Tasks / Product Craft

Build a roadmap

Can the model sequence bets against capacity and dependencies, and explain the order?

Task type v1.0 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

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

From 16 graded outputs by 8 models. 69% were usable with at most a quick edit.

Reliably right

Produces the required deliverable95% pass
The roadmap is complete, in the right form for a Group PM, within the word limit, and actionable without major gaps.
GPT-6.1 Sol · API · Fourteen requests from one customer
Outcomes, with certainty that falls with distance94% pass
Every item names its outcome (e.g., reduce paper transcription, reduce manual reconciliation), and near-term items are specific while later ones are deliberately looser (e.g., '7 weeks for rollout, workflow improvement and contingency').
GPT-6.1 Sol · API · Fourteen requests from one customer
Handles the big customer with evidence94% pass
It uses Calder's rising usage and the second-hand 'might leave' to propose a conversation first, not committing the report builder, and offers a bounded export if needed.
GPT-6.1 Sol · API · Fourteen requests from one customer

Where it slips

Sees through the request count72% pass
The output never counts requests by requester, does not mention that 14 requests come from Calder or that several are duplicates, and does not compare themes by customer count.
GPT-6 Luna · API · Fourteen requests from one customer
Uses the supplied evidence correctly72% pass
States credit notes as 4 customers, but the request log contains 5 credit note tracking requests.
Sonnet 5.5 · API · Fourteen requests from one customer
Fits the stated capacity73% pass
Q3 Expenses row lacks squad-week allocation, so the committed plan cannot be fully checked against the 11-week capacity.
Opus 5.5 · Claude · A year of spend management, with a hard deadline

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.

Outcomes, with certainty that falls with distance
Does each roadmap item say the problem or outcome it serves, and does precision (dates, scope) decrease for later items?
Passes when Every item names its outcome or problem. Near-term items are specific and later ones are deliberately looser.
Melissa PerriLenny’s Podcast Christian IdiodiLenny’s Podcast
Fits the stated capacity
Does the committed plan fit the team capacity the brief gives, with sums a reader could check, and does it say what was cut or deferred to make it fit?
Passes when The committed work adds up to no more than the stated capacity, with some slack, the sums can be checked, and it names what was cut or pushed later and why.
Gokul RajaramLenny’s Podcast
Tells the story of the bets
Does the roadmap group the work into a few themes and explain why those are the biggest things to invest in now, rather than presenting a scored or ranked list of projects?
Passes when A handful of themes, each with the reason it matters most now, and the items sit under them.
Jiaona ZhangLenny’s Podcast

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 · Sequences around dependencies · 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 Group PM at Fernhill. Build the roadmap for the next half (Q1 and Q2) for our two squads, in no more than 900 words plus a table: what each squad builds, in what order, the outcome each item serves, what we're not doing and why, and what we tell Calder Group. The request log, usage, capacity and goal are attached.

What the model was given5 items: About Fernhill, company_goal_h1.md, feature_requests_last_6_months.csv, feature_usage.md, capacity_and_estimates.md

About FernhillPurchasing and inventory software for restaurants and hotels: orders, deliveries, invoices and stock.

company_goal_h1.md5 lines · Download

# Company goal for the half

Cut churn among independent restaurants (82% of accounts, 61% of revenue) from 2.4% to 1.6% a month. Top churn reason in exit surveys: "too much admin; didn't save us time on deliveries and invoices".

