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 16 graded outputs by 8 models. 25% 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 deliverable94% 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. Proposes tests that could fail36% pass
    Launch gates have numeric thresholds and actions, but lack explicit measurement windows (e.g., pilot duration, number of meetings) needed to read out results.
    GPT-6.1 Sol · API · Meeting summaries with action items
  2. Avoids unsupported claims42% pass
    Presents the join log and identity tiers as established facts without labelling them as assumptions, when the context provides no support.
    Opus 5.5 · Claude · Meeting summaries with action items
  3. Uses the supplied evidence correctly44% pass
    The output invents a join log and identity tiers (Verified, Guest, Room) that are not in the supplied context, making unsupported claims about the current system.
    Opus 5.5 · Claude · 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 · v1.19

The outputs

8 setups answered this brief.

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 77 · LLM judge 58
Decision model checks
  • failUses the supplied evidence correctly11%
  • passAddresses the actual decision86%
  • passRespects explicit constraints51%
  • passIdentifies material uncertainty82%
  • partialAvoids unsupported claims38%
  • passProduces the required deliverable79%
  • passSpecifies AI failure behaviour100%
  • passSuccess settles trade-offs76%
  • partialProposes tests that could fail74%
  • passSays who it's for, and who it isn't37%
  • partialPlans how people will find it98%
Run
Run
#1
API response time
20 s
Submitted
8 Oct 2026

Usable with a quick edit2 of 11 checks passed

The PRD is detailed and actionable for engineering and design leads, with only minor unsupported claims that can be quickly edited out without redoing any reasoning.

Output

Product Requirements Document (PRD)

Feature: Automated Meeting Summaries, Decisions, and Action Item Attribution Product: Huddle Video Conferencing Target Audience: Engineering Leads, Design Leads, Product Ops Status: Ready for Review Target Release: Q3

---

1. Executive Summary & Objective

Today, Huddle’s 3,000 corporate customers rely on manual note-taking. This results in lost context, untracked deliverables, and administrative drag.

This project introduces an AI-powered pipeline that automatically generates: 1. A concise meeting summary. 2. A structured list of key decisions. 3. Assigned action items mapped strictly to verified attendees.

Key Objectives

  • Reduce post-meeting admin time by 80%.
  • Achieve $\ge 90\%$ user acceptance rate on action item assignments (measured by lack of manual re-assignment or deletion).
  • Hard Rule Compliance: 0% assignment of action items to non-attendees.

---

2. Participant Verification & System Constraints

Our models must handle two structural data flaws: 1. Diarization/Labeling Error Rate: ~8% of transcript lines have inaccurate speaker tags (common during crosstalk or multi-person hardware rooms). 2. Ad-Hoc Joiners: ~15% of meetings feature participants who were not on the original calendar invite.

