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 20 graded outputs by 7 models. 65% were usable with at most a quick edit.

Reliably right

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
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

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 a Staff PM at Pathwise, working for the CPO, Grace Mensah. Last quarter's OKR scorecard is below, along with the company goal it was meant to serve. Grace wants a memo of no more than 1,100 words: what last quarter's OKRs actually tell us (not just the scores), and next quarter's OKRs for the Learner app, Admin and Growth teams, each with baselines and targets, that add up to the company goal.\n\nThe files are attached.

What the model was given5 items: About Pathwise, company_okr_q3.md, okr_scorecard_q3.csv (team_label is the type each team gave its key result), metric_dictionary.md, churn_reasons_q3.md

About PathwiseOnline learning for companies: courses employees take, and tools for the HR and L&D teams who buy it. Sold per seat on annual contracts.

company_okr_q3.md7 lines · Download

# Company OKR, Q3 (set by the CEO)

Objective: grow by keeping and expanding the customers we have.
- Net revenue retention: from 103% to 108%. Actual: 102%.
- Quarterly logo churn: from 3.1% to 2.5%. Actual: 3.4%.

Q3 average key result score across product teams: 0.81. The all-hands slide said "a great quarter".

okr_scorecard_q3.csv (team_label is the type each team gave its key result)team,objective,key_result,team_label,baseline,target,actual,score Content,Best course library in L&D,Publish 40 new courses,output,0,40,41,1.00 Content,Best course library in L&D,Course completion rate,outcome,41,55,58,1.00 Content,Best course library in L&D,Translate top 20 courses into 3 languages,output,0,60,60,1.00 Learner app,Learning people love,Ship mobile offline mode,output,0,1,1,1.00 Learner app,Learning people love,Weekly active learners per seat,outcome,0.31,0.4,0.29,0.00 Learner app,Learning people love,Ship streaks and badges,output,0,1,1,1.00 Learner app,Learning people love,App store rating,outcome,4.2,4.6,4.5,0.75 Admin,Admins get value fast,Launch 3 HRIS integrations,outcome,0,3,3,1.00 Admin,Admins get value fast,Days from contract to first learner,outcome,34,14,31,0.15 Admin,Admins get value fast,Ship new reporting dashboard,output,0,1,1,1.00 Growth,Expand inside accounts,Seats added in existing accounts,outcome,9200,14000,8100,0.00 Growth,Expand inside accounts,Run 12 expansion experiments,output,0,12,13,1.00 Growth,Expand inside accounts,Launch in-app upgrade prompts,output,0,1,1,1.00 Platform,Reliable at scale,Uptime,outcome,99.5,99.9,99.93,1.00 Platform,Reliable at scale,Migrate to new video CDN,output,0,1,1,1.00 Platform,Reliable at scale,Close 150 bugs,output,0,150,162,1.00

metric_dictionary.md5 lines · Download

# Metric dictionary (excerpt)

- Course completion rate: share of course enrolments marked complete. Until 31 July "complete" meant every module finished. From 1 August it means 80% of modules finished (changed so completion matches the certificate rule).
- Weekly active learners per seat: learners who start or continue a course in a week, divided by paid seats.
- Days from contract to first learner: calendar days from contract signature to the first learner starting a course.

churn_reasons_q3.md8 lines · Download

# Why accounts left in Q3 (exit interviews, 38 accounts)

- "Our people didn't use it": 17
- Budget cut: 9
- Moved to the LMS that came with our HR system: 7
- Content didn't fit our industry: 5

…

What a strong answer doesThe answer key the graders mark against

Reads the scorecard for what it is: the average is 0.81, but every output key result scored 1.00 (things shipped) while outcome key results averaged about 0.48, and the company missed both goals (NRR 102% against 108%, churn 3.4% against 2.5%). Two scores flatter: 'Launch 3 HRIS integrations' is labelled an outcome but is output, and course completion only 'beat' its target because completion was redefined on 1 August (all modules to 80%), so the result can't be compared with its baseline. The outcome key results that matter for churn moved backwards or barely (weekly active learners per seat 0.31 to 0.29; days to first learner 34 to 31; seats added 8,100 against 9,200). Builds next quarter's OKRs on the churn evidence: low usage is the top exit reason, and accounts reaching a first learner within 14 days churn at a third of the rate. So Admin owns days from contract to first learner (from 31), Learner app owns weekly active learners per seat (from 0.29), Growth owns seats added in existing accounts or net expansion, each with a baseline from the files and a target, with shipping work as initiatives. Notes the completion metric needs restating on one definition before anyone uses it.

