without authority, directly testing what the last hire failed at.
    GPT-6.1 Sol · API · A loop for the first growth PM

Where it slips

  1. Spots the interviewer pattern65% pass
    Does not notice that VP Engineering scores big-tech candidates higher and those hires were rated lower; only reports a negative correlation without the pattern.
    GPT-6 Luna · API · What our interviews predict
  2. Avoids unsupported claims70% pass
    The claim 'A growth PM in a senior role is likely to be exactly that kind of person' is presented as fact without evidence and is not labelled as an assumption.
    Opus 5.5 · Claude · A loop for the first growth PM
  3. Identifies material uncertainty72% pass
    The output does not name unknowns that could change the hiring decision or the loop design, nor does it say how they would be resolved.
    Sonnet 5.5 · API · A loop for the first growth PM

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. Judges on evidence, not presence

    Does the output judge candidates, or design the process to judge them, on specific evidence of results they drove and how they worked with their team, rather than impressions such as presence, confidence or polish?

    Passes when Asks for or weighs concrete evidence: the metric a candidate moved, the part they played, how they shared credit, what they learned from a failure. Impressions are treated as weak signals.

  2. Defines good for this role first

    Does the output set out what good looks like for this specific role (the competencies that matter most at this level, and what strong and weak look like) before judging candidates or designing interviews?

    Passes when Names the competencies weighted for this role and level, with strong and weak signals, and the judgement or the process follows from them.

  3. Keeps each judgement independent

    Does the output keep each interviewer's judgement independent (written down before the group discusses it), and guard against the loudest voice or the first speaker deciding?

    Passes when Requires or relies on written, independent evaluations before discussion, and discounts views that changed under group pressure.

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 Director of Product at Medvia, and you're the hiring manager for a Group PM. The debrief was yesterday. Write your recommendation to Rosa Lindqvist, our VP Product, in no more than 1,000 words: who to hire (Dev, Joy, or neither), the evidence for it, the risks, and what you'd check before making the offer. The CEO wants a decision by Friday. The interview notes and everything else are below.

What the model was given6 items: The role and the scorecard (agreed before interviews began), Dev Malhotra: interview notes, Joy Adeyemi: interview notes, Scores (1 to 4, entered after the debrief discussion), References, Compensation
The role and the scorecard (agreed before interviews began)Group PM for patient payments, leading three PMs. Must set the strategy for patient payments, lift the online payment rate (now 41%), and lead PMs while working closely with Finance and Compliance. Weighting: execution and influence 35%, product sense 25%, analytics 20%, people leadership 20%.
Dev Malhotra: interview notesProduct sense (Head of Design): 'Brilliant. Redesigned our statement flow live, lots of ideas, very creative.' Notes show six feature ideas in the first five minutes, before asking who pays and why. Analytics (Data lead): 'Strong.' Notes: asked what metric he moved at his last job, he said 'we grew the payments platform massively'; pressed, 'the team shipped 40+ features'. No number. Leadership (VP Engineering): 'Confident, inspiring.' Notes: every story in the first person; said his last PM team 'struggled to keep up with me'. Presentation: 'the best deck we've ever seen'.
Joy Adeyemi: interview notesProduct sense (Head of Design): 'Solid, slower.' Notes: spent 15 minutes asking who pays and why they don't pay online, then proposed one change: text-to-pay for the 58% of patients who pay by phone, with its trade-offs. Analytics (Data lead): notes say she raised payment completion at her last company from 33% to 47% over 18 months, and walked through an experiment that failed and what it taught her. Leadership (VP Engineering): notes say she coached a struggling PM into a different role, promoted another, and named her engineers when describing wins. Debrief comments: 'Not much presence.' 'Not sure she'd command the room with the CFO.'
Scores (1 to 4, entered after the debrief discussion)Dev: 4, 4, 4, 4. Joy: 3, 3, 3, 3. The Head of Design spoke first in the debrief. The VP Engineering's own notebook, written before the debrief, says: 'Joy: 4, strongest leader we've seen.' They entered a 3 after the discussion.
ReferencesDev: two references, both people who report to him now. Rosa knows someone at his previous company, who says: 'great in front of execs; his team turned over a lot.' Joy: her former manager and a peer, both strongly positive. Nobody has asked either reference about working with a CFO.
CompensationBand $220k to $250k. Dev is asking for $245k, Joy for $230k.
What a strong answer doesThe answer key the graders mark against

