Tasks / Product Craft

Write a PRD

Can the model turn a brief into a spec engineers could build from, including how an AI feature behaves when it is wrong?

Task type v1.2 · 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. 28% were usable with at most a quick edit.

Reliably right

  1. Specifies AI failure behaviour100% pass
    It specifies what happens when uncertain or wrong: block ownership, show retry states, disable assignments on violation, and never fall back to calendar data.
    GPT-6 Astra · ChatGPT · Meeting summaries with action items
  2. Builds the regulator's rules in100% pass
    All three rules (2-business-day acknowledge, 15-business-day resolve or explain, no harder than paying) appear as testable requirements R8, R10, R13.
    Sonnet 5.5 · API · Disputes that aren't disputes
  3. Produces the required deliverable96% pass
    The PRD is complete, written for engineering and design leads, within the 1,000–1,500 word range, and directly actionable.
    GPT-6 Astra · ChatGPT · Meeting summaries with action items

Where it slips

  1. Says who it's for, and who it isn't35% pass
    Does not name the target users (individual account holders) or explicitly exclude a group (e.g., business accounts), missing a required scope decision.
    Sonnet 5.5 · API · Disputes that aren't disputes
  2. Proposes tests that could fail36% pass
    The A/B expansion gate relies on a fraud-reversal guardrail but does not specify a numeric threshold, leaving the test incomplete.
    Sonnet 5.5 · API · Disputes that aren't disputes
  3. Plans how people will find it44% pass
    Gives a staged rollout and discovery method (reviewer notification), but does not name the teams that need to be ready (e.g., support, sales) beyond technical dependencies.
    GPT-6.1 Sol · API · Meeting summaries with action items

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. Success settles trade-offs

    Does the PRD define success as a measurable outcome with a target and time frame, and say which goal wins when two of them conflict?

    Passes when Names a success metric with a target and time frame, and gives at least one explicit trade-off rule (for example, accepting lower coverage to keep precision above a stated level).

  2. Says who it's for, and who it isn't

    Does the PRD name the specific users it is for, say explicitly who it is not for, and make at least one scope decision that follows from that choice?

    Passes when Names the target users and at least one group it deliberately leaves out, and a scope decision visibly follows from the exclusion.

  3. Plans how people will find it

    Does the PRD say how the feature reaches people: how it rolls out, how users discover it, and who needs to be ready for it (support, sales), rather than stopping at what to build?

    Passes when Gives a staged rollout, a way users will discover the feature, and the teams that need to be ready, all fitted to this feature.

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 · Proposes tests that could fail · and 1 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

Write a PRD for AI-generated meeting summaries that list the decisions made and assign action items to attendees. The PRD is for the engineering and design leads who will build it: write it so they could start work from it. Aim for 1,000–1,500 words.

What the model was given4 items: Scenario, Known limits, Constraint, Request from Sales
ScenarioHuddle is a video-meeting app used by 3,000 companies. Meetings are recorded and transcribed with speaker labels, and the attendee list comes from the calendar invite. Today people write their own notes after the call.
Known limitsSpeaker labels are wrong about 8% of the time, usually when people talk over each other or several people join from one meeting room. About 15% of meetings include someone who joined without a calendar invite.
ConstraintSummaries must never assign an action to someone who was not in the meeting.
Request from SalesSales wants action items emailed automatically to their owners as soon as each meeting ends.
What a strong answer doesThe answer key the graders mark against

Specifies attribution confidence, editing, and what happens on mis-assignment; keeps a person reviewing before action items go out.

Critical failures (cap the score at 40)
  • Assigns actions to non-attendees
Task

Mid-level · v1.20

The outputs

9 setups answered this brief.

Usable with a quick edit6 of 11 checks passed

The PRD is detailed and usable; a quick edit to remove the unsupported time claim and add a brief rollout note would make it complete.

Output

Product Requirements Document: AI Meeting Summaries & Action Items

Product: Huddle Author: Product Management Target Audience: Engineering & Design Leads Status: Ready for Kickoff

---

1. Overview & Problem Statement

Huddle currently records and transcribes video meetings for 3,000 B2B customers. However, users still spend 10–15 minutes after every call reviewing transcripts or typing up raw notes to document decisions and action items.

We are building an AI-powered post-meeting summary feature that automatically extracts Key Decisions and Action Items (with assigned owners) from meeting transcripts.

The Core Tension: Speed vs. Accuracy

Sales has requested that we automatically email action items to their owners the moment a meeting ends. However, we have three strict operational realities: 1. Hard Constraint: We must never assign an action item to someone who did not attend the meeting. 2. Data Gap 1 (The 15% problem): 15% of meetings include uninvited guests (e.g., forwarded links, conference rooms) who are missing from the calendar invite. 3. Data Gap 2 (The 8% problem): Speaker diarization/labels are wrong 8% of the time, primarily due to crosstalk or multiple people sharing one room mic.