2.1 The "Eligible Assignee" Rule Engine (Non-Negotiable)

``` [Calendar Invite] [Client Telemetry Logs] \ / \ / v v +----------------------------------------+ | Participant Reconciliation Engine (PRE)| +----------------------------------------+ | v [Eligible Assignee Roster (EAR)] - Authenticated Users (User ID + Email) - Verified Guests (Session ID + Display Name) | v +----------------------------------------+ | LLM Extraction & Assignment Pipeline | +----------------------------------------+ | +--> Action assigned to User in EAR? --> ACCEPT | +--> Action assigned to External Party? | v Route to In-Room Proxy (or flag as Unassigned Dependency) ```

Constraint Definition: An action item can only be assigned to an entity present in the Eligible Assignee Roster (EAR) for that specific meeting session.

#### Step 1: Ingest Telemetry at Call Termination (`call.ended` event) Do not rely on the calendar invite as the source of truth for presence. Telemetry establishes physical attendance: * Calendar Attendees ($P{cal}$): Used only as an identity-matching fallback. * Connected Clients ($P{conn}$): Every participant with connection duration $> 60\text{ seconds}$ in the meeting telemetry logs. * Eligible Assignee Roster ($EAR$): Defined strictly as $EAR = P_{conn}$.

#### Step 2: Resolving the 15% Uninvited Attendees * Authenticated Joiners: If an uninvited joiner is logged into a Huddle account, resolve their `userid`, display name, and email directly into the $EAR$. * Guest Joiners (Unauthenticated): If a user joins via guest link without an account: * Capture their client display name and the session ID. * Tag them in the $EAR$ as `Guest: [Display Name]` with `userid = null`. * They are eligible for assignment in the UI, but will require an email address input if notifications are to be sent.

#### Step 3: Mitigating the 8% Diarization Noise Diarization errors mean the speaker label alone cannot be trusted for commitments (e.g., if Bob says "I'll take that," but the audio engine tags Alice). * The LLM prompt must cross-reference explicit verbal attribution ("Jane, can you take that?" $\rightarrow$ "Sure") against semantic context rather than relying exclusively on transcript speaker IDs. * When semantic confidence falls below threshold ($\tau < 0.75$), flag the action item as Unassigned rather than guessing.

#### Step 4: External Dependencies (Out-of-Room Third Parties) If a meeting participant says: "We need legal to review this, I'll have Sarah from Legal look at it," Sarah was not present and cannot be assigned the task. * Enforcement: The system assigns the task to the speaker (the in-room proxy) as: Assignee: Current Speaker (`[Speaker Name]`) Task: "Coordinate with Sarah (Legal) to review [X]." * If the speaker cannot be deduced with high confidence, set `assignee = UNASSIGNED` with a note: `External dependency: Sarah (Legal)`.

---

3. Product & Feature Specifications

3.1 LLM Processing Pipeline

Within 60 seconds of call termination, the processing service executes the following workflow:

``` [Call Ended] | v [Fetch Clean Transcript + Telemetry EAR] | v [LLM Context Construction] ├── System Prompt & Guardrails ├── Strict EAR List (Names + Emails + IDs) └── Diarized Transcript | v [Structured Output JSON Generation] ├── Executive Summary (Paragraph, <= 150 words) ├── Key Decisions (Bullet points) └── Action Items (Array: Task, Assignee ID, Due Date, Evidence Quote) | v [Deterministic Post-Processing Validator] ├── Cross-reference every Assignee ID against EAR └── Overwrite invalid assignments to "UNASSIGNED" | v [Summary Artifact Created & Persisted] ```

3.2 Action Item Post-Processing Validation Logic

Before writing to the database, run a deterministic verification function:

```python def validateactionitems(extractedactions, eligibleassigneeroster): validatedactions = [] for action in extractedactions: assigneeid = action.get("assigneeid") # Validation: Assignee must explicitly exist in the EAR if assigneeid and assigneeid in eligibleassigneeroster: action["status"] = "assigned" action["assignee"] = eligibleassigneeroster[assigneeid] else: # Fallback for hallucinated or external assignments action["status"] = "unassigned" action["assignee"] = None action["flag"] = "ASSIGNEENOTINROOM" validatedactions.append(action) return validated_actions ```

---

4. Sales Request Resolution: Auto-Email Notifications

The Conflict

Sales requested that action items be emailed automatically to assignees immediately when the meeting ends. However, because speaker labels fail ~8% of the time, immediately sending unreviewed emails will blast misattributed commitments to clients and internal executives, eroding trust.

The Solution: "Grace Period Auto-Dispatch"

We satisfy Sales' need for immediate execution while protecting data integrity through a time-delayed trigger and host bypass mechanism.

``` [Meeting Ends] │ ├─► AI Generates Artifacts (Target: <60 seconds) │ ├─► Start 5-Minute Grace Period │ │ │ ├─► Host edits/approves? ──► Send instantly with updates │ │ │ └─► Timer expires? ────────► Auto-dispatch without host intervention │ └─► (Workspace Setting Override: "Instant Send" with 0-min delay available) ```

1. Host Review Window (Default): * A 5-minute countdown starts the moment the summary finishes processing. * The meeting host gets a browser/desktop push notification: "Your Huddle summary is ready. Auto-sending in 5:00 minutes. Click to review." * If the host makes edits or clicks "Send Now", the countdown ends and emails dispatch immediately. * If the host takes no action, the emails dispatch automatically at the 5-minute mark. 2. Sales/Workspace Configuration Flag: * Enterprise Admins can toggle the grace window per team (e.g., Sales team can set Grace Period to `0 minutes` for true instant dispatch; default for other workspaces is `5 minutes`). 3. Safety Fallback: Action items marked as `UNASSIGNED` are omitted from individual assignment emails and instead bundled exclusively into the primary meeting recap sent to the host and organizer.