Recommends Joy, or at minimum not Dev on this evidence. Weighs both against the agreed scorecard rather than presence or the deck: Joy gives a metric she moved (33% to 47%), learns from failure, credits her team and coaches PMs, which fits execution, analytics and people leadership; Dev couldn't name a metric he moved, frames everything as 'I', jumped to features before asking about users, and the back channel reports high team turnover, all red flags. Points out that the scores were entered after a discussion the Head of Design opened, and the VP Engineering moved Joy from 4 to 3, so they show anchoring, not independent judgement. Treats 'presence with the CFO' as the one real open question and proposes a targeted reference check on how Joy worked with Finance leaders before the offer. Suggests the next loop collect written scores before the debrief.

Critical failures (cap the score at 40)
  • Recommends hiring Dev on the strength of the debrief scores or the presentation, without addressing that he couldn't name a metric he moved
Task

Staff-level · v1.0 · synthetic · healthcare, fintech, hiring, staff-level

The outputs

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

Usable with a quick edit12 of 12 checks passed

The output is well-structured, evidence-based, and actionable. A PM could use it with minor wording edits, without re-checking evidence or redoing reasoning.

Output

To: Rosa Lindqvist From: Director of Product Re: Group PM, Patient Payments: recommendation

Recommendation

Hire Joy Adeyemi, conditional on two checks I can finish before Friday (below). I would not hire Dev on the current evidence. If the checks on Joy go badly, I would hold the search open rather than default to Dev, because his file has gaps too.

Why the recorded scores mislead

The recorded scores are Dev 4/4/4/4 and Joy 3/3/3/3, which would make this an easy call for Dev. But I don't trust them, for three reasons:

  • They were entered after the debrief discussion, and the Head of Design spoke first. That is a classic anchoring setup.
  • The VP Engineering's own pre-debrief notebook said "Joy: 4, strongest leader we've seen." They entered a 3 after the discussion. Uniform 3s for Joy and uniform 4s for Dev look like a group narrative, not independent assessments.
  • Dev's scores aren't supported by the interview notes (below).

Evidence, by scorecard area

Analytics (20%) - Dev was asked what metric he moved and answered "we grew the payments platform massively." When pressed, he said "the team shipped 40+ features." That is output, not outcome, and he never gave a number. The Data lead's "Strong" doesn't match their own notes. - Joy raised payment completion from 33% to 47% over 18 months and walked through an experiment that failed and what it taught her. That is directly relevant to our 41% online payment rate.

Product sense (25%) - Dev generated six feature ideas in five minutes, before asking who pays and why. The redesign was creative, but it started from solutions. - Joy spent 15 minutes on who pays and why they don't pay online. She then proposed one change, text-to-pay for the 58% of patients who pay by phone, and discussed its trade-offs. "Slower" is how it looked, but it is the better strategic instinct for a role whose first job is setting payments strategy.

People leadership (20%) - Dev told every story in the first person and said his last PM team "struggled to keep up with me." Two of his references are current direct reports, so they aren't independent. Your contact at his previous company said his team "turned over a lot." That is one informal source, but it is consistent with the interview. - Joy coached a struggling PM into a better-fit role, promoted another, and credited her engineers by name. The VP Engineering's original instinct was that she was the strongest leader we've seen. This role leads three PMs.

Execution and influence (35%, the heaviest weight) - Dev's strength is real here. His deck was "the best we've ever seen," and your contact says he is "great in front of execs." That is evidence of influence upward, though not of execution. - Joy's evidence is thinner. The only concerns are "not much presence" and "not sure she'd command the room with the CFO." Those are impressions, not observed behavior, and nobody has tested them. Her 14-point completion lift is execution evidence, but I want to confirm her role in it. - Neither file has much direct evidence on delivery, such as shipping through Finance and Compliance constraints. That is a gap for both.

My rescoring. These are my judgment, not data. I gave Dev a 3 or 4 on execution and influence, and Joy a 3.

Exec/influenceProduct senseAnalyticsLeadershipWeighted
Dev4322~3.0
Joy3344~3.4

Even generous to Dev on the heaviest-weighted category, Joy comes out ahead. The result is sensitive to the influence score, which is why the CFO check matters.