Calder Group renews in May. Their account manager says they "might" leave without the report builder; they haven't raised it with Customer Success, and their usage is up 20% this year.

feature_requests_last_6_months.csv53 rows · Download

request,requester,requester_arr_usd,source,title,theme
FR-300,Customer 553,4353,Support ticket,Credit note tracking,Invoicing
FR-301,Customer 391,9773,Sales call,Match invoices to deliveries automatically,Invoicing
FR-302,Customer 341,4856,Support ticket,Match invoices to deliveries automatically,Invoicing
FR-303,Calder Group,186000,Account manager,ERP export (CSV variant),Integrations
FR-304,Customer 895,9595,Sales call,Mobile receiving (check deliveries on a phone),Receiving
FR-305,Calder Group,186000,Account manager,Report builder with pivots,Reporting
…

feature_usage.md8 lines · Download

# Usage of related features today (last 30 days, 1,140 accounts)

| Feature | Accounts using it | Note |
|---|---|---|
| Standard reports | 212 (19%) | Calder Group uses them daily |
| Receiving checklist (desktop only) | 640 (56%) | 71% of uses start on a desktop, then get re-typed from paper |
| Invoice upload | 870 (76%) | Matching is manual |
…

capacity_and_estimates.md5 lines · Download

# Capacity, Q1 to Q2

Two squads, about 10 squad-weeks of roadmap work each per quarter (40 in total over the half).

Estimates (squad-weeks): Mobile receiving 8. Flag short deliveries 3 (needs mobile receiving). Invoice matching 9 (needs the new invoice parser, 5). Credit note tracking 3 (needs invoice matching). Accounting export with credits 2 (needs credit note tracking). Custom report builder 12. ERP export 4. Approval chains 6. Site-level permissions 4. SSO 3.

What a strong answer doesThe answer key the graders mark against

Builds the half around the churn goal (independent restaurants, whose top exit reason is admin around deliveries and invoices), not the request count. The log has 53 requests, but 14 come from one customer, Calder Group, and several of theirs are the same thing under different names (report builder three times, ERP export twice, bulk SKU twice, scheduled reports twice); across everyone else, receiving and invoicing requests dominate (mobile receiving 9, invoice matching 8, flag short deliveries 7, credit notes 5, accounting export 4). Sequences the dependency chain: mobile receiving (8), then flag short deliveries (3); the invoice parser (5), then invoice matching (9), then credit note tracking (3), then the accounting export (2): about 30 squad-weeks, inside the 40 available with slack. Handles Calder with evidence: their usage is up 20% and they haven't raised leaving with Customer Success, so talk to them before committing the 12-week report builder; offer a smaller item (SSO, 3, or the ERP export, 4) if it fits. Says what's not being done (report builder, approval chains, dark mode, multi-currency) and why, with near-term items specific and later ones looser.

Critical failures (cap the score at 40)

Schedules an item before the item it depends on (for example invoice matching before the parser)

Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

GPT-6.1 SolAPI100Usable with a quick edit Opus 5.5Claude98Usable with a quick edit GPT-6 AstraChatGPT96Usable with a quick edit Sonnet 5.5API94Usable with a quick edit GPT-6 LunaAPI92Usable with a quick edit Haiku 5.5API90Usable with a quick edit Gemini 3.8 FlashAPI71Needs more work Gemini 3.5 Flash-LiteGemini48Needs more work

GPT-6.1 SolAPI

Usable with a quick edit12 of 12 checks passed

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

Output

H1 roadmap: remove delivery and invoice admin

Prioritise the receiving-to-invoice workflow, not a general reporting platform. Our goal is to reduce independent-restaurant monthly churn from 2.4% to 1.6%. These customers represent 82% of accounts and 61% of revenue; their leading exit reason directly names delivery and invoice admin.

The evidence supports this choice: - Invoice upload reaches 76% of accounts, but matching remains manual. - Receiving reaches 56%, with substantial paper-to-desktop re-entry. - Mobile receiving, invoice matching and shortage flagging attracted requests from 9, 8 and 7 customers, respectively. - Calder’s multiple requests represent one $186,000 account, not repeated incremental ARR. Its renewal risk warrants investigation, but the report-builder threat is currently second-hand and usage is growing.

Sequenced delivery plan

Squad names below describe their H1 ownership. Estimates are squad-weeks; each squad has ten per quarter.