---

5. User Experience & Design Specs

5.1 Post-Meeting In-App Summary Tab

Located inside the Huddle desktop and web apps under the meeting historical detail view.

``` +-----------------------------------------------------------------------+ | Meeting Summary: Product Architecture Sync [Share] [Edit]| | Today at 10:00 AM • 42 mins • 6 Attendees | +-----------------------------------------------------------------------+ | EXECUTIVE SUMMARY | | The team finalized the data migration strategy for Q3. Key risks | | around API rate limiting were addressed by introducing Redis caching.| +-----------------------------------------------------------------------+ | DECISIONS MADE | | • Chose Redis over Memcached for distributed cache. | | • Deferred mobile client updates to Sprint 44. | +-----------------------------------------------------------------------+ | ACTION ITEMS Auto-sending in [ 04:12 ] [x]| | [ + Add Action Item]| | [x] Task: Implement Redis cache layer | | Assignee: [ (Avatar) David Chen v ] Due: [ Friday v ] | | Source: "David: I can set up the cluster by end of week." | | | | [ ] Task: Verify Legal compliance on data retention | | Assignee: [ Unassigned v ] Due: [ Set Date v ] | | ! Flag: External dependency (Sarah / Legal was not present) | +-----------------------------------------------------------------------+ ```

5.2 Key UI Components

1. Assignee Dropdown Menu: * Filtered exclusively to individuals in the $EAR$. * Section 1: Authenticated Attendees (1-click select). * Section 2: Ad-Hoc / Guest Attendees (Displays `[Guest Name] - prompt for email`). * Option to select "Unassigned". * Explicitly disallows searching the global company directory within the "Assignee" field to prevent violating the non-attendee constraint. 2. Context Tooltip ("Source"): * Hovering over an action item shows the transcript snippet that triggered the assignment, allowing the host to quickly verify ambiguous attributions caused by diarization errors.

---

6. Email Dispatch Specs (Sales Notification System)

6.1 Individual Assignee Notification

  • To: Assignee email
  • Reply-To: Host email
  • Subject: `[Action Required] Action item from: {Meeting Title}`

``` Hi {Assignee First Name},

The following action item was assigned to you during {Meeting Title}:

Task: {Task Description} Due Date: {Due Date or "Not specified"} Context: "{Relevant transcript excerpt}"

View full meeting notes and recording: {Link to Huddle Meeting Record}

Did the AI get this wrong? Click here to reassign or remove: {One-Click Edit Link} ```

---

7. Telemetry, Edge Cases & Error Handling

Scenario / Edge CaseFailure Mode / RiskEngineering Resolution
:---:---:---
Multi-person room (Conference hardware)Single client IP/audio line represents 4 people; 1 is calendar-invited, 3 are walk-ins.PRE detects conference room profile. System assigns task to the Conference Room account by default, and sets an inline warning: "Assigned to hardware room. Click to reassign to an individual."
Cross-talk Diarization FlipsAudio assigns Alice's promise ("I'll ship it") to Bob.LLM applies verification: does speaker assignment align with prior conversation context? If confidence score $< 0.75$, fallback to `UNASSIGNED`.
Short/Accidental JoinsAttendee joined for 12 seconds, dropped, and was not present for decisions.PRE requires continuous session duration $\ge 60\text{ seconds}$ to enter the $EAR$.
Uninvited Guest Missing EmailUninvited guest gets an action item; auto-dispatch fails due to missing destination address.Action item is attributed to guest's display name. System flags: "Cannot send email: No address for [Display Name]." Host is prompted with an input box to provide an email.