Risks

  1. Executive presence with the CFO. This is Joy's real risk, because the role requires working closely with Finance. But the feedback is vague and unverified, and "presence" comments are also where style bias tends to enter. We should test it rather than assume it.
  2. Attribution of the 33%→47% result. I haven't verified what she owned versus what the team or market drove.
  3. Dev's strengths are not nothing. He may be a strong operator with weak interview discipline on metrics. But the absence of a single number, combined with the team-turnover signal and the "struggled to keep up with me" comment, is a pattern. For a role leading three PMs, that is the riskier error.
  4. Process risk. If we hire on the recorded scores, we are acting on data we know was contaminated.

What I'd check before the offer

All of this fits before Friday:

  1. A 30-minute working session with the CFO and a Compliance lead. Joy would take a real case, such as a payment-plan or fee trade-off. Interviewers would score against defined criteria: clarity, handling of pushback, and command of the numbers. This tests the specific concern directly.
  2. Targeted reference calls on Joy. I would ask her former manager and peer how she handled disagreement with Finance or senior executives, with a specific example. I would also ask for one additional reference from a Finance or Compliance partner. Nobody has asked this yet.
  3. Verify the 33%→47% result. I would ask Joy what she personally owned and how it was measured, and ask her references to confirm.
  4. Ask the debrief group to put concrete observations behind "presence." I would also ask the VP Engineering what changed their score from 4 to 3. If the answer is "the discussion" and not new evidence, that tells us something.

If Dev stays in consideration, I would require two things first: a specific metric he moved and how, and two independent references (former peers or reports, not current ones). I am not proposing to hold the process open for him, but it is the bar I'd want met.

Compensation

Joy asks for $230k and Dev for $245k, in a $220–250k band. I would offer Joy at or near her ask. If the CFO session is strong, there is room to move up within the band if needed to close. Her ask leaves headroom for growth, and I don't want to lowball her because she asked for less.

Process note for next time

We should collect written scores independently before the debrief, and have the most senior person speak last. Otherwise the loudest early voice becomes the score.

Bottom line: Joy is the stronger fit on the evidence, with one open question (influence with the CFO) that I can resolve before Friday. I recommend we make the offer if the CFO session and reference checks come back clean.

Grades and run details

Decision model 100 · LLM judge 13 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly64%
  • passAddresses the actual decision100%
  • passRespects explicit constraints50%
  • passIdentifies material uncertainty100%
  • passAvoids unsupported claims44%
  • passProduces the required deliverable96%
  • passWeighs against the agreed scorecard99%
  • passCatches the red flags in Dev's evidence100%
  • passSays what would change the call100%
  • passJudges on evidence, not presence76%
  • passDefines good for this role first68%
  • passKeeps each judgement independent96%
Run
Run
#1
API response time
34 s
Submitted
5 Oct 2026

Usable with a quick edit10 of 12 checks passed

The output is well-structured and evidence-based; a quick edit to add the condition that would change the call (e.g., 'if the reference reveals she struggled with finance, we will not proceed') would make it fully complete.

Output

MEMORANDUM

TO: Rosa Lindqvist, VP of Product FROM: Director of Product, Medvia DATE: October 24, 2023 SUBJECT: Hiring Recommendation: Group PM, Patient Payments

*

Rosa,

Following yesterday’s debrief, I have reviewed the scorecards, interview transcripts, and reference notes for our open Group PM role. We are tasked with finding a leader who can set strategy, lift our online payment rate from its current 41%, lead a team of three PMs, and collaborate effectively with Finance and Compliance in a regulated healthcare environment.

The CEO needs our decision by Friday. My recommendation is that we extend an offer to Joy Adeyemi at $230k. We should not hire Dev Malhotra.

Below is the evidence, the associated risks, and the pre-offer checks required to secure Joy.

---

1. The Evidence: Why Joy Adeyemi over Dev Malhotra

While the final entered scores (Dev straight 4s; Joy straight 3s) suggest a clear winner, the raw interview notes and hidden dynamics of our debrief tell a different story.

