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?

Task type v1.1 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

What AI gets right here, and what you’ll still have to catch

From 18 graded outputs by 9 models. 50% 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. Respects explicit constraints99% pass
    The output is a memo to Grace Mensah, within the 1,100-word limit, and includes the requested diagnosis and team OKRs with baselines and targets.
    GPT-6 Astra · ChatGPT · A great quarter, on paper
  3. Produces the required deliverable99% pass
    The memo is complete, in the right form for the CPO, and a PM could act on it with only light edits.
    GPT-6 Astra · ChatGPT · A great quarter, on paper

Where it slips

  1. Flags the bonus link47% pass
    Mentions bonus multipliers only to justify outcome KRs; does not name the risk of safe targets or suggest decoupling OKR scores from bonuses.
    Haiku 5.5 · API · Eleven key results and a bonus
  2. Avoids unsupported claims67% pass
    Presents 'the gap is correlation' and 'the safe play is to pick easy KRs' as facts without labeling them as hypotheses.
    Sonnet 5.5 · API · Eleven key results and a bonus
  3. Identifies material uncertainty68% 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

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

  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 compare up to three outputs, 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 · v1.1

The outputs

9 setups answered this brief.

Usable with a quick edit11 of 11 checks passed

The output is clear, evidence-based, and directly usable by a PM with at most minor wording tweaks; it flags a data discrepancy and bonus risk proactively.

Output

Proposed onboarding OKRs — next quarter

Objective: Help new teams experience shared value quickly and keep collaborating.

Key resultBaseline → target
1. Increase the share of new teams still active in week 4. This is our contribution to the company’s retention goal, shared with other squads that influence it.34% → 40%
2. Increase the share of new teams with three or more people joining in their first week.29% → 40%
3. Reduce median time from sign-up to the first note opened by another team member. The share of teams reaching this milestone within seven days must not decline.3 days → 1 day

The targets for KRs 2 and 3 are proposed, pending a feasibility review before leadership approval. They are not forecasts of retention impact.

Measurement and quality guardrails

  • Freeze cohort eligibility, “active,” membership, and shared-note definitions before the quarter starts. Use consistent definitions for baselines and results.
  • Score week-4 retention only on cohorts that have reached week 4; report immature cohorts separately.
  • Reconcile the data before approval: the supplied segment figures imply approximately 32% overall retention, rather than 34%. Confirm whether they use different cohorts or definitions.
  • Track onboarding reliability alongside these KRs. Critical onboarding defects are release blockers, not a bug-count target.
  • Improve collaboration through genuine value—not forced invitations or changes that artificially inflate membership.

Candidate initiatives — not scored key results

Prioritize the checklist, invite-flow improvements, a small set of useful templates, and lifecycle emails according to their expected contribution to these outcomes. Use interviews and funnel analysis to choose and refine experiments.

Defer dark mode and AI summaries unless evidence shows they address a major onboarding barrier. Publish help content where research identifies a specific need.

Note to the squad

I replaced three broad objectives and a shipping checklist with one focused objective and measurable user outcomes. Launches, interviews, articles, and bug fixes are ways to make progress; completing them does not prove that onboarding improved.

Early team participation is our strongest available signal: teams with three or more people joining in week 1 have much higher week-4 retention. That is correlation, not proof of causation, so we’ll test whether our changes improve retention rather than assume invitations alone will do it.

I also narrowed the scope for our five-engineer, one-designer squad. NPS is too broad to diagnose this onboarding problem, while dark mode and AI summaries would compete with the core work.

Because OKR scores now affect bonuses, I want targets, scoring rules, guardrails, and shared ownership agreed before the quarter—not adjusted afterward. We should review the bonus linkage with leadership and HR so it does not reward metric manipulation or discourage honest experiments.

Grades and run details

Decision model 95 · LLM judge 100
Decision model checks
  • passUses the supplied evidence correctly49%
  • partialAddresses the actual decision22%
  • passRespects explicit constraints51%
  • passIdentifies material uncertainty56%
  • passAvoids unsupported claims64%
  • passProduces the required deliverable86%
  • passBuilds the key results on the data100%
  • passFlags the bonus link55%
  • passKey results are outcomes, not output98%
  • passFocuses on the big rock100%
  • passShows how the goals add up66%
Run
Run
#1
API response time
27 s
Submitted
5 Oct 2026

Needs more than a quick edit6 of 11 checks passed

The flawed arithmetic about 45% hitting 40% retention would mislead leadership and requires rework, so a PM cannot use this as-is without re-checking the numbers.