---

8. Success Metrics

  1. Attribution Precision: $\ge 98\%$ of assigned action items correctly reflect real commitments made by that person (measured via sampling and low manual-edit rates).
  2. Zero-Hallucination Rate: $100\%$ compliance with the hard constraint: 0 automated emails dispatched to individuals outside the $EAR$.
  3. Dispatch Velocity: 95th percentile of summary and action item deliveries completed within $< 90\text{ seconds}$ post-meeting (or exactly at the expiration of the chosen grace period).
  4. Host Intervention Rate: $< 12\%$ of summaries require manual host corrections during the grace period window, indicating high trust in baseline accuracy.

Claims the judge couldn’t find in the brief

  • Manual note-taking results in lost context, untracked deliverables, and administrative drag.
  • Diarization errors mean the speaker label alone cannot be trusted for commitments.

Grades and run details

Decision model 45 · LLM judge 33
Decision model checks
  • failUses the supplied evidence correctly69%
  • passAddresses the actual decision64%
  • failRespects explicit constraints64%
  • failIdentifies material uncertainty40%
  • failAvoids unsupported claims48%
  • passProduces the required deliverable65%
  • passSpecifies AI failure behaviour99%
  • passSuccess settles trade-offs27%
  • partialProposes tests that could fail64%
  • partialSays who it's for, and who it isn't80%
  • failPlans how people will find it62%
Run
Run
#1
API response time
35 s
Submitted
1 Oct 2026

Needs more than a quick edit6 of 11 checks passed

The PRD is missing a time frame for the primary success metric, explicit user exclusion, discovery and team readiness plans, and testable gates with thresholds. These gaps would require more than a quick edit to fill.

Output

PRD: AI-Generated Meeting Summaries and Action Items

Audience: Engineering and design leads Product: Huddle Status: Draft

1. Summary

Huddle will generate a post-meeting summary that highlights decisions and action items, identifies owners when it can do so reliably, and can email confirmed action items to their owners automatically. This replaces the manual note-taking people do after calls.

The core safety requirement is non-negotiable: an action must never be assigned to someone who did not attend the meeting. Calendar invitations alone cannot establish attendance: some invitees do not join, and around 15% of meetings include someone who was not invited. Speaker labels also cannot establish identity on their own; they are wrong about 8% of the time, especially during interruptions or when several people share a meeting-room connection.

The product must therefore separate three questions: what was decided, what needs to be done, and who—if anyone—can be safely identified as the owner. When attendance or ownership is uncertain, Huddle must leave the action unassigned rather than guess.

2. Problem and goals

Today, attendees write their own notes after meetings. This is inconsistent, takes time, and makes it easy for decisions and follow-ups to be lost.

Goals - Produce a concise, useful summary after a recorded meeting. - Clearly distinguish decisions from discussion, proposals, and unresolved questions. - Extract action items and assign them only to verified meeting attendees. - When enabled, email confirmed action items to their owners shortly after the meeting ends. - Let users correct mistakes and give feedback.

Non-goals for the first release - Replacing full meeting transcripts or recordings. - Creating tasks in external project-management tools. - Inferring deadlines, priorities, or owners that were not stated. - Automatically assigning work when the owner is unclear. - Resolving disagreements about whether a decision was made without review.

3. Users and primary workflow

Users: meeting attendees who need an accurate recap; organizers who want to review or share it; and action owners who need a clear follow-up.

  1. A recorded meeting ends. Huddle gathers the transcript, speaker labels, calendar context, and actual session attendance data.
  2. Huddle generates a summary with separate Decisions, Action items, and, where useful, Open questions sections.
  3. For each action, Huddle identifies the supporting transcript evidence and checks the proposed owner against the verified attendee roster.
  4. If automatic action-item email is enabled for the organization, Huddle emails only actions with a sufficiently clear owner whose attendance is verified.
  5. The organizer and attendees can view the summary, correct it, and report errors. Unassigned actions remain visible for follow-up but are not emailed as assigned work.