  • Product Sense & Problem Solving: Dev is a classic "feature factory" ideator. The Head of Design noted Dev pitched six features in five minutes before asking who pays and why. Conversely, Joy spent her first 15 minutes investigating the root causes of friction—asking who pays and why others don’t—before proposing a targeted, high-leverage solution: text-to-pay tailored to the 58% of patients currently paying via phone. For a critical metric like our 41% online payment rate, we need clinical execution, not a scattershot feature list.
  • Analytics & Rigor: The Data Lead’s notes on Dev are a red flag. When pressed on metrics, Dev offered vague marketing speak ("grew massively," "shipped 40+ features") with zero data attribution. Joy provided hard, demonstrable impact: she raised payment completion at her previous company from 33% to 47% over 18 months. Crucially, she also walked through a failed experiment and extracted lessons—proving she understands the complex trade-offs of healthcare payments.
  • People Leadership & Culture: Dev speaks entirely in the first person ("I," "my wins") and casually remarked that his last team "struggled to keep up with me." Rosa, you likely noted your contact’s warning that his team suffered high turnover. Joy is the inverse: she coaches struggling reports, promotes talent, and attributes wins directly to her engineers. The VP of Engineering’s pre-debrief notebook literally rated Joy as a "4, strongest leader we've seen," before social pressure in the debrief caused the scores to normalize.
  • The "Presence" Bias: The primary knock on Joy during the debrief was a lack of "presence" and a concern about whether she could "command the room with the CFO." This is a subjective trap. We do not need a charismatic showman to present slide decks; we need a strategist who can build bulletproof business cases with Finance and navigate Compliance. Joy’s methodical, customer-first approach is precisely what Finance will trust.

---

2. Risks to Manage

  • The CFO/Executive Presence Risk: Because the interview panel worried Joy might be quiet in front of executive stakeholders, we must validate her ability to defend her strategy to our CFO and Compliance leads.
  • Compensation and Closing: Joy is asking for $230k (right in the middle of our $220k–$250k band), making her a financially sound hire. However, because she is measured and thoughtful, she may take a few days to deliberate. We need to move decisively.

---

3. Pre-Offer Checks (To Be Completed by Thursday EOD)

Before we release the offer to Joy, I will execute two final validation steps:

  1. Backchannel/Targeted Reference Check: Since our previous reference calls did not explicitly probe her dynamic with Finance or Compliance, I will spend 10 minutes with her former manager specifically asking: “How did Joy handle budget cross-examinations with Finance? How did she partner with Legal/Compliance when building payment flows?”
  2. The "CFO Alignment" Test: I’d like to spend 30 minutes with Joy and our current Director of Finance (or you and I) presenting a mock 10-minute briefing on our 41% online payment bottleneck, asking her to walk us through how she would construct the business case for the Q1 roadmap.

Conclusion

Dev is dazzling in a pitch meeting, but his metrics are hollow, his leadership style is toxic to retention, and his instinct is to build before understanding. Joy is rigorous, customer-obsessed, metrics-driven, and a multiplier for her team and cross-functional partners.

Let’s trust the substance over the theater. I am ready to initiate the final check and prepare the $230k offer for Friday morning.

Grades and run details

Decision model 92 · LLM judge 11 of 13 checks
Decision model checks
  • passUses the supplied evidence correctly26%
  • passAddresses the actual decision76%
  • passRespects explicit constraints60%
  • passIdentifies material uncertainty78%
  • partialAvoids unsupported claims56%
  • passProduces the required deliverable98%
  • passWeighs against the agreed scorecard94%
  • passCatches the red flags in Dev's evidence100%
  • passSays what would change the call48%
  • passJudges on evidence, not presence98%
  • partialDefines good for this role first18%
  • passKeeps each judgement independent68%
Run
Run
#1
Time to output
6 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

Says what would change the callRightMixed
Sonnet 5.5 · API

It identifies Joy's influence with the CFO as the open question and proposes a working session with the CFO and targeted reference checks to resolve it.

Gemini 3.5 Flash-Lite · Gemini

It names the CFO influence open question and proposes a targeted reference check and mock briefing, but does not state what answer or result from those checks would change the hiring decision.

Defines good for this role firstRightWrong
Sonnet 5.5 · API

It sets out the scorecard competencies and weightings for the role and uses them to define strong and weak signals before judging candidates.

Gemini 3.5 Flash-Lite · Gemini

It does not explicitly set out the weighted competencies and what strong and weak look like for this role before judging the candidates.

All got right 10

Uses the supplied evidence correctlyRightRight
Sonnet 5.5 · API

Every claim about the current situation is directly supported by the supplied context, with no invented facts.

