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

Needs more than a quick edit3 of 11 checks passed

The output contains unsupported claims about the current situation that would need verification, and it lacks trade-off rules and a rollout plan.

Output

Product Requirements Document (PRD): AI-Generated Meeting Summaries & Action Items

1. Document Overview & Objective

1.1 Product Name

Huddle Intelligent Summaries (Internal Codename: Project Cue)

1.2 Target Audience

Engineering Leads, Frontend/Backend Engineers, and Product Designers.

1.3 Objective

Build an automated, post-meeting intelligence feature for Huddle that processes meeting transcripts to extract structured decisions, generate comprehensive summaries, and assign action items to attendees. The feature must account for real-world messy transcript data (speaker errors, uninvited participants) and adhere to strict safety boundaries regarding action item assignments and automated distribution.

1.4 Background & Scenario

Huddle is a video-meeting application currently utilized by 3,000 enterprise and mid-market companies. * Existing Infrastructure: Meetings are automatically recorded and transcribed with basic speaker labeling. Attendee lists are reliably synchronized from the originating calendar invite. * Current State & Pain Point: Users currently rely on manual post-meeting note-taking, leading to dropped tasks, misaligned follow-ups, and fragmented alignment. * Value Proposition: Automating meeting wrap-ups directly inside Huddle saves hours of manual administrative overhead per week4, driving higher platform stickiness and daily active use.

---

2. Core User Stories & Personas

2.1 Personas

  • The Engineering Manager (User): Attends 5–6 back-to-back technical syncs daily. Needs clear, unequivocal records of architectural decisions and precise engineering follow-ups without digging through raw, messy transcripts.
  • The Account Executive / Sales Rep (User): Operates on rapid customer cadences. Requires immediate, zero-friction distribution of post-meeting action items to stakeholders without manual typing.
  • The Engineering & Design Leads (Builders): Require explicit edge-case handling, system constraints, deterministic UI layouts, and scalable pipeline specs to build and ship the feature efficiently.

2.2 Key User Stories

  1. As a meeting participant, I want Huddle to automatically generate a structured summary and action item list the moment a meeting concludes, so I don't have to spend time writing manual recap notes.
  2. As an attendee with assigned tasks, I want clear ownership labels on my action items so there is never any ambiguity about who is driving a deliverable.
  3. As a sales representative, I want my action items emailed to me automatically as soon as the meeting ends, so I can immediately execute client follow-ups.
  4. As a meeting host, I want to review, edit, or override incorrect speaker attributions or bad action assignments before summaries are locked or shared externally.

---

3. Detailed Functional Requirements

3.1 Data Inputs & Pre-Processing Pipeline

The AI summarization engine triggers asynchronously via webhook immediately upon meeting termination (`recording.ended`).

  • Input A: Transcript with Speaker Labels: Full text payload containing timestamped utterances, text strings, and speaker IDs. Known limitation: Speaker labels suffer from an 8% error rate, primarily caused by cross-talk, overlapping dialogue, or multi-person huddle rooms.
  • Input B: Calendar Invite Attendee List: Ground-truth list of officially invited participants (Email, Name, Role).1 Known limitation: 15% of meetings include an uninvited participant (e.g., ad-hoc joiners via shared link).
  • Input C: Meeting Metadata: Duration, title, organization ID, and recording URL.

3.2 Summarization & Extraction Logic (LLM Pipeline)

The backend processing pipeline must execute three logical passes: 1. Meeting Overview Generation: Condense the transcript into a 3–5 sentence executive summary alongside 3 key thematic highlights. 2. Decision Extraction: Isolate explicit agreements, architectural choices, or policy resolutions made during the call. Format as an bulleted list of declarative statements (e.g., "Decided to migrate the primary auth service to OAuth2 by Q3"). 3. Action Item Extraction & Assignment Engine: * Extract explicit tasks, owners, and deadlines. * Crucial Constraint (Safety Guardrail): The engine must never assign an action item to someone who was not present in the meeting. * Resolution Strategy for Uninvited Attendees: If an uninvited participant (who joined without a calendar invite) is identified as the owner of a task, the system must cross-reference their presence against the audio/transcript participant logs. If verified present, they are added to a dynamic "Extended Participants" list for assignment validation. If their identity cannot be verified or they were absent, the task defaults to unassigned or falls back to the meeting host with a UI warning flag2.

3.3 Sales Request: Automated Instant Emailing

  • Requirement: Sales teams have requested that action items be emailed automatically to their owners the second a meeting ends.
  • Product Specification & Guardrail: Due to the 8% speaker label error rate and potential hallucination risks, fully autonomous external emailing on meeting close creates high business risk.
  • Implementation Design:
  • Implement an Opt-In Workspace Policy configured by organization admins.
  • For workspaces with "Auto-Send Sales Recaps" enabled, summaries and assigned action items are dispatched via email to internal organization members immediately upon processing completion.3
  • For external participants or non-opt-in workspaces, a 10-minute "Grace Period / Review Window" notification banner appears in the Huddle desktop client, allowing hosts to review and edit assignments before dispatch.