Output

Q3 Onboarding Squad OKRs (Revised)

Objective: Get new teams collaborating in Fieldnote during their first week.

  • KR1: Increase the share of new teams with 3+ members in their first week from 29% to 45%.
  • KR2: Reduce the median time from sign-up to a teammate opening a shared note from 3 days to under 24 hours.
  • KR3: Increase Week 4 retention for new sign-up cohorts from 34% to 40%.

(Note: Previously listed features like the invite flow redesign, templates, and onboarding emails will now live on our roadmap as the bets we’ll place to hit these numbers).

*

Note to the Squad: What I changed and why

Hey team, thanks for pulling the draft together. Before we send this to leadership on Friday, I’ve made some big cuts and reframed our goals. Because HR is tying our bonus multipliers directly to our OKR score this year, it’s critical that we set ourselves up to win with goals we actually control and can realistically hit.

Here is the thinking behind the changes:

1. One focused Objective tied to company math Leadership’s only goal this quarter is moving Week 4 retention from 34% to 40%. Look at our data: teams with 3+ members in week one retain at 58%, vs. 21% for everyone else. Right now, only 29% of teams hit that 3-member mark. By mathematical average, if we just move the needle on that 3-member metric from 29% to ~45%, we will single-handedly hit the company’s 40% retention target. Furthermore, since Fieldnote charges at 6+ members, getting teams to 3+ in week one is the best thing we can do for the business.

2. Moving from outputs to outcomes Our draft KRs (checklists, templates, invite flow, emails) were a task list. If we ship all four and retention stays at 34%, leadership won’t view the quarter as a success. By making the KRs about behavior (inviting teammates and sharing a first note quickly), we give ourselves the freedom to experiment. We’ll still build the invite redesign and templates, but if they work early, we don't have to build the rest. If they don’t work, we can pivot without failing our OKRs.

3. Protecting our bandwidth (and our bonuses) We are five engineers and one designer. The draft had us building dark mode, AI summaries, and four onboarding features, plus fixing 50 bugs. That’s way too much for one quarter. * Cut "Delight Users" (Obj 3): Dark mode and AI summaries are huge scope creeps for an onboarding team. Also, NPS is a lagging, company-wide metric—we shouldn’t tie our bonuses to a number that core product bugs or pricing changes can tank. User interviews and help articles are great, just as part of our weekly process. * Cut "Improve Quality" (Obj 2): Fixing 50 bugs incentivizes us to hunt for 50 tiny typos, while "zero P1s" is a baseline engineering standard, not a quarterly goal. We will keep squashing bugs in our normal sprint triage, especially anything breaking the sign-up or invite flows, without making it an OKR.

Let’s chat in standup tomorrow to make sure everyone feels good about these numbers before I send them up on Friday!

Claims the judge couldn’t find in the brief

  • If we just move the needle on that 3-member metric from 29% to ~45%, we will single-handedly hit the company's 40% retention target.
  • NPS is a lagging, company-wide metric.

Grades and run details

Decision model 77 · LLM judge 58
Decision model checks
  • passUses the supplied evidence correctly43%
  • failAddresses the actual decision18%
  • passRespects explicit constraints56%
  • partialIdentifies material uncertainty35%
  • partialAvoids unsupported claims28%
  • passProduces the required deliverable91%
  • passBuilds the key results on the data100%
  • partialFlags the bonus link94%
  • passKey results are outcomes, not output97%
  • passFocuses on the big rock100%
  • passShows how the goals add up73%
Run
Run
#1
API response time
10 s
Submitted
13 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 5

Uses the supplied evidence correctlyRightMixed
GPT-6.1 Sol · API

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

Gemini 4 Argon · API

The claim that moving the 3-member metric to 45% hits 40% retention is arithmetically wrong (45% yields 37.65%, not 40%), so evidence is not used correctly.

Addresses the actual decisionRightMixed
GPT-6.1 Sol · API

The output commits to a clear rewritten OKR set and a note explaining the changes, framed for the squad, and states that targets are proposed pending feasibility review.

Gemini 4 Argon · API

The output commits to a clear rewritten OKR set and a note to the squad, addressing the ask.

Identifies material uncertaintyRightWrong
GPT-6.1 Sol · API

It identifies the data discrepancy, the correlation vs. causation risk, and the need for feasibility review, and says how to resolve them.

