Tasks / Metrics & Experimentation

Draft OKRs

Can the model turn a team's wish list into a few outcome-based OKRs that add up to the company's goals?

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. Builds the key results on the data100% pass
    Key results 2 and 3 are built directly on the first-week joining data and time-to-shared-note data, with baselines from the brief and targets.
    GPT-6.1 Sol · API · Eleven key results and a bonus
  2. Aims the teams with the churn data100% pass
    Uses churn reasons and segment data to aim Payroll at errors, Integrations at 3+ integration adoption, and Core HR at churn reduction and price/value risk, with explicit links.
    GPT-6.1 Sol · API · The goals that don't add up
  3. Reads the 0.95 average for what it is100% pass
    Recognizes the 0.95 average suggests safe targets, and advises against tying bonuses to OKR scores because it would encourage sandbagging.
    GPT-6.1 Sol · API · The goals that don't add up

Where it slips

  1. Flags the bonus link61% pass
    It names the risk of safe targets but only proposes asking leadership how a 70% score will be read, not a concrete fix like separating bonuses from OKR scores.
    Opus 5.5 · Claude · Eleven key results and a bonus
  2. Identifies material uncertainty66% pass
    The output does not name any specific unknowns that could change the OKRs or how they would be resolved.
    GPT-6 Luna · API · Eleven key results and a bonus
  3. Uses the supplied evidence correctly71% pass
    Claims that the gap is correlation and that the safe play is to pick easy KRs are not supported by the brief or arithmetic.
    Sonnet 5.5 · API · Eleven key results and a bonus

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. Key results are outcomes, not output

    Is every key result a measurable change in customer or business behaviour, rather than something the team will ship or do?

    Passes when Every key result names a metric with a baseline and a target; shipping work appears only as initiatives that serve them.

  2. Focuses on the big rock

    Does the output cut the goals down to the few that matter most, rather than covering everything the team could do?

    Passes when Few objectives, each with a small number of key results, and says what was dropped and why.

  3. Shows how the goals add up

    Does the output show how each team-level key result contributes to the company-level goals, and flag any that don't?

    Passes when Every key result is linked to a company goal, with the reasoning or data that connects them, and any that serve no company goal are flagged or cut.

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 the PM for Fieldnote's onboarding squad. The squad drafted its OKRs for next quarter and asked you to tidy them up before they go to leadership on Friday. Rewrite them, and add a short note to the squad on what you changed and why. No more than 600 words in all. What we know is below.

What the model was given6 items: About Fieldnote, The company's goal for next quarter (set by leadership), The squad's draft, Data, A note from HR, The squad
About FieldnoteShared notes and docs for teams, sold self-serve. Teams sign up free and pay once they pass five members.
The company's goal for next quarter (set by leadership)Objective: make Fieldnote the place new teams get started. Key result: raise the share of new teams still active in week 4 from 34% to 40%.
The squad's draftObjective 1: Ship the new onboarding. Key results: launch an onboarding checklist; ship 5 templates; redesign the invite flow; launch onboarding emails. Objective 2: Improve quality. Key results: fix 50 bugs; get P1 bugs to zero. Objective 3: Delight users. Key results: raise NPS from 31 to 40; launch dark mode; ship AI summaries; run 10 user interviews; publish 4 help articles.
DataNew teams where three or more people join in the first week: 58% still active in week 4. Everyone else: 21%. Today 29% of new teams get three or more people in the first week. Median time from sign-up to the first note someone else opens: 3 days.
A note from HRFrom this year, each squad member's bonus multiplier is set by their squad's OKR score.
The squadFive engineers, one designer and you.
What a strong answer doesThe answer key the graders mark against

Rewrites the draft into one objective tied to the company's (new teams get started) with two or three outcome key results built from the data, such as raising the share of new teams with three or more people in week one from 29% to a stated target, and cutting the time to the first shared note from 3 days to a stated target, with the company's week-4 retention as the result they serve. The checklist, templates, invite redesign and emails become initiatives, not key results. Drops what doesn't serve the goal (dark mode, AI summaries) or moves it out of the OKRs; keeps bug work as a health measure rather than an objective. Flags that tying bonuses to OKR scores will push the squad to set safe targets, and suggests keeping them apart. Explains the changes to the squad briefly and warmly.

Critical failures (cap the score at 40)
  • Keeps the draft's key results essentially unchanged
Task

Mid-level · v1.0 · synthetic · B2B SaaS, self-serve, goals

The outputs

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

Needs more than a quick edit8 of 11 checks passed

The output fails to address the bonus-link risk properly and makes an unsupported claim about the biggest retention lever, requiring edits before use.

Output

To the Onboarding Squad:

Thanks for putting these together. I’ve rewritten our OKRs for leadership. Before I explain why, please note HR’s rule: our bonuses are now tied directly to our OKR scores. That means our OKRs must be about outcomes, not output, and they must tightly align with the company goal.