---

4. User Experience & Interface (UX/UI) Specifications

4.1 Post-Meeting View (Huddle Web & Desktop App)

Upon meeting conclusion, a new tab labeled "AI Summary" appears alongside the existing "Transcript" and "Recording" tabs within the meeting details drawer.

``` +-------------------------------------------------------------------+ | Huddle Meeting: Q3 Architecture Sync [Share] [Export]| +-------------------------------------------------------------------+ | Tabs: [Overview] [Transcript] [Recording] | |-------------------------------------------------------------------| | EXECUTIVE SUMMARY | | The team reviewed the Q3 scaling bottlenecks. Consensus was | | reached on moving auth infrastructure to OAuth2. Database latency | | issues will be mitigated via Redis caching layers. | | | | KEY DECISIONS | | • Migrate core auth service to OAuth2 by August 15. [Edit] | | • Adopt Redis cluster for session state caching. [Edit] | | | | ACTION ITEMS | | [x] Investigate OAuth2 token expiry edge cases | | Owner: Sarah Jenkins (Calendar Invite) | Due: Next Tuesday | | [ ] Set up staging Redis cluster | | Owner: Unassigned ⚠️ (Verify Attendee) | Due: Friday | | | | [Send Sales Recap Email Now] [Approve & Lock] | +-------------------------------------------------------------------+ ```

4.2 Interactive States & Edge-Case UI Handling

  • Unassigned / Unverified Owner State: If the model detects an action item owner who was an uninvited participant or has an ambiguous speaker label, render a yellow warning badge: `⚠️ Unverified Owner — Click to Reassign`.
  • Inline Editing: Every decision and action item card features an inline pencil icon allowing hosts or assignees to edit text, change owners via a searchable dropdown of confirmed attendees, or delete hallucinated items.

---

5. Technical Architecture & Engineering Guidelines

5.1 Pipeline Flow & Data Schema

  1. Webhook Listener: Receives `meeting.ended` event containing metadata and storage URIs.
  2. Context Aggregator: Merges calendar invite list (`invitedusers[]`) with actual audio track speaker diarization outputs (`detectedspeakers[]`).
  3. LLM Structured Extraction: Prompt engineering optimized for JSON output schema enforcement (using strict JSON mode / function calling):

```json { "meetingid": "string", "summary": "string", "decisions": ["string"], "actionitems": [ { "task": "string", "assigneeemail": "string | null", "assigneename": "string", "isverifiedattendee": boolean, "due_date": "ISO-8601 | null" } ] } ```

5.2 Error Handling & Fallbacks

  • Speaker Label Inaccuracy (8% Error Rate): Mitigation via UI feedback loops. If an assignee name has low confidence score (< 0.85) from the diarization model, flag the action item with a review indicator in the UI.
  • Uninvited Participant Handling (15% Occurrences): The validation layer must query the roster of active audio streams. If an email is missing from the calendar invite but present in audio metadata, tag them as `guest_participant` and permit assignment only if their voiceprint or explicit self-identification ("Hi, this is Dave from Finance joining late") is logged in the transcript text. Otherwise, fail-safe to unassigned.

---

6. Non-Functional Requirements & Constraints

  • Latency: Summary generation and database write must complete within < 120 seconds of meeting termination for a standard 45-minute meeting.
  • Security & Privacy: Meeting transcripts and summaries must be encrypted at rest (AES-256) and in transit (TLS 1.3). Customer data must never be used to train foundational models across tenant boundaries.
  • Reliability: 99.9% pipeline uptime. In the event of an LLM timeout or parsing failure, the system must gracefully fall back to displaying raw transcripts with an error banner stating "AI summary generation delayed. Click to retry."

---

7. Metrics & Success Criteria (KPIs)

  1. Adoption Rate: % of completed meetings where users view, edit, or export the AI summary (Target: > 40% within 30 days post-launch).
  2. Action Item Completion Rate: Increase in marked-complete action items compared to legacy manual workflows.
  3. Accuracy & Correction Rate: Frequency of manual edits made to action item owners and speaker assignments (Target: < 5% manual override rate, validating low false-positive rates for uninvited attendee checks).
  4. Sales Workflow Efficiency: Reduction in post-call administrative turnaround time reported by enterprise sales teams using the automated email dispatch feature.

What a PM had to fix

  1. 1Constraint missedRestore the constraintStart again

    Source: ConstraintSummaries must never assign an action to someone who was not in the meeting.

    What we’d changeBuild the list of possible owners from who actually joined, not who was invited, and enforce it in code on generated owners, manual edits and sending. A model-set is_verified_attendee flag can't guarantee it.

  2. 2Constraint missedRestore the constraintSubstantial rework

    What we’d changeLeave items with an unverifiable owner unassigned. Handing them to the host invents responsibility nobody agreed to.