Critical failures (cap the score at 40)

Describes last quarter as a success on the strength of the 0.81 average

Task

Staff-level · v1.0 · synthetic · B2B SaaS, goals, data files, staff-level

The outputs

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

GPT-6 AstraChatGPT100Usable with a quick edit GPT-6.1 SolAPI98Usable with a quick edit Sonnet 5.5API94Usable with a quick edit GPT-6 LunaAPI90Usable with a quick edit Opus 5.5Claude85Needs more work Gemini 3.5 Flash-LiteGemini31Needs more work

Sonnet 5.5API

Usable with a quick edit11 of 11 checks passed

The memo is complete, evidence-based, and actionable; a PM could use it with minor wording edits without re-checking facts or redoing reasoning.

Output

MEMO To: Grace Mensah, CPO From: Staff PM Re: What Q3 OKRs tell us, and Q4 OKRs for Learner app, Admin and Growth

Bottom line

Q3 scored 0.81 and missed the company goal. NRR was 102% against a 103% baseline, and churn was 3.4% against 3.1%. Both got worse. Teams delivered what they planned, but the plan didn't move usage, and non-use is why customers leave. The Q4 OKRs below target usage and activation directly.

What Q3 actually tells us

1. The score measures delivery, not results. Ten of 16 KRs are outputs, and all ten scored 1.00. That count includes "Launch 3 HRIS integrations", which Admin labelled an outcome. The six true outcome KRs average 0.48. The 0.81 is mostly shipping.

2. What shipped didn't move the customer metrics. - Offline mode plus streaks and badges shipped, and weekly active learners per seat fell from 0.31 to 0.29. - Three integrations and a new dashboard shipped, and days to first learner moved only from 34 to 31. - Thirteen experiments and upgrade prompts shipped, and seats added fell from 9,200 to 8,100.

3. Customers told us why they left. Of 38 exits, 17 (45%) said their people didn't use it. Seven (18%) moved to their HRIS vendor's bundled LMS, nine cited budget and five cited content fit. Accounts reaching first learner within 14 days churned at a third of the rate of the rest. That could partly be selection, since healthier customers may simply move faster. It is still the strongest lead we have.

4. Two "wins" shouldn't be trusted. - Completion rate (41% to 58%) is not evidence. The definition loosened on 1 August from all modules to 80%, so the baseline and actual are measured differently. It needs restating on one definition before anyone cites it. - Uptime hit target, but no exit cited reliability. That is good hygiene, not a retention lever.

5. Process fix. Outputs should be initiatives, not KRs. Every team should carry at least one KR tied to a churn or NRR driver, and we should report outcome KRs separately from the average.

Q4 OKRs

Assumption (please confirm): the company goal carries forward unchanged: NRR 102% to 108% and quarterly logo churn 3.4% to 2.5%.

Learner app: Learners come back every week

Key result	Baseline	Q4 target
Weekly active learners per seat	0.29	0.35
Shared with Admin: churn attributed to non-use (pts of the 3.4)	1.5	0.75
Guardrail: app store rating	4.5	≥4.5

Admin: Admins get learners learning in two weeks

Key result	Baseline	Q4 target
Days from contract to first learner	31	21
Churn from accounts moving to an HRIS-bundled LMS (pts)	0.6	0.45
Shared with Learner: churn attributed to non-use (pts)	1.5	0.75

Growth: Expand where seats are used

Key result	Baseline	Q4 target
Seats added in existing accounts	8,100	12,000

Growth's experiments should target accounts with healthy usage, since expansion into idle accounts is unlikely. Growth also depends on Learner's usage KR.

How it adds up

Churn: 3.4% to 2.5%. I split the 3.4 points by exit reason, assuming the 38 interviews are representative.

Reason	Share of exits	Pts of churn	Q4
Non-use	45%	1.5	0.75
Budget	24%	0.8	0.8
HRIS-bundled LMS	18%	0.6	0.45
Content fit	13%	0.4	0.4
Total		3.4	~2.4

Halving non-use churn takes 0.75 points, shared between Learner (usage) and Admin (activation). The HRIS reduction takes 0.15. That gets to about 2.4, leaving 0.1 of buffer. I've put nothing against budget or content fit, since neither is addressable this quarter.

