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 16 graded outputs by 7 models. 63% were usable with at most a quick edit.

Reliably right

  1. Focuses on the big rock100% pass
    The output reduces the draft to one objective with three KRs, explicitly drops dark mode, AI summaries, and NPS, and explains why.
    GPT-6.1 Sol · API · Eleven key results and a bonus
  2. 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
  3. 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

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 uncertainty70% 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 correctly75% 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.

Usable with a quick edit9 of 11 checks passed

The output is clear and actionable; the unsupported correlation claim can be fixed with a quick wording edit, and the rest is solid.

Output

Onboarding squad OKRs, next quarter

Objective 1: New teams get their teammates in during week 1, and stay (supports the company goal: make Fieldnote the place new teams get started)

  • KR1: Raise the share of new teams with 3+ people in week 1 from 29% to 45%.
  • KR2: Cut the median time from sign-up to the first note someone else opens from 3 days to 1 day.
  • KR3: Raise the share of new teams active in week 4 from 34% to 40% (shared with the company KR).

Planned bets, not KRs: onboarding checklist, invite flow redesign, 5 templates, onboarding emails. We'll drop or swap any of them if they aren't moving KR1 and KR2.

Objective 2: Keep the product dependable while we change onboarding

  • KR1: Zero open P1 bugs at quarter end, and no P1 open longer than 5 days at any point.

Backlog, not this quarter: dark mode, AI summaries, NPS target, help articles.

---

Note to the squad

Thanks for the draft. Here's what I changed and why.

1. Outcomes, not deliverables. Most of the draft listed things we'd ship. We could ship all of it and leave week-4 activity flat. The checklist, invite redesign, templates and emails are now the bets we make to hit the KRs.

2. One clear line to the company goal. Teams where 3+ people join in week 1 are still active in week 4 58% of the time, versus 21% for everyone else, and only 29% of teams reach 3+ today. So KR1 is about getting more teams to that point. Reaching 40% overall probably needs about 45% of teams there, assuming the relationship holds. KR2 measures how quickly a new team sees value from a teammate.

3. Cut and moved. Dark mode, AI summaries and the NPS target have no clear link to the company goal, and NPS isn't something onboarding alone can move. They go to the backlog, not the bin. User interviews and help articles are useful, but as activities inside Objective 1, not KRs. "Fix 50 bugs" rewarded volume over impact, so it became a P1 guardrail.

4. Two caveats to settle before Friday. - The data implies a week-4 rate of about 32% (0.29×58% + 0.71×21%), not the 34% in the company KR. Likely a definition or timing difference, but I'll confirm which baseline we're using. - The 58% vs 21% gap is correlation. Teams that invite people quickly may already be more committed, so the real lift from nudging invites will probably be smaller. That's why KR3 sits next to KR1, so we'll see whether the outcome really moves.

5. The bonus link. Since scores now affect everyone's multiplier, the safe play is to pick easy, output-style KRs. I've deliberately not done that. KR1 and KR2 are stretch targets, and I'd rather be upfront about it than quietly sandbag. Before Friday I'll ask leadership and HR how scores convert to multipliers, and I'll push for 0.7 on stretch KRs to count as a good result. I'll share the answer as soon as I have it. If you think a target is unrealistic, tell me before Friday so we can adjust it openly.

Please reply with objections or number challenges by Thursday noon.

Claims the judge couldn’t find in the brief

  • The 58% vs 21% gap is correlation.
  • The safe play is to pick easy, output-style KRs.

Grades and run details

Decision model 95 · LLM judge 10 of 12 checks
Decision model checks
  • passUses the supplied evidence correctly59%
  • passAddresses the actual decision81%
  • partialRespects explicit constraints23%
  • passIdentifies material uncertainty96%
  • passAvoids unsupported claims49%
  • passProduces the required deliverable80%
  • passBuilds the key results on the data100%
  • passFlags the bonus link100%
  • passKey results are outcomes, not output73%
  • passFocuses on the big rock100%
  • passShows how the goals add up59%
Run
Run
#1
API response time
25 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

Needs more than a quick edit7 of 11 checks passed

The output includes unsupported claims about NPS and bug incentives, and fails to properly address the bonus-link risk, so a PM would need to rework those parts before use.

Output

Here is the revised OKR draft along with the team note.

*

Proposed Q3 OKRs: Onboarding Squad

Objective 1: Accelerate new teams to their first collaborative moment * KR 1: Increase the share of new teams with 3+ members in Week 1 from 29% to 45%. * KR 2: Reduce median time from team creation to first collaborative note opened from 3 days to <24 hours. * KR 3: Contribute directly to the company goal by lifting new team Week 4 retention from 34% to 40%.

Objective 2: Deliver a seamless, zero-friction first-run experience * KR 1: Maintain zero open P1 bugs across the sign-up, invite, and first-note flows throughout the quarter. * KR 2: Increase invite-flow conversion (invites sent per new team) by 25%.

*

Note to the Squad

Team,

Thanks for pulling the initial draft together! Ahead of our review with leadership this Friday, I’ve refocused our OKRs. Here is what changed and why:

#### 1. We shifted from outputs to outcomes Shipping checklists, templates, and emails are solutions, not goals. If we ship all four and retention doesn’t budge, we haven’t succeeded. By framing our KRs around moving specific user behaviors (e.g., getting teams to collaborate faster), we give ourselves the flexibility to iterate, drop what isn’t working, and double down on what does.