Squad / quarter	Build order and allocation	Outcome served / release gate
Invoice Automation — Q1	1. New invoice parser: 5 weeks. 2. Begin invoice matching: 5 of 9 weeks.	Establish reliable invoice data, then build matching against deliveries. Validate representative invoices before relying on parsed data. Matching carries into Q2; do not promise a production release in Q1.
Receiving — Q1	1. Mobile receiving: 8 weeks. 2. Pilot, hardening and adoption: 2 weeks.	Let staff record deliveries where they happen, reducing paper transcription. Pilot on real delivery shifts before wider rollout.
Invoice Automation — Q2	3. Complete invoice matching: remaining 4 weeks. 4. Credit note tracking: 3 weeks. 5. Accounting export with credits: 2 weeks. 1 week reserved.	Reduce manual reconciliation, make outstanding credits visible, and avoid re-entering credit adjustments in accounting. Release sequentially; ambiguous matches require review, not silent acceptance.
Receiving — Q2	3. Flag short deliveries: 3 weeks, after mobile receiving. 7 weeks for rollout, workflow improvement and contingency.	Capture discrepancies at the door so invoice reconciliation starts with accurate received quantities. Use remaining capacity to improve adoption and fix measured workflow friction, not add speculative features.

Capacity: committed feature builds total 30 of 40 squad-weeks. The other ten are explicitly allocated to rollout, quality, adoption and contingency—not assumed available for an extra large feature. Each squad stays within its quarterly limit.

The two squads must agree early on delivery-line identifiers, received quantities and discrepancy states. Invoice matching must still handle ordinary deliveries without waiting for shortage flagging.

Success measures and checkpoints

The primary measure is independent-restaurant monthly churn, tracked monthly against the 1.6% goal. Report results by adoption cohort, while recognising that adoption alone does not prove causation.

In the first two weeks, baseline: - Admin minutes per delivery and invoice. - Paper/re-entry frequency. - Manual matching effort, match accuracy and exception rate. - Credit resolution time.

Set numerical improvement targets after that baseline, rather than inventing savings from request counts. Confirm pilot customers include independent restaurants; the request log does not establish every requester’s segment.

At the Q1 review, assess mobile adoption, time saved and parser readiness. During Q2, expand releases only when accuracy and support burden are acceptable. If adoption is weak, spend the reserved capacity removing friction before widening scope.

What we are not doing

Custom report builder this half: 12 weeks would take the selected feature programme to 42 weeks before rollout or contingency. Standard reporting has 19% penetration; Calder’s daily use makes its needs worth understanding, but does not establish broad demand for a builder.
ERP export: four weeks is feasible in isolation, but is not presently justified ahead of workflow adoption and reliability. Revisit only with verified renewal requirements and an explicit capacity trade-off.
Approval chains, site permissions and SSO: defer; they do not directly address the leading churn reason.
Custom dashboards, scheduled reports, custom PDFs/fields, SKU tooling, more suppliers, dark mode and multi-currency: no H1 commitment. Several lack estimates, and universal catalogue usage is not evidence that catalogue enhancements will reduce churn.

What we tell Calder Group

Hold a joint PM, CS and account-manager conversation early in Q1—not just before May. Validate who requires the builder, which decisions it supports, and whether renewal actually depends on it.

Tell them:

> “Our H1 investment is reducing delivery and invoice administration. A custom report builder is not committed for this half. We want to understand your specific reporting gaps and test whether existing reports or a bounded export can meet them before renewal.”

Agree a written reporting-gap assessment and a pre-renewal checkpoint. Any newly verified retention requirement triggers a leadership trade-off decision—not an uncosted promise or a quiet roadmap diversion.

Grades and run details

Decision model 100 · LLM judge 100

Decision model checks

passUses the supplied evidence correctly56%
passAddresses the actual decision98%
passRespects explicit constraints42%
passIdentifies material uncertainty96%
passAvoids unsupported claims50%
passProduces the required deliverable82%
passSees through the request count68%
passHandles the big customer with evidence98%
passOutcomes, with certainty that falls with distance85%
passFits the stated capacity87%
passSequences around dependencies76%
passTells the story of the bets100%

Run

Run: #1
API response time: 43 s
Submitted: 6 Oct 2026

Check by check

Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Got right 12

Uses the supplied evidence correctlyRight

All factual claims about the current situation are directly supported by the supplied context, with no invented numbers or facts.