NRR: 102% to 108%. I'm assuming a 0.9-point quarterly churn improvement is worth about 3.6 NRR points annualised. Expansion has to supply the other 2.4. The 12,000 seats target (about 30% above Q2's 9,200) is a placeholder sized to that gap. I don't have the seat base or ARR to convert it exactly.

Risks and asks

The churn target is a stretch. On annual contracts, Q4 churn comes from accounts renewing in Q4, so a 31-to-21-day onboarding improvement mostly pays off at later renewals. I want a list of accounts renewing before the end of Q1, with their usage. Learner and Admin should work that list first, so Q4 effort lands on accounts that can still change their decision. If we can't act on it, a realistic fallback is about 2.9% churn and 104-105% NRR. I'd rather agree that with the CEO now than explain it in January.
Finance: please confirm how churn converts to NRR and what 12,000 seats is worth in NRR points.
Data: restate completion rate on the 80% rule for Q2 and Q3.
Exit data: 38 interviews is a small sample, and I assumed it covers all churn. Please confirm.
Decision for you: confirm the Q4 company goal, and whether you'll back splitting the score into output and outcome in the next all-hands.

Grades and run details

Decision model 86 · LLM judge 12 of 12 checks

Decision model checks

passUses the supplied evidence correctly10%
passAddresses the actual decision53%
partialRespects explicit constraints15%
passIdentifies material uncertainty98%
partialAvoids unsupported claims34%
partialProduces the required deliverable31%
passReads the scorecard for what it is93%
passCatches the redefined key result100%
passKey results are outcomes, not output81%
passFocuses on the big rock56%
passShows how the goals add up29%

Run

Run: #1
API response time: 1.5 min
Submitted: 6 Oct 2026

GPT-6.1 SolAPI

Usable with a quick edit11 of 11 checks passed

The memo is evidence-based, correctly reads the scorecard, and provides actionable OKRs; a PM could use it with only a quick edit to fill in the revenue baselines once Finance provides them.

Output

To: Grace Mensah, CPO From: Staff PM Subject: Q3 delivered features, not retained growth; proposed Q4 OKRs

What Q3 actually tells us

We missed the company objective despite a 0.81 product score. NRR fell from 103% to 102%, versus a 108% target. Quarterly logo churn rose from 3.1% to 3.4%, versus a 2.5% target. Calling this “a great quarter” confuses delivery with business impact.

The scorecard explains that disconnect:

Shipping dominated scoring. Ten of the 16 KRs measured outputs, including “Launch 3 HRIS integrations,” incorrectly labeled an outcome. All ten scored 1.00. The six genuine outcome KRs averaged approximately 0.48, not 0.81.
Learner value weakened while features shipped. Weekly active learners per paid seat fell from 0.31 to 0.29 despite offline mode, streaks and badges. A better app-store rating does not demonstrate adoption across employer accounts.
Admin setup barely improved. Contract-to-first-learner time moved from 34 to 31 days, far short of 14. Shipping integrations and reporting did not establish that customers reached value faster.
Expansion weakened despite experimentation. Seats added in existing accounts fell from 9,200 to 8,100—about 12%—while experiments and upgrade prompts exceeded delivery targets. Seats are also not revenue: discounts, seat removals and account losses can offset additions.
The content “win” is not comparable. Completion rose from 41% to 58%, but the definition changed from finishing every module to finishing 80%. We cannot separate real improvement from reclassification without recalculating both periods under one definition.
Reliability genuinely improved. Uptime exceeded target. That is valuable, but insufficient evidence of retention or expansion impact.

The churn evidence suggests where to intervene, without proving causality. Low usage was cited by 17 of 38 interviewed departing accounts, the largest category. Accounts reaching a first learner within 14 days churned at one-third the rate of other accounts. These findings support prioritizing activation and adoption; they do not establish that accelerating activation will itself cut churn by two-thirds. Customer characteristics may explain part of the association.

Q4 company commitment

Retain the CEO’s destination, rebased to Q3 actuals:

Company KR	Baseline	Q4 target
---	---:	---:
Net revenue retention	102%	108%
Quarterly logo churn	3.4%	2.5%

Before scoring starts, Finance must confirm consistent NRR periods, cohort rules and revenue treatment. Annual contracts make renewal timing especially important.