Product Decision: If we auto-send emails immediately after the meeting ends, the 8% speaker attribution error rate and the 15% calendar mismatch rate will inevitably cause us to violate our hard constraint or embarrass users by emailing the wrong people. Therefore, v1 will use a "Human-in-the-Loop" flow with a fallback auto-send. For high-confidence extractions, emails will send automatically after a 30-minute review window. For low-confidence extractions (crosstalk, unverified guests), we will hold the email until the meeting host approves the summary.

---

2. Goals & Success Metrics

MetricTargetRationale
:---:---:---
Summary Adoption Rate> 60% of recorded meetingsMeasures core value of the feature (viewed or shared).
False-Absentee Attribution Rate0.0%Hard Constraint. No non-attendee is ever assigned an item.
Assignee Correction Rate< 10% of assigned action itemsMeasures how well our system handles the 8% diarization error.
Host Review Completion> 50% within 30 minutesValidates whether the host-in-the-loop notification UX works.

---

3. System Architecture & The "Actual Attendee" Roster

Right now, Huddle relies on the calendar invite for the attendee list. To obey our hard constraint, Engineering must build a unified `Actual_Attendees` service that fires when a meeting ends.

Defining an "Actual Attendee"

A person is only eligible to be assigned an action item if they exist in the `Actual_Attendees` array for that specific meeting session. Calendar invitees who did not join the call are explicitly excluded.

  1. Authenticated Huddle Users: Anyone who joins the call while logged into a Huddle account is added to `Actual_Attendees` (solves the 15% uninvited guest problem for logged-in users).
  2. Unauthenticated Web Guests: Guests joining via link must type their display name in the pre-call lobby. They are added to `Actual_Attendees` with `email: null`.
  3. Conference Room Hardware: When a room system joins (e.g., "4th Floor Conf Room"), the room itself is not an assignable person. (See Edge Cases in Section 4).

---

4. Functional Requirements (Engineering)

4.1 LLM Extraction Pipeline

* Trigger: Fires upon completion of the final meeting transcript. * Inputs: 1. Time-stamped transcript with speaker labels. 2. Diarization confidence scores per utterance. 3. The `ActualAttendees` JSON array (Name, UserID, Email, JoinMethod). 4. The `CalendarInviteesNoShow` array (Explicit negative constraint list). * Prompting & Guardrails: * Decisions: Extract 1–7 bullet points summarizing agreed-upon outcomes. Include timestamp citations. * Action Items: Extract tasks, deadlines (if mentioned), and the owner. * Assignment Logic: The LLM may only populate the `owner` field using an exact match from `ActualAttendees`.

4.2 Handling the Constraints & Edge Cases

  • Scenario A: Someone on the call says, "Let's have Sarah do this," when Sarah is NOT in `Actual_Attendees`.
  • System Behavior: The system must not assign the task to Sarah. Instead, it assigns the task to the speaker who volunteered Sarah (or leaves it "Unassigned") and rewrites the task text to: "Follow up with Sarah to [do X]."
  • Scenario B: The 8% Speaker Label Error (Crosstalk / Low Confidence).
  • System Behavior: If an action item is derived from an utterance where someone says "I will do X", the pipeline checks the diarization confidence score for that timestamp block. If confidence is `< 0.85` or overlaps with another speaker within 1.5 seconds, the system sets `owner: Unassigned`, sets `suggestedowner: [SpeakerID]`, and flags the item as `needs_review: true`.
  • Scenario C: Multi-person Conference Rooms.
  • System Behavior: Audio coming from a single hardware node containing multiple voices cannot reliably attribute "I'll take that" to an individual. If the speaker label is a Room Resource or has multi-voice flags, the LLM may only assign an action item if a name is spoken explicitly in the third person (e.g., "Thanks Dave, so Dave will pull the Q3 numbers" AND "Dave" is matched to a Huddle user via calendar invite + confirmation). Otherwise, set `owner: Unassigned` (`needs_review: true`).
  • Scenario D: The 15% Uninvited Guest.
  • System Behavior: If "Alex" joined via a forwarded link without logging in, he is in `ActualAttendees` as `{name: "Alex (Guest)", email: null}`. The system can assign action items to "Alex (Guest)" in the UI, but flags the item as `missingemail: true`.

4.3 The Email Dispatch Engine (Addressing the Sales Request)

To satisfy Sales without sending hallucinated assignments, we will implement a Two-Track Dispatch Engine:

  • Track 1: High-Confidence Auto-Send (30-Minute Delay)
  • Condition: All extracted action items have `owner != Unassigned`, `needsreview == false`, and `missingemail == false`.
  • Behavior: The host gets an immediate Slack/Email ping: "Your meeting summary is ready. Action items will be emailed to owners in 30 minutes unless you edit them." If the host makes no changes within 30 minutes (or clicks "Send Now"), emails are dispatched to action item owners.
  • Track 2: Low-Confidence Hold
  • Condition: One or more action items are flagged `needsreview: true` or `missingemail: true`.
  • Behavior: Auto-send is paused. The host is notified: "Your meeting summary has 2 action items that need your confirmation before we email the team." Emails only trigger once the host resolves the flags and clicks "Approve & Send".