Gemini 4 Argon · API

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

Avoids unsupported claimsRightWrong
GPT-6.1 Sol · API

Hypotheses and causes are clearly labelled (e.g., 'correlation, not proof of causation'), and no interpretations are presented as established fact.

Gemini 4 Argon · API

It presents the 45%-to-40% arithmetic as fact, and NPS as company-wide, without support from the brief.

Flags the bonus linkRightWrong
GPT-6.1 Sol · API

The output flags the risk of bonus-linked OKRs leading to safe targets or manipulation, and suggests reviewing the linkage with leadership and HR.

Gemini 4 Argon · API

It flags the bonus link but only suggests setting controllable goals, not separating bonuses from OKR scores or another concrete fix like keeping them apart.

All got right 6

Respects explicit constraintsRightRight
GPT-6.1 Sol · API

The output is a note to the squad with rewritten OKRs, well under 600 words, and respects the requested form and reader.

Gemini 4 Argon · API

The output is under 600 words, includes the OKRs and a note to the squad, respecting the form and reader.

Produces the required deliverableRightRight
GPT-6.1 Sol · API

The deliverable is a complete, usable OKR rewrite and squad note, in the right form and within the word limit.

Gemini 4 Argon · API

The required deliverable—rewritten OKRs and a note to the squad—is present and usable within the word limit.

Builds the key results on the dataRightRight
GPT-6.1 Sol · API

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.

Gemini 4 Argon · API

KR1 uses the 3+ member share with baseline 29%, and KR2 uses time to first shared note with baseline 3 days.

Key results are outcomes, not outputRightRight
GPT-6.1 Sol · API

All key results are measurable outcomes with baselines and targets; shipping work appears only as candidate initiatives.

Gemini 4 Argon · API

All key results are measurable behavior changes (3+ member share, time to shared note, week-4 retention); shipping work is listed as initiatives.

Focuses on the big rockRightRight
GPT-6.1 Sol · API

The output reduces the draft to one objective with three KRs, explicitly drops dark mode, AI summaries, and NPS, and explains why.

Gemini 4 Argon · API

The output cuts the draft's many objectives down to one objective with three KRs, and explains what was dropped (dark mode, AI summaries, bug-fix objective).

Shows how the goals add upRightRight
GPT-6.1 Sol · API

Every key result is linked to the company retention goal, with KR1 directly shared and KRs 2-3 as leading indicators, and the reasoning is explained.

Gemini 4 Argon · API

Each KR is linked to the company goal via the data on early collaboration driving retention, and the output explains the connection.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated. Where our PM checked the graders by hand, they agreed on 100% of 12 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT97.7100.02None
2GPT-6.1 SolwithAPI97.7100.02None
3Sonnet 5.5withAPI100.091.72None
4Haiku 5.5withAPI93.295.82None
5Opus 5.5withClaude95.579.22None
6GPT-6 LunawithAPI79.579.22None
7Gemini 4 ArgonwithAPI77.370.82None
8Gemini 3.8 FlashwithAPI61.462.52None
9Gemini 3.5 Flash-LitewithGemini59.145.82None

What you get for the cost

Haiku 5.5 · API scores within 4 points of GPT-6 Astra · ChatGPT at about a thirtieth of the cost.

Task score against relative cost for each setup. The table below has the same figures.4050607080901001×3×10×30×100×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Astra · ChatGPT: task score 99, 83× the cheapestGPT-6.1 Sol · API: task score 99, 16× the cheapestSonnet 5.5 · API: task score 96, 44× the cheapestHaiku 5.5 · API: task score 95, 2.1× the cheapestOpus 5.5 · Claude: task score 83, 78× the cheapestGPT-6 Luna · API: task score 78, the cheapestGemini 4 Argon · API: task score 71, 13× the cheapestGemini 3.8 Flash · API: task score 59, 13× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 50, 2.1× the cheapestGPT-6 Astra · ChatGPTGPT-6.1 Sol · APISonnet 5.5 · APIHaiku 5.5 · APIOpus 5.5 · ClaudeGPT-6 Luna · APIGemini 4 Argon · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Best valueNobody beats these on both score and cost
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API781.0×
Gemini 3.5 Flash-Lite · Gemini502.1×
Haiku 5.5 · API952.1×
Gemini 3.8 Flash · API5913×
Gemini 4 Argon · API7113×
GPT-6.1 Sol · API9916×
Sonnet 5.5 · API9644×
Opus 5.5 · Claude8378×
GPT-6 Astra · ChatGPT9983×

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

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