Proposed team OKRs

Feature launches become initiatives, not scored KRs. Each team owns customer or commercial results.

Team / objective	Key result	Baseline	Q4 target
---	---	---:	---:
Learner app: Make paid seats deliver sustained learning value	Weekly active learners per paid seat, using the existing definition	0.29	0.40
Admin: Get customers to value quickly and protect renewal revenue	Days from contract to first learner, using the same aggregation as Q3	31 days	14 days
Admin	Existing-customer revenue lost to churn and contraction, divided by opening cohort revenue	L₃, to be calculated from Q3 billing	L₃ − 3 percentage points
Admin, shared with Learner and CS	Quarterly logo churn	3.4%	2.5%
Growth: Turn demonstrated customer value into paid expansion	Existing-customer expansion revenue, divided by opening cohort revenue	E₃, to be calculated from Q3 billing	E₃ + 3 percentage points
Growth	Seats added in existing accounts, retaining Q3’s definition	8,100	14,000

The logo-churn KR is one shared result, not three independently credited achievements. Admin coordinates the retention plan with CS; Learner owns adoption improvements. Growth’s revenue KR takes precedence over its seat-volume KR: buying seat growth through discounts is not success.

The revenue baselines are deliberately not invented. The supplied scorecard lacks expansion revenue, contraction revenue, churned revenue and opening recurring revenue. Finance must populate E₃ and L₃ before final approval. If Q3 revenue loss was below three percentage points, the proposed loss reduction is impossible; we must shift that portion of the six-point improvement budget to expansion and reassess feasibility.

How these add up

Using a consistent existing-customer cohort definition:

NRR = 100% + expansion revenue rate − churn/contraction revenue rate.

Q3 therefore implies E₃ − L₃ = 2 percentage points. The proposed Q4 improvement budget is:

Growth: three additional percentage points of expansion;
Admin, supported by Learner and CS: three fewer percentage points of revenue loss.

Together: 100% + (E₃ + 3) − (L₃ − 3) = 108%.

This is a six-point commercial bridge, not a claim that engagement or activation mechanically converts into revenue. Logo churn remains a separate constraint: retaining small accounts could improve logo churn without materially improving NRR. Conversely, one large expansion could improve NRR while many customers leave.

Operating discipline

In week one, Finance, Product and CS should establish the revenue baselines, renewal exposure and account-level retention/expansion plan. Validate that the improvement budget is achievable before locking the OKRs.

Review weekly by account cohort, not just company averages. Track activation coverage alongside time-to-first-learner so one early learner cannot conceal a stalled rollout. Inspect engagement across accounts so a few large customers cannot conceal widespread non-use.

Recalculate completion under a constant definition. Keep uptime as a guardrail. Do not average feature delivery and customer outcomes into another celebratory score: Q4 is successful only if customer value improves and the company retention goals are met.

Grades and run details

Decision model 95 · LLM judge 12 of 12 checks

Decision model checks

passUses the supplied evidence correctly62%
passAddresses the actual decision90%
passRespects explicit constraints20%
passIdentifies material uncertainty99%
passAvoids unsupported claims78%
partialProduces the required deliverable72%
passReads the scorecard for what it is100%
passCatches the redefined key result100%
passKey results are outcomes, not output74%
passFocuses on the big rock58%
passShows how the goals add up66%

Run

Run: #1
API response time: 62 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.

All got right 11

Uses the supplied evidence correctlyRightRight

Sonnet 5.5 · API

All claims about the current situation are directly from the supplied context or follow by arithmetic.

GPT-6.1 Sol · API

All factual claims about the current situation are taken directly from the supplied context or derived by arithmetic; no invented numbers or facts.

Addresses the actual decisionRightRight

Sonnet 5.5 · API

Commits to clear Q4 OKRs with baselines and targets, and says what would change the plan (confirming company goal, data availability).

GPT-6.1 Sol · API

The memo commits to a clear set of Q4 OKRs with baselines and targets, addresses the CPO, and specifies conditions (revenue baselines, feasibility) that would change the plan.