Addresses the actual decisionRight

The output commits to a clear roadmap (no report builder, focus on receiving/invoice workflow) and states that a verified retention requirement would trigger a leadership trade-off.

Respects explicit constraintsRight

The output is under 900 words (536 words), includes the required table, and covers what each squad builds, order, outcomes, what's not done and why, and what to tell Calder.

Identifies material uncertaintyRight

It names specific unknowns (Calder's actual renewal dependency, adoption rates, parser accuracy) and describes how they will be resolved (joint conversation, pilot baselines, Q1 review).

Avoids unsupported claimsRight

Interpretations like Calder's threat being second-hand are clearly presented as analysis, not established fact, and no unsupported causal claims are made.

Produces the required deliverableRight

The roadmap is complete, in the right form for a Group PM, within the word limit, and actionable without major gaps.

Sees through the request countRight

The output explicitly notes Calder's 14 requests are from one account and counts demand by distinct customers (9, 8, 7) rather than raw request count.

Handles the big customer with evidenceRight

It uses Calder's rising usage and the second-hand 'might leave' to propose a conversation first, not committing the report builder, and offers a bounded export if needed.

Outcomes, with certainty that falls with distanceRight

Every item names its outcome (e.g., reduce paper transcription, reduce manual reconciliation), and near-term items are specific while later ones are deliberately looser (e.g., '7 weeks for rollout, workflow improvement and contingency').

Fits the stated capacityRight

Committed feature builds sum to 30 of 40 squad-weeks, with 10 explicitly reserved for rollout and contingency, and it names what was cut (report builder, ERP export, etc.).

Sequences around dependenciesRight

All dependencies are respected (parser before matching, mobile receiving before flagging, matching before credit notes, credit notes before accounting export) and named.

Tells the story of the betsRight

The work is grouped into two themes (Invoice Automation, Receiving) with a clear narrative about reducing delivery and invoice admin to cut churn, not a scored list.

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 79% of 150 checks.

#	Model · Harness	Decision model	LLM judge	Runs	Critical failures
1	GPT-6.1 SolwithAPI	100.0	96.4	2	None
2	GPT-6 AstrawithChatGPT	93.9	100.0	2	None
3	Opus 5.5withClaude	90.2	96.4	2	None
4	Sonnet 5.5withAPI	94.1	92.6	2	None
5	GPT-6 LunawithAPI	92.0	85.4	2	None
6	Haiku 5.5withAPI	74.2	96.4	2	None
7	Gemini 3.8 FlashwithAPI	74.2	66.8	2	None
8	Gemini 3.5 Flash-LitewithGemini	56.9	36.5	2	1 capped

What you get for the cost

No setup scores within 5 points of the leader at half its cost or less here, so the leader is also the best value.

Nobody beats these on both score and costAt least one critical failure

Task score and relative cost by setup
Setup	Task score	Relative cost (cheapest = 1)
GPT-6 Luna · API	87	1.0×
Gemini 3.5 Flash-Lite · Gemini	44	1.1×
Haiku 5.5 · API	86	2.1×
Gemini 3.8 Flash · API	68	5.5×
GPT-6.1 Sol · API	98	6.9×
Sonnet 5.5 · API	94	36×
Opus 5.5 · Claude	94	37×
GPT-6 Astra · ChatGPT	97	39×

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

The PM job

Turning strategy into a sequenced plan.

Why it matters

A roadmap is where strategy meets capacity. Dated feature lists turn guesses into promises.

What good looks like

Items are problems or outcomes, not just features
Sequencing reflects dependencies
Explicit trade-offs
Commitment falls with distance

Deliberately not measured

Gantt formatting

Capability tested

Sequencing under constraints

The failure we’re looking for

A dated wishlist sorted by excitement

Grading

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