Here is what I changed:

  1. Dropped the "feature laundry list." Draft 1 listed outputs (checklists, dark mode, 50 bugs). If we ship all those and week 4 retention doesn't budge, leadership fails us—and our bonuses drop. We need to focus on what happens to the user, not what we build.
  2. Anchored on the Company Goal: Leadership wants week 4 team retention up from 34% to 40%. Our entire quarter must serve this.
  3. Used our Data: Our data shows a massive retention cliff: teams where 3+ people join in week one retain at 58%, versus 21% for everyone else. Right now, only 29% hit that 3+ threshold. Furthermore, the median time to a shared note is 3 days—too slow.

Here is the finalized version going to leadership on Friday:

*

Q3 Onboarding Squad OKRs

Objective: Get new teams collaborating faster so they stick around.

  • KR 1: Raise the share of new teams with 3+ active members in week 1 from 29% to 40%. (Directly attacks our biggest retention lever).
  • KR 2: Reduce the median time from sign-up to the first note opened by a second team member from 3 days to under 24 hours. (Accelerates "time-to-value" and teamwork).
  • KR 3: Raise the share of new teams still active in week 4 from 34% to 40%. (Our direct contribution to the company goal).

*

What this means for us: Features like the checklist, templates, AI summaries, and invite flow redesign are still our tools to hit these goals, but we’ll prioritize them based on whether they actually drive team invites and fast collaboration. Let’s crush this.

Grades and run details

Decision model 82 · LLM judge 8 of 12 checks
Decision model checks
  • passUses the supplied evidence correctly76%
  • passAddresses the actual decision71%
  • passRespects explicit constraints56%
  • failIdentifies material uncertainty87%
  • partialAvoids unsupported claims54%
  • passProduces the required deliverable92%
  • passBuilds the key results on the data100%
  • partialFlags the bonus link93%
  • passKey results are outcomes, not output99%
  • passFocuses on the big rock100%
  • passShows how the goals add up79%
Run
Run
#1
Time to output
3 s
Submitted
5 Oct 2026

Usable with a quick edit11 of 11 checks passed

The output is a well-structured, evidence-based OKR rewrite with a clear note; a PM could use it with at most a quick edit to finalize targets after the suggested validation discussions.

Output

Proposed onboarding OKRs — next quarter

Objective: Help new teams start collaborating quickly and build a habit that lasts.

Key resultBaseline → proposed target
1. Increase the share of new teams with at least three members joining within seven days of sign-up.29% → 40%
2. Reduce median time from sign-up to the first note opened by another member of the team.3 days → 1 day
3. Increase the share of new teams still active in week 4, supporting the company’s retention goal.34% → 40%

Measurement and scoring - Use consistent definitions of “new team,” membership and “active,” agreed with whoever owns company reporting. Exclude test accounts consistently. - For KR2, use a fixed observation window and report the share of teams that never reach the milestone alongside the median. A faster median must not hide more teams failing to collaborate. - Score week-4 retention only on cohorts old enough to observe it; agree the final reporting date before the quarter starts. - Treat these as proposed targets, not finalized bonus commitments, until we validate measurement, scope and feasibility.

Candidate work—not additional KRs

Prioritize the invite-flow redesign and a minimal onboarding checklist as the first experiments. Use interviews to understand why teammates do or don’t join. Test templates, onboarding emails and help content where evidence suggests they address a specific obstacle; do not commit to shipping every item.

Keep capacity for onboarding reliability. Before approval, agree measurable quality guardrails for sign-up, invitations and note access using current performance and existing incident standards. Do not trade reliability for activation gains.

Defer dark mode and AI summaries unless research establishes a stronger onboarding opportunity than the current priorities.

Note to the squad

I replaced three broad objectives and a delivery checklist with one focused objective and measurable user outcomes. Launches, bug counts and interviews describe work; they don’t establish whether onboarding helped teams succeed. NPS is useful context, but too broad to be our primary onboarding score.

Early team participation is our strongest current signal: teams with three or more people joining in week 1 retain at 58%, versus 21% for others. That is correlation, not proof that getting more people to join will cause higher retention. We should test changes and measure downstream retention—not optimize invitations alone.

We also need to reconcile the data before locking targets: the supplied segment rates and 29% participation share imply about 32% overall retention, rather than 34%. They may describe different cohorts or definitions.

Finally, because OKR scores now determine bonuses, scoring rules are part of the commitment. Before Friday’s sign-off, we should agree target realism, weights, cohort timing and treatment of material external dependencies with leadership and HR. The company retention target remains shared accountability, not an outcome onboarding controls alone. With five engineers and one designer, focused experiments and reliable core flows are a more credible plan than nine separate launches.