4. Functional requirements

4.1 Establishing who attended

  • Use Huddle’s meeting-session join/leave records as the attendance source of truth. The calendar invite is context, not proof that someone attended.
  • Include identifiable people who joined without a calendar invitation. Exclude invitees who did not join.
  • Represent an attendee using a verified Huddle account, authenticated guest identity, or another approved identity mechanism.
  • If a meeting-room device represents several people and Huddle cannot reliably map speakers to individuals, do not treat the device name or a guessed speaker identity as a person. The organizer may confirm identities or owners in the summary UI.
  • If reliable session attendance data is unavailable, fail closed: generate the summary, but do not assign or email action items.

4.2 Generating decisions and action items

  • Generate a short overview and the three optional sections: Decisions, Action items, and Open questions.
  • Include a decision only when the transcript supports an explicit agreement or choice. Label proposals or unresolved discussion as open questions, not decisions.
  • Phrase each action as a concrete task. Include a due date only if one was explicitly stated; otherwise show no due date.
  • Attach a brief transcript excerpt and timestamp to each decision and action so users can check the source.
  • Do not invent missing details. If the task is clear but no owner is, show it as Owner not confirmed. If the task itself is ambiguous, flag it for review rather than present it as certain.

4.3 Assigning owners safely

  • Every proposed owner must match a verified person in the actual attendee roster. Enforce this as a backend validation rule, not just a model instruction or UI check.
  • A calendar invitee who did not join must never be assigned. A person who joined without an invite may be assigned if their identity and attendance are verified.
  • Speaker labels may help identify who volunteered, but must not independently authorize an assignment. When overlap, room audio, or attribution uncertainty makes ownership unclear, leave the action unassigned.
  • Users may assign or change an owner in the UI only to a verified attendee. Record the change and its actor in the audit log.
  • Recheck the attendance invariant before saving an assignment and immediately before sending an email.

4.4 Automatic emails

  • Provide an organization-level setting to enable automatic action-item emails. Once enabled, send an email after generation to each verified owner with at least one confirmed action.
  • Do not send an assignment email for an unassigned action or to anyone not verified as having attended. Do not send the full transcript by default.
  • Include the meeting title and date, the recipient’s confirmed actions, any explicitly stated due dates, a link to the Huddle summary, and a way to report an incorrect assignment.
  • If the meeting has no verified owners, send no owner emails. The organizer may receive a link to the summary according to organization settings.
  • Target delivery within five minutes of the meeting ending at the 95th percentile. If generation or identity checks fail, do not send speculative emails; show the organizer that the summary or email is delayed or needs review.
  • Provide clear controls to turn automatic emails off. Email delivery status and failures must be visible to the organizer.

5. UX requirements

The summary should make confidence and uncertainty understandable without exposing raw model scores. Show an Owner confirmed state for verified assignments and Owner not confirmed when a person cannot be safely identified. Present source timestamps as links into the recording or transcript, subject to existing access permissions.

Organizers must be able to edit summary text, remove an incorrect item, or assign an unassigned item to a verified attendee. Edits must be saved with a clear indication of who made them. If an action email has already been sent, do not silently replace the original assignment: notify the organizer that the owner changed and offer a corrected email to the new verified owner. Never email the former owner again unless explicitly requested.

The UI should explain that the calendar list may include people who did not attend and that unidentified room participants require confirmation. Use clear empty states when there are no decisions or actions.

6. Data, privacy, and operational requirements

Use only meeting data already available to Huddle for recording, transcription, and attendance, subject to existing recording consent and organization policies. Apply existing access controls to summaries, transcript evidence, and recording links. Store generated summaries, edits, assignment records, email status, and relevant audit events under Huddle’s existing retention and deletion policies. Do not use action-item content to train models unless the organization’s existing data policy explicitly permits it.

Generation and email must be idempotent: retries must not create duplicate summary items or send duplicate emails. Log the evidence reference, proposed owner, roster-validation result, any human correction, and delivery outcome for troubleshooting and audit.