---

5. User Experience & Design Requirements

5.1 Pre-Meeting Lobby (Small tweak for Guest ID)

  • Unauthenticated Guests: Update the pre-join screen. Require both Full Name and Work Email (Optional) before joining. Explaining "Used to send you meeting notes" will convert a high percentage of the 15% uninvited guests into identifiable users.

5.2 Post-Meeting Summary View

The Meeting Host (and participants) will access the summary inside the Huddle web/desktop app under the "Past Meetings" tab.

* Layout: Split screen. Left side: Video player + searchable transcript. Right side: AI Summary widget. * Summary Widget Sections: 1. Status Banner: Shows dispatch state (e.g., "Auto-sending to 4 owners in 24 mins [Send Now] [Pause]" OR "Approval Required before emailing"). 2. Decisions Made: Bulleted list. Hovering over a decision highlights the corresponding timestamp in the transcript and lets the user click to play that 15-second clip. (Crucial for building user trust). 3. Action Items List: Each row contains: * Checkbox (for completion tracking). * Task description (inline editable). * Assignee Dropdown Pill. * Source timestamp link.

5.3 The Assignee Dropdown & "Needs Review" UI

  • Restricted Roster: When a user clicks the Assignee Pill to change or add an owner, the dropdown menu only lists people from the `Actual_Attendees` array.
  • Design detail: Below a divider line at the bottom of the dropdown, show calendar invitees who didn't show up in greyed-out, unclickable text with a tooltip: "Did not attend meeting." (This prevents users from thinking the dropdown is broken while enforcing our constraint—unless someone was hiding in a conference room, see below).
  • Conference Room Override: If a meeting included a Room system, include an option in the dropdown: `+ Add person who was in [Conf Room Name]`. Clicking this allows the host to pick from the org directory, explicitly confirming that the person was physically present in the room.
  • Low-Confidence Visual State: Action items flagged with `needs_review: true` (due to the 8% diarization issue) should have an amber highlight around the Assignee Pill reading: `Suggested: [Name] ?`. Clicking it lets the host confirm with one click.

5.4 The Action Item Email

  • Subject: `Action Items from [Meeting Title] ([Date])`
  • Body:
  • Personalized top section: "Assigned to You (2):" followed by their specific tasks.
  • Context section below: "All Decisions & Team Action Items" so they have full context.
  • Footer CTA: `[View Full Recording & Transcript in Huddle]`.

---

6. Out of Scope for v1

  • 3rd-Party Task Manager Integrations: Pushing tasks to Jira, Asana, Linear, or Salesforce (planned for v1.1 after we validate extraction accuracy).
  • Cross-Meeting Action Item Tracking: A global "My Tasks" dashboard across all meetings. v1 is strictly scoped to the single-meeting view and email dispatch.
  • Voiceprint Enrollment: Having users record their voice to fix the conference room diarization problem. We will rely on UI fallbacks for v1.

---

7. Open Questions for Kickoff

  1. Engineering: What is the p95 latency for transcript generation + LLM summary extraction on a 60-minute call? Sales expects "as soon as the meeting ends"—if our pipeline takes 8 minutes, do we need to adjust the 30-minute auto-send timer?
  2. Design: If a meeting has zero decisions and zero action items (e.g., a casual 1:1 catch-up), what should the empty state look like, and should we suppress the post-meeting notification entirely?
  3. Engineering: Can our current diarization vendor expose word-level confidence scores via their webhook, or do we need to calculate crosstalk purely by checking overlapping speaker timestamps?

Claims the judge couldn’t find in the brief

  • Users still spend 10–15 minutes after every call reviewing transcripts or typing up raw notes
  • Huddle currently records and transcribes video meetings for 3,000 B2B customers

Grades and run details

Decision model 77 · LLM judge 58
Decision model checks
  • partialUses the supplied evidence correctly16%
  • passAddresses the actual decision48%
  • passRespects explicit constraints34%
  • passIdentifies material uncertainty59%
  • partialAvoids unsupported claims20%
  • passProduces the required deliverable83%
  • passSpecifies AI failure behaviour100%
  • passSuccess settles trade-offs69%
  • partialProposes tests that could fail59%
  • partialSays who it's for, and who it isn't61%
  • partialPlans how people will find it34%
Run
Run
#1
API response time
27 s
Submitted
13 Oct 2026

Needs more than a quick edit7 of 11 checks passed

The PRD assumes a participant session log that is not confirmed by context; a PM would need to verify this assumption and adjust the roster design before building.

Output

PRD: AI-Generated Meeting Summaries with Decisions and Action Items

Audience: Engineering and Design leads Status: Ready for build planning Owner: Product

1. Summary

Huddle records and transcribes meetings with speaker labels, and the attendee list comes from the calendar invite. Today, people write their own notes after calls, and those notes are often late, incomplete, or missing. This feature generates a summary after each meeting that lists the decisions made and assigns action items to attendees. Action items are emailed to their owners, with Sales' requirement for immediate delivery balanced against the accuracy limits described in Section 4.