#### 2. We’re anchoring on our biggest retention lever Our company goal is lifting Week 4 retention from 34% to 40%. Our data shows an undeniable path to get there: * Teams with 3+ members in Week 1 retain at 58% (vs. 21% for everyone else). * Currently, only 29% of teams hit that milestone. * It takes 3 full days for a teammate to open their first shared note.

Our primary job this quarter is simple: get teammates into the workspace and collaborating on day one. Every template, invite prompt, or checklist we build should serve that specific loop.

#### 3. We cut scope to protect our focus (and our bonuses) Now that HR ties our bonus multipliers directly to squad OKR completion, our OKRs must be focused, measurable, and within our sphere of influence: * Dropped AI summaries and Dark Mode: Both are cool features, but neither solves the core first-week team activation problem. They dilute our engineering capacity. * Dropped NPS (31 to 40): NPS is a lagging, noisy metric influenced by the entire product (pricing, bugs elsewhere, feature gaps), not just onboarding. * Refined bug targets: Fixing an arbitrary "50 bugs" incentivizes fixing trivial issues. Instead, we’re committing to zero critical bugs in our core onboarding and invitation paths.

Let’s chat through any questions at standup tomorrow so we’re fully aligned before Friday!

Claims the judge couldn’t find in the brief

  • NPS is a lagging, noisy metric influenced by the entire product (pricing, bugs elsewhere, feature gaps), not just onboarding.
  • Fixing an arbitrary '50 bugs' incentivizes fixing trivial issues.

Grades and run details

Decision model 77 · LLM judge 7 of 12 checks
Decision model checks
  • passUses the supplied evidence correctly28%
  • passAddresses the actual decision63%
  • passRespects explicit constraints19%
  • failIdentifies material uncertainty65%
  • partialAvoids unsupported claims31%
  • passProduces the required deliverable65%
  • passBuilds the key results on the data100%
  • partialFlags the bonus link82%
  • passKey results are outcomes, not output40%
  • passFocuses on the big rock94%
  • partialShows how the goals add up81%
Run
Run
#1
API response time
20 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 4

Uses the supplied evidence correctlyMixedRightMixed
Sonnet 5.5 · API

Claims that the gap is correlation and that the safe play is to pick easy KRs are not supported by the brief or arithmetic.

GPT-6 Astra · ChatGPT

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

Gemini 3.8 Flash · API

The output presents unsupported claims about NPS being lagging/noisy and the 50-bug target incentivizing trivial issues, which are not in the brief or derivable from it.

Identifies material uncertaintyRightRightWrong
Sonnet 5.5 · API

Names baseline discrepancy and correlation vs causation, and says how to resolve them; also addresses bonus conversion uncertainty.

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).

Gemini 3.8 Flash · API

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

Avoids unsupported claimsMixedRightWrong
Sonnet 5.5 · API

Presents 'the gap is correlation' and 'the safe play is to pick easy KRs' as facts without labeling them as hypotheses.

GPT-6 Astra · ChatGPT

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

Gemini 3.8 Flash · API

It states as fact that NPS is lagging/noisy and that the 50-bug target incentivizes trivial issues, without labelling these as hypotheses or interpretations.

Flags the bonus linkRightRightWrong
Sonnet 5.5 · API

Flags risk of safe targets due to bonus link and suggests pushing for 0.7 on stretch KRs to count as good.

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.

Gemini 3.8 Flash · API

The output mentions the bonus link but does not name the risk of safe targets or suggest separating bonuses from OKR scores; it only says OKRs must be focused.

All got right 7

Addresses the actual decisionRightRightRight
Sonnet 5.5 · API

Commits to rewritten OKRs and note, and says squad feedback by Thursday could change targets.

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.

Gemini 3.8 Flash · API

The output commits to a clear revised OKR set and explains the changes, framed for the squad.

Respects explicit constraintsRightRightRight
Sonnet 5.5 · API

Output is a rewritten OKRs and note under 600 words, addressed to the squad.

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.

Gemini 3.8 Flash · API

The output is a rewritten OKR draft with a note, under 600 words, and respects the requested form and reader.

Produces the required deliverableRightRightRight
Sonnet 5.5 · API

Provides complete OKRs and note within word limit, usable by the squad.

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.

Gemini 3.8 Flash · API

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

Builds the key results on the dataRightRightRight
Sonnet 5.5 · API

KR1 uses the 29% baseline and KR2 uses the 3-day median, both from the data.

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.

Gemini 3.8 Flash · API

KR1 uses the 29% baseline and sets a target; KR2 uses the 3-day baseline and sets a target; both are built on the supplied data.

Key results are outcomes, not outputRightRightRight
Sonnet 5.5 · API

All KRs are outcome metrics with baselines and targets; shipping work is listed as initiatives.

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.

Gemini 3.8 Flash · API

All key results are measurable changes in user behavior or quality (e.g., share of teams, time, retention, bug count, conversion), not shipped features.

Focuses on the big rockRightRightRight
Sonnet 5.5 · API

Cuts to one main objective with three KRs and a quality guardrail, drops unrelated items and explains why.

GPT-6 Astra · ChatGPT

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

Gemini 3.8 Flash · API

The output reduces the draft to two objectives with a few KRs, drops dark mode, AI summaries, and NPS, and explains why.

Shows how the goals add upRightRightRight
Sonnet 5.5 · API

Links KR1 and KR2 to company goal via the data, and flags items that don't serve it.

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.

Gemini 3.8 Flash · API

Each KR is linked to the company goal via the retention data, and the note explains how the 3+ member and time-to-collaboration metrics drive Week 4 retention.

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-LitewithGemini77.362.52None

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