7. Acceptance criteria

The release is acceptable only if: 1. A calendar invitee who did not join is never assigned or emailed an action. 2. An identifiable, authenticated attendee who joined without an invite can be assigned when the transcript supports the assignment. 3. An unclear speaker in overlapping audio produces an unassigned action, not a guessed owner. 4. A shared-room connection with no reliable person mapping cannot result in an individual assignment without organizer confirmation. 5. Every emailed action has a verified attendee owner and a link to its supporting summary. 6. Email retries do not result in duplicate messages. 7. Users can correct an assignment, and the correction is restricted to verified attendees.

8. Success metrics and rollout

Track summary generation success, time to summary, owner-confirmation rate, action-item correction and removal rates, email delivery time, duplicate-email rate, and user feedback. The primary safety metric is the number of assignments or assignment emails to non-attendees; the target is zero. Also monitor missed or unassigned owners to understand the trade-off between safety and usefulness.

Roll out behind an organization-level feature flag. Start with internal meetings, then a small customer cohort. Review false assignments and user corrections before expanding. Do not broaden automatic-email availability until attendance validation, idempotency, and failure handling pass the acceptance criteria.

9. Dependency to resolve before implementation

Engineering must confirm that Huddle can provide reliable, person-level join/leave records, including authenticated guests and shared-room behavior. If that data is unavailable or insufficient for a meeting, the safe behavior is to generate the summary without assigning or emailing owners.

Grades and run details

Decision model 68 · LLM judge 58
Decision model checks
  • failUses the supplied evidence correctly6%
  • passAddresses the actual decision88%
  • passRespects explicit constraints26%
  • passIdentifies material uncertainty86%
  • partialAvoids unsupported claims32%
  • passProduces the required deliverable75%
  • passSpecifies AI failure behaviour100%
  • partialSuccess settles trade-offs87%
  • partialProposes tests that could fail78%
  • partialSays who it's for, and who it isn't43%
  • partialPlans how people will find it99%
Run
Run
#1
API response time
34 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 8

Uses the supplied evidence correctlyWrongWrongMixed
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.

Gemini 3.8 Flash · API

The output claims that manual note-taking results in lost context, untracked deliverables, and administrative drag, and that speaker labels cannot be trusted for commitments, neither of which appears in the supplied evidence or follows by arithmetic.

GPT-6 Luna · API

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

Addresses the actual decisionRightMixedRight
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.

Gemini 3.8 Flash · API

The output commits to a specific design but does not state what result or condition would change that decision, as required by the criterion.

GPT-6 Luna · API

The PRD commits to a clear approach (generate summaries, assign only verified owners, email only confirmed actions) and states that the call depends on reliable join/leave records, with a fallback if unavailable.

Respects explicit constraintsRightMixedRight
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.

Gemini 3.8 Flash · API

The PRD enforces the hard constraint of never assigning actions to non-attendees via the Eligible Assignee Roster, validation logic, and UI restrictions, and it is written for engineering and design leads within the requested length.

GPT-6 Luna · API

The only explicit constraint is that actions must never be assigned to non-attendees, and the PRD enforces this through verified attendance, backend validation, and email rules; the form, audience, and length are also respected.

Identifies material uncertaintyRightWrongRight
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.

Gemini 3.8 Flash · API

The output does not name specific unknowns that could change the overall approach (e.g., if the 8% error rate is higher in practice) or how they would be resolved.

GPT-6 Luna · API

The PRD identifies the key uncertainty (availability of reliable person-level join/leave records) and specifies the safe fallback behavior if it is not available.

Avoids unsupported claimsWrongWrongRight
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.

Gemini 3.8 Flash · API

It presents interpretations like 'lost context, untracked deliverables, and administrative drag' and 'speaker label alone cannot be trusted' as established facts without labelling them as hypotheses.

GPT-6 Luna · API

No interpretations, causes, or forecasts are presented as established fact; the dependency on join/leave records is clearly labelled as needing confirmation.

Success settles trade-offsRightMixedWrong
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.