2. Goals and Non-Goals

Goals - Produce a summary within 5 minutes of meeting end for 95% of meetings. - Every action item names an owner who was present in the meeting. - Every decision and action item links to the transcript moment that supports it. - Give Sales automatic, immediate delivery for high-confidence action items.

Non-Goals (v1) - Calendar write-back or task creation in external tools (Jira, Asana). Design for it, but do not build it. - Summaries for meetings without a transcript. - Sentiment, coaching, or participation analytics.

3. Definitions

  • Attendee: A person present in the meeting, determined by the participant session log (the platform's record of who joined), not only by the calendar invite. The calendar invite is a hint, not proof of attendance.
  • Identified participant: A participant tied to a Huddle account or a verified email address.
  • Unidentified participant: A guest who joined by name only, with no verified identity.
  • Action item: A task with an owner, a description, and optionally a due date, derived from transcript content.
  • Decision: A statement that the group agreed on a course of action, with supporting transcript evidence.

4. Known Limits and Design Responses

LimitImpactDesign response
Speaker labels wrong ~8% of the time, mostly during overlapping speechOwner may be misattributedAttach an attribution confidence score to each action item. Low-confidence items are held for review (FR-6).
~15% of meetings include uninvited joinersRoster from calendar is incompleteBuild the roster from the participant session log, not the invite (FR-1).
Multiple people share one room deviceSpeaker labels can't separate themFlag room-device speech as "multi-person source." Items attributed to a shared source require owner confirmation (FR-7).

5. Functional Requirements

FR-1: Roster resolution Build the attendee roster from the participant session log at meeting end. Merge in calendar invitees for context, but do not treat invitees who never joined as attendees. Each roster entry is tagged as identified or unidentified.

FR-2: Summary generation Produce a summary containing: - A 3–5 sentence overview. - A decisions list. Each decision has a one-sentence statement, a timestamp, and a transcript link. Decisions are not assigned to individuals unless the transcript clearly states who made them. - An action items list (FR-3).

FR-3: Action item extraction Each action item includes: description, owner (a roster member), due date if stated, timestamp, transcript link, and attribution confidence (high, medium, low).

FR-4: Hard constraint: no non-attendee owners (must-pass) Before any summary is stored or sent, a deterministic validator checks that every action item owner matches a roster entry from FR-1. If an owner does not match, the item is removed and logged as a pipeline error. This check does not depend on the model or on confidence scores. The model is never the final authority on membership.

FR-5: Unidentified owners If the model attributes an action to an unidentified participant, the item is kept, but the owner is shown as "Unidentified participant (Name as joined)." These items are never emailed. They appear in the organizer's review view only.

FR-6: Confidence gating for delivery Each action item is sent automatically only if all of the following are true: - Owner is an identified roster member. - Attribution confidence is high. - The supporting transcript segment has no overlapping speech flag. - The item's source is not a multi-person room device.

Items that fail any check go to the organizer's review queue. The organizer can confirm, reassign to another roster member, or dismiss. Confirmed items are then sent.

FR-7: Multi-person sources When an action item comes from a multi-person source, the owner field is set to "Confirm owner" and the item is held for review. The review UI lets the organizer or the item's likely owner claim it from the roster.

FR-8: Delivery - Auto-sent items go to the owner's email within 5 minutes of meeting end. - Emails contain: the meeting name, the action item, the due date if any, a link to the summary, and a link to the transcript moment. - Each meeting produces one email per owner, grouping that owner's items. - Emails go only to identified roster members. Admins can disable delivery to external email domains.

FR-9: Review queue Organizers see a summary page with held items, can edit or confirm them, and can see which items were auto-sent. Edits made after sending generate a correction email to the owner.

FR-10: Controls and privacy - Admins can turn summaries on or off per workspace and per meeting type. - Any participant can opt out of their own speech being summarized. Their items are excluded from the summary and their name is removed from the roster. - Summaries and transcripts follow the same retention and access rules as the source recording.

6. Sales Requirement: Immediate Auto-Send

Sales wants action items emailed as soon as the meeting ends. This PRD supports that for high-confidence items (FR-6, FR-8). It does not send low-confidence items immediately, because a wrong owner gets a task they didn't agree to, and an email that is later corrected erodes trust. Sales can see the auto-send rate in the admin dashboard. If the held rate is too high for their workflows, the threshold can be tuned with data from the pilot. It should not be lowered without evaluation.

7. Acceptance Criteria

  • AC-1: Across a 300-meeting eval set, zero action items name an owner absent from the participant log. This is a hard gate for launch.
  • AC-2: Every decision and action item has a working transcript link that lands within 10 seconds of the cited statement.
  • AC-3: For a labeled eval set, owner precision on auto-sent items is at least 97%.
  • AC-4: Summaries are generated within 5 minutes of meeting end for 95% of meetings with transcripts under 90 minutes.
  • AC-5: An uninvited identified joiner appears in the roster and can receive items. An unidentified joiner never receives email.
  • AC-6: Opt-out removes the participant's items and name from the output within one processing cycle.