Gemini 3.5 Flash-Lite · Gemini

Every claim about the current situation is directly supported by the supplied context, with no invented facts.

Addresses the actual decisionRightRight
Sonnet 5.5 · API

The output commits early to hiring Joy, conditional on checks, says not to hire Dev, and states that if checks go badly it would hold the search open.

Gemini 3.5 Flash-Lite · Gemini

The output unambiguously recommends hiring Joy and not Dev, framed for Rosa, and implies the offer is contingent on the pre-offer checks resolving the CFO concern.

Respects explicit constraintsRightRight
Sonnet 5.5 · API

The output is a memo to Rosa, under 1,000 words, and respects the form and length requested.

Gemini 3.5 Flash-Lite · Gemini

The memo is addressed to Rosa, stays under 1,000 words, and includes the required elements: who to hire, evidence, risks, and pre-offer checks.

Identifies material uncertaintyRightRight
Sonnet 5.5 · API

It names the unknowns (CFO presence, attribution of the metric, Dev's pattern) and specifies how to resolve them with targeted checks before the offer.

Gemini 3.5 Flash-Lite · Gemini

It names the CFO presence concern as the key unknown, bounds it to the panel's impression, and proposes specific checks to resolve it.

Avoids unsupported claimsRightRight
Sonnet 5.5 · API

Interpretations like anchoring and output vs outcome are clearly presented as the author's judgment, not as established fact.

Gemini 3.5 Flash-Lite · Gemini

Interpretations like 'feature factory' are clearly presented as conclusions drawn from the evidence, not as established facts.

Produces the required deliverableRightRight
Sonnet 5.5 · API

The memo is complete, addressed to Rosa, within the word limit, and actionable with a clear recommendation and next steps.

Gemini 3.5 Flash-Lite · Gemini

The recommendation memo is complete, in the correct form, and could be acted on with light edits.

Weighs against the agreed scorecardRightRight
Sonnet 5.5 · API

The recommendation walks through each scorecard competency with evidence for both candidates and notes that presence and the deck are not on it.

Gemini 3.5 Flash-Lite · Gemini

The output evaluates candidates on product sense, analytics, and people leadership, and explicitly dismisses presence and the deck as not on the scorecard.

Catches the red flags in Dev's evidenceRightRight
Sonnet 5.5 · API

It names at least four red flags: no metric moved, first-person credit and team struggled to keep up, jumping to features, and back-channel turnover report.

Gemini 3.5 Flash-Lite · Gemini

It catches Dev's missing metric, first-person credit and team struggle, jumping to features, and the back-channel turnover report—all four red flags.

Judges on evidence, not presenceRightRight
Sonnet 5.5 · API

It weighs concrete evidence (metric moved, team credit, learning from failure) and treats impressions like presence as weak, unverified signals.

Gemini 3.5 Flash-Lite · Gemini

It judges on concrete evidence (metric moved, team credit, learning from failure) and treats presence as a weak, subjective signal.

Keeps each judgement independentRightRight
Sonnet 5.5 · API

It points out that scores were entered after discussion, notes the VP Engineering's pre-debrief score changed, and recommends written independent scores before debrief.

Gemini 3.5 Flash-Lite · Gemini

It relies on the VP Engineering's independent pre-debrief rating and discounts the score that changed under group pressure.

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-6.1 SolwithAPI94.4100.03None
2Sonnet 5.5withAPI95.896.22None
3Gemini 3.8 FlashwithAPI89.692.32None
4GPT-6 AstrawithChatGPT95.887.23None
5Opus 5.5withClaude91.787.23None
6GPT-6 LunawithAPI93.184.63None
7Gemini 3.5 Flash-LitewithGemini75.064.13None

About the task

The PM job

Hiring product managers.

Why it matters

A bad PM hire costs a team a year. Most loops reward polish and presence, and the debrief goes to whoever speaks first.

What good looks like

  • Defines what good looks like for this role before judging anyone
  • Judges on evidence of results and how they work with a team
  • Tests the real job, not a rehearsed framework
  • Keeps each interviewer's judgement independent

Deliberately not measured

  • Employment law
  • Compensation benchmarking
Capability tested

Hiring judgement

The failure we’re looking for

Hiring for presence and polish over evidence

Grading

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