Respects explicit constraintsRightRight

Sonnet 5.5 · API

Memo is under 1,100 words, addressed to Grace Mensah from Staff PM, and includes the requested sections.

GPT-6.1 Sol · API

The output is a memo to Grace Mensah, well under 1,100 words, and respects the requested form and reader.

Identifies material uncertaintyRightRight

Sonnet 5.5 · API

Names assumptions (company goal, churn-to-NRR conversion, seat base) and asks for confirmations; provides a fallback if onboarding can't affect Q4 renewals.

GPT-6.1 Sol · API

It names missing revenue baselines, the redefined completion metric, and the possibility that Q3 loss is too low to hit the target, and says how to resolve each.

Avoids unsupported claimsRightRight

Sonnet 5.5 · API

Interpretations like selection bias are labelled as possibilities ('could partly be'), and conclusions are drawn from the data.

GPT-6.1 Sol · API

Interpretations and causal claims are clearly labelled as suggestions or hypotheses, not presented as established fact.

Produces the required deliverableRightRight

Sonnet 5.5 · API

Provides a complete memo with analysis of Q3, Q4 OKRs for the three teams with baselines and targets, and a ladder to the company goal.

GPT-6.1 Sol · API

The memo provides the required analysis of Q3 and proposed Q4 OKRs for the three teams, with baselines and targets, and is usable as-is.

Reads the scorecard for what it isRightRight

Sonnet 5.5 · API

Splits output KRs (all 1.00) from outcome KRs (average 0.48) and contrasts with the missed company goals (NRR 102%, churn 3.4%).

GPT-6.1 Sol · API

It splits the scores into output (all 1.00) and outcome (average ~0.48) and contrasts them with the missed company goals.

Catches the redefined key resultRightRight

Sonnet 5.5 · API

Catches the redefined completion metric (baseline and actual not comparable) and the mislabelled 'Launch 3 HRIS integrations' as output.

GPT-6.1 Sol · API

It catches both the redefined completion metric (definition change on 1 August) and the integrations KR being output labelled as outcome.

Key results are outcomes, not outputRightRight

Sonnet 5.5 · API

All Q4 key results are outcome metrics (weekly active learners, churn points, days to first learner, seats added) with baselines and targets; no shipping KRs.

GPT-6.1 Sol · API

Every proposed key result is a measurable change in customer or business behaviour with a baseline and target; shipping work is explicitly moved to initiatives.

Focuses on the big rockRightRight

Sonnet 5.5 · API

Focuses on usage and activation, drops content and budget as unaddressable this quarter, and gives each team a single objective with few KRs.

GPT-6.1 Sol · API

The OKRs are cut to a few critical outcomes per team, dropping output KRs and focusing on activation, time-to-value, expansion revenue, and churn.

Shows how the goals add upRightRight

Sonnet 5.5 · API

Links each KR to churn or NRR: non-use churn shared between Learner and Admin, HRIS churn to Admin, seats added to NRR, with arithmetic showing how they add up to the company goal.

GPT-6.1 Sol · API

The memo shows how each team KR contributes to NRR and logo churn via a revenue bridge formula, and flags the separate logo churn constraint.

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 · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6 AstrawithChatGPT	98.5	100.0	3	None
2	GPT-6.1 SolwithAPI	98.5	100.0	3	None
3	Sonnet 5.5withAPI	89.4	94.4	3	None
4	GPT-6 LunawithAPI	90.9	86.1	3	None
5	Opus 5.5withClaude	90.9	86.1	3	None
6	Gemini 3.8 FlashwithAPI	68.2	70.8	2	None
7	Gemini 3.5 Flash-LitewithGemini	65.2	50.0	3	None

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

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

Reliably right

Where it slips

How it’s graded

Key results are outcomes, not output

Focuses on the big rock

Shows how the goals add up

Plus our standard checks

The tasks

The outputs

Output

Bottom line

What Q3 actually tells us

Q4 OKRs

Learner app: Learners come back every week

Admin: Admins get learners learning in two weeks

Growth: Expand where seats are used

How it adds up

Risks and asks

Grades and run details

Decision model checks

Run

Output

What Q3 actually tells us

Q4 company commitment

Proposed team OKRs

How these add up

Operating discipline

Grades and run details

Decision model checks

Run

Check by check

All got right 11

Results

About the task

The PM job

Why it matters

What good looks like

Deliberately not measured