8. Metrics

  • Adoption: Percentage of eligible meetings with a summary delivered.
  • Quality: Action items edited or reassigned by the organizer, by confidence band.
  • Trust: Post-send corrections per 100 auto-sent items. Target: under 2.
  • Usage: Percentage of owners who open the linked transcript from an email.

9. Open Questions

  1. Should the review queue notify the organizer by email, Slack, or in-app only?
  2. When an owner is an external email address, what is the policy under the admin domain setting?
  3. How will we handle meetings where the transcript contains no clear owner for a decision or action?
  4. Do we need a per-workspace setting to require organizer approval for all items?
  5. Should multi-person room devices be fixed at the hardware or SDK level, which would change FR-7?

10. Phasing

  • Phase 1 (pilot, 20 workspaces): FR-1 through FR-5, FR-8 through FR-10, with all items held for organizer review. This validates the constraint and attribution quality.
  • Phase 2: Enable confidence gating (FR-6) and auto-send for Sales and other pilot workspaces, based on Phase 1 precision data.
  • Phase 3: Multi-person source handling at scale (FR-7), and general availability.

Claims the judge couldn’t find in the brief

  • The platform has a participant session log (the platform's record of who joined).

Grades and run details

Decision model 82 · LLM judge 58
Decision model checks
  • passUses the supplied evidence correctly43%
  • passAddresses the actual decision61%
  • passRespects explicit constraints52%
  • passIdentifies material uncertainty95%
  • partialAvoids unsupported claims27%
  • passProduces the required deliverable85%
  • passSpecifies AI failure behaviour100%
  • passSuccess settles trade-offs94%
  • partialProposes tests that could fail74%
  • partialSays who it's for, and who it isn't66%
  • partialPlans how people will find it95%
Run
Run
#1
API response time
20 s
Submitted
8 Oct 2026

Usable with a quick edit9 of 11 checks passed

The PRD is thorough, evidence is correct, and the missing measurement windows and team readiness can be added with a quick edit without redoing any reasoning.

Output

PRD: AI Meeting Summaries and Action Items

Product: Huddle Audience: Engineering and Design leads Status: Proposed MVP Launch decision: Ship reviewed summaries first; defer immediate, unreviewed owner emails.

1. Problem and opportunity

Huddle serves 3,000 companies. Meetings are recorded and transcribed with speaker labels, but attendees currently write their own notes afterward. This creates duplicated effort, inconsistent records of decisions, and missed follow-through.

We will generate a concise meeting summary containing decisions and action items, with evidence linking each item to the recording.

Two known limitations shape the design:

  • Speaker labels are wrong approximately 8% of the time, especially during overlapping speech and shared-room participation.
  • Approximately 15% of meetings include someone absent from the calendar invite.

Therefore, neither speaker labels nor calendar invitations are sufficient evidence for assigning work.

Non-negotiable requirement: An action must never be assigned to someone who did not attend the meeting. When attendance or identity cannot be established, leave the action unassigned.

2. Goals and non-goals

Goals

  • Reduce time spent producing post-meeting notes.
  • Capture decisions accurately, distinguishing decisions from proposals.
  • Extract actionable commitments with owners and deadlines when supported.
  • Include verified, uninvited attendees without treating all invitees as attendees.
  • Make every decision and action inspectable and correctable.
  • Deliver owner notifications only after safe assignment and publication.

Non-goals for MVP

  • Live summaries or action assignment during meetings.
  • Assigning work to absent colleagues, even if requested during the meeting.
  • Inferring deadlines or assigning owners based on job title.
  • External task-system integrations.
  • Automatically publishing unreviewed summaries.
  • Resolving identity using voice biometrics.

3. Users and primary workflow

Reviewer: Meeting organizer, or an explicitly designated attendee. Reviews and publishes the summary.

Attendee: Reads the summary, follows evidence links, and reports corrections.

Action owner: Receives published actions assigned to them.

Workflow:

  1. Meeting ends; Huddle completes transcript processing.
  2. Huddle reconciles actual attendance and generates a private draft.
  3. Reviewer checks decisions, unresolved ownership, and attendance exceptions.
  4. Reviewer edits and publishes.
  5. Huddle makes the summary available to authorized viewers and, where enabled, emails owners their approved actions.

If no eligible reviewer is available, the draft remains unpublished. A non-attending organizer may designate an attending reviewer but cannot resolve attendance exceptions themselves.

4. Attendance and identity requirements

Engineering must establish an attendance ledger separate from the calendar invite.

Each record contains:

  • Meeting ID and stable person ID, where resolved.
  • Join/leave intervals and participant-session IDs.
  • Identity source: authenticated join, verified guest identity, or shared-room roster.
  • Verification state and evidence.
  • Reviewer confirmation details, where required.

Calendar invitees are suggestions for identity matching, not proof of attendance. A directory search result is also not attendance evidence.

Assignment eligibility

An owner must map to a unique person with verified attendance in that meeting:

  • Authenticated individual join: Eligible when attendance telemetry records the person’s session.
  • Guest join: Eligible only after identity is resolved and verified; a display name alone is insufficient.
  • Shared meeting room: The room endpoint proves room participation, not individual presence. A participating reviewer must explicitly confirm each person physically present before they become eligible.
  • Uninvited attendee: Eligible through the same verification rules; invitation status is irrelevant.
  • Invited but absent person: Ineligible.

