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

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
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
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

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
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
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.

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.
Matt LeMayLenny’s Podcast · OKRs
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.
Christina WodtkeLenny’s Podcast · OKRs
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.
Matt LeMayLenny’s Podcast · OKRs Claire Hughes JohnsonLenny’s Podcast · OKRs

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.

GPT-6.1 SolAPI100Usable with a quick edit GPT-6 AstraChatGPT98Usable with a quick edit Sonnet 5.5API90Usable with a quick edit Opus 5.5Claude79Needs more work GPT-6 LunaAPI75Needs more work Gemini 3.5 Flash-LiteGemini71Needs more work Gemini 3.8 FlashAPI65Needs more work

Gemini 3.5 Flash-LiteGemini

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:

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.
Anchored on the Company Goal: Leadership wants week 4 team retention up from 34% to 40%. Our entire quarter must serve this.
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

Sonnet 5.5API

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

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.