Gemini 3.8 Flash · API

Success metrics are given with targets but no time frame, and the PRD does not explicitly state which goal wins when two conflict (e.g., speed vs. accuracy), even though the zero-hallucination rule is a hard constraint.

GPT-6 Luna · API

The success metric (zero assignments to non-attendees) lacks a time frame, and no explicit trade-off rule (e.g., accepting lower coverage to maintain zero false assignments) is stated, only a mention of monitoring the trade-off.

Proposes tests that could failRightWrongWrong
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.

Gemini 3.8 Flash · API

No tests, gates, or kill criteria with numeric thresholds, measurement windows, and specified actions are proposed.

GPT-6 Luna · API

The acceptance criteria and rollout gates do not include numeric thresholds, measurement windows, or the specific actions each outcome would trigger, as required.

Says who it's for, and who it isn'tMixedWrongWrong
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.

Gemini 3.8 Flash · API

The PRD names the target audience (engineering, design, product ops) but does not explicitly name any group it deliberately leaves out, nor does a scope decision visibly follow from such an exclusion.

GPT-6 Luna · API

The PRD names target users (attendees, organizers, action owners) but does not explicitly name any group it deliberately leaves out, and no scope decision visibly follows from such an exclusion.

All got wrong 1

Plans how people will find itWrongWrongWrong
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.

Gemini 3.8 Flash · API

The PRD does not describe a staged rollout, how users will discover the feature, or which teams (support, sales) need to be ready.

GPT-6 Luna · API

While a staged rollout is described, the PRD does not say how users will discover the feature or which teams (support, sales) need to be ready, as required.

All got right 2

Produces the required deliverableRightRightRight
Haiku 5.5 · API

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

Gemini 3.8 Flash · API

The output is a complete PRD with specifications, UI mockups, and edge cases, written for engineering and design leads, and falls within the 1,000–1,500 word range.

GPT-6 Luna · API

The output is a complete PRD aimed at engineering and design leads, within the requested word count, and contains enough detail for them to start work.

Specifies AI failure behaviourRightRightRight
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.

Gemini 3.8 Flash · API

The PRD specifies fallback to UNASSIGNED on low confidence, deterministic validation that overwrites invalid assignments, a grace period for host review, and handling for missing emails and multi-person rooms.

GPT-6 Luna · API

The PRD specifies that when uncertain or when checks fail, actions are left unassigned, emails are not sent, and the organizer is notified of delays or needed review.

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 70% of 81 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 AstrawithChatGPT87.184.62None
2Sonnet 5.5withAPI75.979.82None
3GPT-6.1 SolwithAPI82.660.92None
4Haiku 5.5withAPI76.163.82None
5Opus 5.5withClaude75.960.62None
6GPT-6 LunawithAPI75.859.92None
7Gemini 3.8 FlashwithAPI49.828.22None
8Gemini 3.5 Flash-LitewithGemini49.832.12None

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.

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 77, 35× the cheapestGPT-6.1 Sol · API: task score 71, 14× the cheapestHaiku 5.5 · API: task score 67, the cheapestOpus 5.5 · Claude: task score 65, 85× the cheapest (1 of 2 outputs estimated)GPT-6 Luna · API: task score 65, the cheapestGemini 3.8 Flash · API: task score 39, 9.9× the cheapestGemini 3.5 Flash-Lite · Gemini: task score 39, 4.4× the cheapest (1 of 2 outputs estimated)GPT-6 Astra · ChatGPTSonnet 5.5 · APIGPT-6.1 Sol · APIHaiku 5.5 · APIOpus 5.5 · ClaudeGPT-6 Luna · APIGemini 3.8 Flash · APIGemini 3.5 Flash-Lite · Gemini
Nobody beats these on both score and costCost partly estimated (app runs with no usage meter)
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API651.0×
Haiku 5.5 · API671.2×
Gemini 3.5 Flash-Lite · Gemini394.4× (partly estimated)
Gemini 3.8 Flash · API399.9×
GPT-6.1 Sol · API7114×
Sonnet 5.5 · API7735×
Opus 5.5 · Claude6585× (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