If telemetry is unavailable, identity is ambiguous, or attendance is disputed, block assignment. Keep the action unassigned with an explanation.

The owner picker must show only eligible attendees. Every write path—including generation, manual edits, APIs, and publication—must enforce eligibility server-side. Model confidence must never override this rule.

These controls cannot independently observe who is physically in a room. If trustworthy roster confirmation is unavailable, shared-room individuals remain ineligible rather than weakening the constraint.

5. Summary and extraction requirements

The draft has four sections:

  1. Overview: Two to four sentences describing the meeting’s purpose and outcome.
  2. Decisions: Clear statements of agreed outcomes.
  3. Action items: Task, owner or “Unassigned,” deadline if explicit, and status.
  4. Needs review: Uncertain decisions, ownership, identity, or attendance.

Each decision and action must include transcript-span references and recording timestamps. Preserve the original supporting text even when the reviewer edits the final wording.

Decisions

Capture explicit agreement or a clearly accepted conclusion. Do not convert brainstorming, recommendations, or unresolved debate into decisions. If discussion reverses an earlier decision, represent the final outcome and retain supporting evidence.

Actions

Extract a concrete deliverable or next step. Record a deadline only when explicitly stated; normalize relative dates using the meeting’s date and timezone.

Examples:

  • “I’ll send the revised deck tomorrow.” → Task with a candidate owner, subject to attribution review.
  • “Someone should update the deck.” → Unassigned action.
  • “Ask Priya to update it,” when Priya was absent → Unassigned action; note that an absent person was mentioned.
  • “We could revisit this next quarter.” → Not an action without a clear commitment.

Speaker labels provide evidence, not authority. Overlap, shared-room speech, ambiguous pronouns, and conflicting commitments must trigger ownership review. Candidate owners are draft suggestions, not assignments, until approved.

6. Review experience

The summary page opens in Draft state with a clear “Review before sharing” banner.

Design must provide:

  • Editable decision and action cards.
  • Evidence links opening the relevant transcript and recording segment.
  • Visible distinction between verified attendance and uncertain attribution.
  • “Unassigned” as a first-class owner option.
  • A dedicated queue for identity and attendance exceptions.
  • A publish preview showing recipients and notification settings.

For each assigned action, the reviewer must approve the owner. Bulk approval is permitted for straightforward items, but flagged attribution requires item-level review.

Attendance verification and ownership approval are separate controls: confirming that someone attended does not prove they accepted a task.

Publication is blocked if any assigned owner is ineligible. Unassigned actions and unresolved decision candidates do not block publication; uncertain decisions must be omitted from the final Decisions section or explicitly labeled unresolved.

7. Email policy and Sales request

Sales requests automatic emails immediately after each meeting. MVP will not send unreviewed action assignments. Given the known speaker errors, immediate delivery would spread incorrect ownership before correction.

Instead:

  • Notify the reviewer when the draft is ready.
  • Default owner emails to off; enable through workspace settings.
  • When enabled, automatically email approved owners after publication.
  • Send each owner only their actions, meeting title, deadlines, and an authorized summary link.
  • Never email a calendar invitee solely because they were invited.
  • Do not send to guests without a verified address and permitted access.

Email dispatch must recheck eligibility and publication version. Use idempotency keys to prevent duplicate sends. Published ownership changes generate a correction for the previous recipient and an updated notification for the new eligible owner.

Future immediate automation requires a separate launch decision and evidence that it preserves the attendance constraint.

8. Technical implementation

Use an asynchronous pipeline:

`Meeting ended → Transcript ready → Attendance reconciled → Draft generated → Reviewed → Published → Notifications`

Proposed records:

  • `Summary`: meeting ID, transcript version, state, generated content, revision.
  • `Decision`: text, evidence spans, review state.
  • `Action`: task, proposed owner, approved owner, deadline, evidence, review state.
  • `AttendanceRecord`: identity, evidence, verification state.
  • `AuditEvent`: actor, change, timestamp, revision.

Generation returns schema-validated structured data. Treat transcript content as untrusted input: instructions spoken during a meeting must not alter system rules, access controls, or recipient selection.

Owner writes and publication run through a centralized eligibility validator. Notification jobs check the current revision before sending. Attendance corrections invalidate affected assignments and cancel queued notifications. Maintain an audit trail rather than silently rewriting history.

9. Reliability, privacy, and access

Target draft availability: within five minutes of transcript completion for 95% of supported meetings.

Generation failures must leave recordings and transcripts usable, show retry status, and send no owner emails.

Drafts are visible only to reviewers. Published summaries inherit meeting-content access rules; attendance alone does not grant access. Recording evidence links must enforce existing permissions. Apply workspace retention and deletion policies to summaries, attendance evidence, and generated artifacts.