Grades and run details

Decision model 95 · LLM judge 12 of 12 checks
Decision model checks
  • passUses the supplied evidence correctly53%
  • passAddresses the actual decision56%
  • partialRespects explicit constraints26%
  • passIdentifies material uncertainty83%
  • passAvoids unsupported claims76%
  • passProduces the required deliverable68%
  • passBuilds the key results on the data100%
  • passFlags the bonus link72%
  • passKey results are outcomes, not output100%
  • passFocuses on the big rock100%
  • passShows how the goals add up48%
Run
Run
#1
Time to output
37 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 3

Identifies material uncertaintyWrongRight
Gemini 3.5 Flash-Lite · Gemini

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

GPT-6 Astra · ChatGPT

It names the correlation vs. causation risk, the data discrepancy, and the bonus-driven target risk, and says how to resolve them (testing, agreeing measurement, treating targets as proposed).

Avoids unsupported claimsWrongRight
Gemini 3.5 Flash-Lite · Gemini

The claim 'biggest retention lever' is presented as fact without support from the data, which only shows correlation.

GPT-6 Astra · ChatGPT

Hypotheses like the correlation signal are clearly labelled, and no interpretations are presented as established fact.

Flags the bonus linkWrongRight
Gemini 3.5 Flash-Lite · Gemini

The output notes the bonus-OKR link but does not suggest separating them or another concrete fix for the risk of safe targets.

GPT-6 Astra · ChatGPT

It flags the risk of bonuses driving safe targets and suggests treating the targets as proposed, not finalized bonus commitments, until validated with leadership and HR.

All got right 8

Uses the supplied evidence correctlyRightRight
Gemini 3.5 Flash-Lite · Gemini

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

GPT-6 Astra · ChatGPT

All statements about the current situation are taken directly from the brief or derived by arithmetic, with no invented facts.

Addresses the actual decisionRightRight
Gemini 3.5 Flash-Lite · Gemini

The output commits to a clear set of rewritten OKRs and a note, addressing the request.

GPT-6 Astra · ChatGPT

The output commits to a clear rewrite with one objective and three outcome KRs, and explains the changes to the squad.

Respects explicit constraintsRightRight
Gemini 3.5 Flash-Lite · Gemini

The output is a note to the squad with the OKRs, well under 600 words, respecting the form and length.

GPT-6 Astra · ChatGPT

The output is a rewritten OKR set and a note to the squad, well within 600 words, and respects the requested form and reader.

Produces the required deliverableRightRight
Gemini 3.5 Flash-Lite · Gemini

The note and OKRs are complete, within length, and usable by the squad with light edits.

GPT-6 Astra · ChatGPT

The output is a complete OKR rewrite and a note to the squad, usable as is with light edits, and within the word limit.

Builds the key results on the dataRightRight
Gemini 3.5 Flash-Lite · Gemini

KR1 and KR2 are built on the first-week joining data and time to shared note, with baselines and targets.

GPT-6 Astra · ChatGPT

KR1 uses the first-week joining data (29% baseline) and KR2 uses the time-to-shared-note data (3 days baseline), both with targets.

Key results are outcomes, not outputRightRight
Gemini 3.5 Flash-Lite · Gemini

All key results are measurable changes in customer behavior, not things to ship.

GPT-6 Astra · ChatGPT

All three key results are measurable changes in user behavior with baselines and targets; shipping work is listed as candidate initiatives, not KRs.

Focuses on the big rockRightRight
Gemini 3.5 Flash-Lite · Gemini

The output cuts the goals to one objective with three KRs and explains what was dropped.

GPT-6 Astra · ChatGPT

It cuts the draft to one objective with three KRs, explicitly defers dark mode and AI summaries, and explains why.

Shows how the goals add upRightRight
Gemini 3.5 Flash-Lite · Gemini

Each KR is linked to the company goal with reasoning or data, and no KR serves no company goal.

GPT-6 Astra · ChatGPT

KR3 directly mirrors the company goal, and the note explains how KR1 and KR2 are leading indicators linked to retention, while flagging that the company target is shared accountability.

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 SolwithAPI100.0100.02None
2GPT-6 AstrawithChatGPT98.5100.03None
3Sonnet 5.5withAPI90.991.72None
4GPT-6 LunawithAPI93.283.32None
5Opus 5.5withClaude90.986.13None
6Gemini 3.8 FlashwithAPI68.270.82None
7Gemini 3.5 Flash-LitewithGemini65.250.03None

About the task

The PM job

Setting a team's goals for the quarter.

Why it matters

Most OKRs are task lists in disguise. Teams ship everything on them and nothing moves.

What good looks like

  • Key results are outcomes, not things to ship
  • Few enough to focus on
  • Each team's goals visibly add up to the company's
  • Kept apart from performance ratings

Deliberately not measured

  • OKR software or formatting
Capability tested

Goal setting

The failure we’re looking for

A task list dressed up as key results

Grading

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