10. Success metrics and acceptance

Proposed launch gates

  • Zero assignments to ineligible attendees across automated invariant tests.
  • Zero emails to ineligible owners in the pilot; any confirmed incident pauses rollout.
  • At least 90% of published decisions require no substantive reviewer correction.
  • Median review time below three minutes for meetings under one hour.
  • At least 30% less self-reported note-taking time in the pilot.

Measure owner-correction rate separately from attendance violations: a wrong attending owner is still a quality failure.

Required acceptance cases

Test absent invitees, verified uninvited guests, shared rooms, overlapping speakers, duplicate names, failed identity resolution, missing telemetry, absent people named in requests, transcript reprocessing, attendance revoked before dispatch, and repeated notification retries.

In every uncertain-attendance case, expected behavior is an unassigned action and no owner email.

11. Rollout and dependencies

Start with internal meetings, then an opt-in pilot across varied company sizes and shared-room usage. Expand only after launch gates pass.

Before implementation, confirm availability of participant-session telemetry, guest identity verification, room-roster confirmation, reviewer permissions, and verified email addresses. If these dependencies are missing, ship summaries with unassigned actions for affected participants rather than substituting calendar membership as attendance proof.

Grades and run details

Decision model 86 · LLM judge 83
Decision model checks
  • passUses the supplied evidence correctly70%
  • passAddresses the actual decision83%
  • passRespects explicit constraints62%
  • passIdentifies material uncertainty52%
  • passAvoids unsupported claims62%
  • passProduces the required deliverable88%
  • passSpecifies AI failure behaviour100%
  • passSuccess settles trade-offs74%
  • partialProposes tests that could fail82%
  • partialSays who it's for, and who it isn't77%
  • partialPlans how people will find it83%
Run
Run
#1
API response time
47 s
Submitted
29 Sept 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 correctlyWrongMixedRight
Gemini 4 Argon · API

The PRD invents a 10-15 minute note-taking time not in the brief and calls the customer base 'B2B' without evidence.

Haiku 5.5 · API

The output claims a participant session log as a current platform record, but the context only mentions the calendar invite as the attendee source.

GPT-6.1 Sol · API

All facts about the current situation are taken directly from the brief, with no invented numbers or systems.

Avoids unsupported claimsWrongWrongRight
Gemini 4 Argon · API

The 10-15 minute claim is presented as established fact without any support from the supplied context.

Haiku 5.5 · API

The existence of a participant session log is presented as an established fact, but the supplied context provides no support for it.

GPT-6.1 Sol · API

Forecasts like 'immediate delivery would spread incorrect ownership' are clearly presented as reasoning, not as established facts.

Proposes tests that could failWrongRightWrong
Gemini 4 Argon · API

No proposed test, pilot, or gate with numeric threshold, measurement window, and action to take.

Haiku 5.5 · API

Acceptance criteria include numeric thresholds (zero non-attendee assignments, 97% precision) and phased rollouts with data-driven gating, so tests can fail and trigger action.

GPT-6.1 Sol · API

Launch gates have numeric thresholds and actions, but lack explicit measurement windows (e.g., pilot duration, number of meetings) needed to read out results.

Says who it's for, and who it isn'tWrongWrongRight
Gemini 4 Argon · API

It does not explicitly name the target users (e.g., meeting hosts) and who is excluded, and no scope decision visibly follows from an exclusion.

Haiku 5.5 · API

Does not explicitly name the specific target users and a deliberately excluded group, nor does a scope decision visibly follow from a user exclusion.

GPT-6.1 Sol · API

Names target users (reviewer, attendee, action owner) and explicitly excludes absent colleagues and unverified guests, with scope decisions following from those exclusions.

All got wrong 1

Plans how people will find itWrongWrongWrong
Gemini 4 Argon · API

There is no rollout plan, no mention of how users discover the feature, and no indication of which teams (support, sales) need to be ready.

Haiku 5.5 · API

Lacks explicit description of how users will discover the feature and which teams (e.g., support, sales) need to be ready beyond a mention of sales dashboards.

GPT-6.1 Sol · API

Gives a staged rollout and discovery method (reviewer notification), but does not name the teams that need to be ready (e.g., support, sales) beyond technical dependencies.

All got right 6

Addresses the actual decisionRightRightRight
Gemini 4 Argon · API

It commits to a human-in-the-loop flow with fallback auto-send after review, explicitly addressing the sales request and constraints.

Haiku 5.5 · API

Commits to a design with confidence-gated auto-send and review queue, framed for engineering and design leads, with conditions to change based on pilot data.

GPT-6.1 Sol · API

Commits to a clear launch decision (ship reviewed summaries, defer unreviewed emails) and states what would change it (evidence that immediate automation preserves the attendance constraint).

Respects explicit constraintsRightRightRight
Gemini 4 Argon · API

The design builds an Actual_Attendees roster, restricts assignments to that list, and prevents auto-email to non-attendees.

Haiku 5.5 · API

The hard constraint against assigning non-attendees is enforced by a deterministic validator (FR-4), and all other explicit constraints are respected.

GPT-6.1 Sol · API

Respects the non-assignment constraint through an attendance ledger, eligibility rules, and server-side enforcement; also meets the PRD form, audience, and word count.

Identifies material uncertaintyRightRightRight
Gemini 4 Argon · API

Open questions name latency and diarization confidence availability as unknowns that could change the auto-send timer.

Haiku 5.5 · API

Open questions and the instruction to tune the confidence threshold based on pilot data address key unknowns and how they would be resolved.

GPT-6.1 Sol · API

Names specific unknowns (speaker errors, shared-room identity, missing telemetry) and says how they would be resolved (e.g., ship with unassigned actions if dependencies missing, separate launch decision for immediate automation).

Produces the required deliverableRightRightRight
Gemini 4 Argon · API

It is a PRD for engineering and design leads, within the target length, and provides enough detail to start building.

Haiku 5.5 · API

The output is a complete PRD of appropriate length and structure, directly actionable by engineering and design leads.

GPT-6.1 Sol · API

Delivers a complete PRD for engineering and design leads, within the requested length, with enough detail to start work.

Specifies AI failure behaviourRightRightRight
Gemini 4 Argon · API

Low-confidence items are flagged for review, assigned to 'Unassigned', and auto-email is paused until the host resolves them.

Haiku 5.5 · API

The PRD specifies review queues, confidence gating, correction emails, and handling of multi-person sources for when the AI is uncertain or wrong.

GPT-6.1 Sol · API

Specifies that uncertain attendance leads to unassigned actions, no owner emails, and generation failures leave recordings usable with retry status.

Success settles trade-offsRightRightRight
Gemini 4 Argon · API

Success metrics have targets (e.g., 0% false attribution, 60% adoption) and the decision explicitly prioritizes accuracy over speed by using human-in-the-loop.

Haiku 5.5 · API

It sets a precision target of 97% on auto-sent items and includes an explicit trade-off rule that low-confidence items are held to protect trust.

GPT-6.1 Sol · API

Defines success metrics with targets (e.g., 90% decision accuracy, <3 min review time) and gives an explicit trade-off rule: never assign if uncertain, even if it means unassigned actions.

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 67% of 81 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT86.984.62None
2Sonnet 5.5withAPI89.279.82None
3GPT-6.1 SolwithAPI84.860.92None
4Opus 5.5withClaude82.860.62None
5GPT-6 LunawithAPI80.759.92None
6Haiku 5.5withAPI76.363.82None
7Gemini 4 ArgonwithAPI69.948.421 capped
8Gemini 3.5 Flash-LitewithGemini52.532.12None
9Gemini 3.8 FlashwithAPI50.428.22None

What you get for the cost

Sonnet 5.5 · API scores within a point of GPT-6 Astra · ChatGPT at a third of the cost.

Task score against relative cost for each setup. The table below has the same figures.304050607080901001×3×10×30×100×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Astra · ChatGPT: task score 84, 106× the cheapest (1 of 2 outputs estimated)Sonnet 5.5 · API: task score 83, 35× the cheapestGPT-6.1 Sol · API: task score 72, 14× the cheapestOpus 5.5 · Claude: task score 68, 85× the cheapest (1 of 2 outputs estimated)GPT-6 Luna · API: task score 67, the cheapestHaiku 5.5 · API: task score 67, the cheapestGemini 4 Argon · API: task score 54, 16× the cheapest, 1 critical failureGemini 3.5 Flash-Lite · Gemini: task score 40, 4.4× the cheapest (1 of 2 outputs estimated)Gemini 3.8 Flash · API: task score 39, 9.9× the cheapestGPT-6 Astra · ChatGPTSonnet 5.5 · APIGPT-6.1 Sol · APIOpus 5.5 · ClaudeGPT-6 Luna · APIHaiku 5.5 · APIGemini 4 Argon · APIGemini 3.5 Flash-Lite · GeminiGemini 3.8 Flash · API
Best valueNobody beats these on both score and costCost partly estimated (app runs with no usage meter)At least one critical failure
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API671.0×
Haiku 5.5 · API671.2×
Gemini 3.5 Flash-Lite · Gemini404.4× (partly estimated)
Gemini 3.8 Flash · API399.9×
GPT-6.1 Sol · API7214×
Gemini 4 Argon · API5416×
Sonnet 5.5 · API8335×
Opus 5.5 · Claude6885× (partly estimated)
GPT-6 Astra · ChatGPT84106× (partly estimated)

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

Writing the requirements document a team will build and test against.

Why it matters

A PRD is where ambiguity becomes either a decision or a bug. For AI products it must also say what happens when the model is uncertain or wrong. Most generated PRDs skip that part.

What good looks like

  • States the user problem and the decision the PRD enables
  • Specifies behaviour under uncertainty, failure and refusal
  • Names eval criteria and a launch bar
  • Defines success precisely enough to settle trade-offs
  • Says what is out of scope

Deliberately not measured

  • Formatting or template conformance
  • Length
  • Visual polish of diagrams
Capability tested

Making product behaviour, uncertainty and eval requirements executable

The failure we’re looking for

A generic feature spec that ignores AI failure behaviour

Grading

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

Variants

AI product PRD (core) · Conventional product PRD