  3. 3Constraint missedRestore the constraintSubstantial rework

    Source: Known limitsSpeaker labels are wrong about 8% of the time

    What we’d changeDon't auto-send. With speaker labels wrong 8% of the time, action items would reach the wrong owners before anyone checks them. Keep organiser review before any email.

  4. 4Hypothesis stated as factReframe it as a hypothesisQuick edit

    What we’d changePresent the time saved as a goal to measure, not a known benefit.

Claims the judge couldn’t find in the brief

  • Huddle is a video-meeting application currently utilized by 3,000 enterprise and mid-market companies.
  • Users currently rely on manual post-meeting note-taking, leading to dropped tasks, misaligned follow-ups, and fragmented alignment.
  • The Engineering Manager (User): Attends 5–6 back-to-back technical syncs daily.

Grades and run details

Decision model 45 · LLM judge 33
Decision model checks
  • failUses the supplied evidence correctly15%
  • passAddresses the actual decision44%
  • failRespects explicit constraints40%
  • partialIdentifies material uncertainty20%
  • failAvoids unsupported claims41%
  • passProduces the required deliverable56%
  • passSpecifies AI failure behaviour92%
  • partialSuccess settles trade-offs54%
  • partialProposes tests that could fail71%
  • partialSays who it's for, and who it isn't84%
  • failPlans how people will find it41%
Artefacts
Run
Run
#1
Time to output
15 s
Submitted
24 Sept 2026

Needs more than a quick edit9 of 11 checks passed

The PRD relies on an unverified join log and identity tiers that are not in the context; a PM would need to re-check feasibility and rework the roster approach.

Output

PRD: AI Meeting Summaries — Decisions & Action Items

Sep 24, 2026 · @Martin Slaney

Summary

We will generate a summary after every recorded Huddle meeting that lists the decisions made and the action items, each with an owner who was verifiably in the meeting. The organizer reviews the summary and sends it in one click; nothing is emailed without that step in v1.

Problem. Today attendees write their own notes after the call. Notes are inconsistent, often missing, and action items lose their owners within a day. We already have what we need to fix this: a recording, a transcript with speaker labels, and the calendar attendee list.

Why this is not trivial. Two of those inputs are unreliable. Speaker labels are wrong about 8% of the time, mostly during crosstalk and when several people share a meeting-room device. About 15% of meetings include someone who joined without a calendar invite. A naive build would misassign owners often enough to lose trust in the first week.

Goals and metrics

The goal is summaries people send without rewriting, with zero actions assigned to non-attendees.

Goals

  • Replace manual post-meeting notes for recorded meetings.
  • Every action item has a correct owner or is explicitly marked unassigned.
  • Get action items to owners within minutes of the meeting ending.

Non-goals (v1)

  • Pushing tasks into Jira, Asana or Linear.
  • Summaries for unrecorded meetings or live, in-call summaries.
  • Improving diarization itself. We design around the 8% error rate, not fix it.

Success metrics

MetricTargetType
Actions assigned to someone not in the meeting0Hard constraint, blocks release
Owner precision on items sent with an owner≥ 97%Quality
Summaries sent within 30 min of meeting end≥ 60% of recorded meetingsAdoption
Summaries sent with no edits≥ 50%Quality
Owner reassignments per sent summary≤ 0.3Quality

Owner precision is measured from organizer edits plus a weekly labelled sample of 200 meetings.

Users and core flow

The organizer owns the summary; attendees receive it.

  • Organizer: reviews, fixes owners, sends. Usually the calendar invite's creator; can hand off to any attendee.
  • Action owner: receives their items by email and in the app.
  • Other attendees: receive the full summary once sent.

```mermaid flowchart LR A[Meeting ends] --> B[Transcript +<br/>join log ready] B --> C[Build verified<br/>roster] C --> D[Extract decisions<br/>and actions] D --> E[Roster filter +<br/>confidence check] E --> F[Organizer review] F --> G[Send to owners<br/>and attendees] ```

The roster filter runs as deterministic code after the model, never inside the prompt alone.2 Target: summary ready for review within 5 minutes of meeting end for meetings up to 90 minutes.

Requirements

An owner can only be chosen from the verified roster: people who actually joined, not people who were invited.

Verified roster

1. R1. Build the roster from the join log, not the calendar invite.1 Invited people who never joined are excluded. 2. R2. Each roster entry has an identity tier: - Verified: joined signed in to a Huddle account, or by a dial-in matched to a known number. - Guest: joined with a display name only (most of the 15% uninvited joiners). - Room: a meeting-room device; people behind it are unknown individuals. 3. R3. Match uninvited joiners to workspace accounts by signed-in account only. Never match on display name alone.

Summary content

  1. R4. Decisions: a short statement of each decision, with a transcript timestamp link. No owner field.
  2. R5. Action items: task, owner (or "Unassigned"), due date if said aloud, timestamp link, and confidence.
  3. R6. Every item links to its evidence in the transcript so reviewers can check it in one click.

Assignment rules

  1. R7. The model returns a proposed owner per item. Code then checks it against the roster. Any owner not on the roster is replaced with "Unassigned", and the named person is shown as a mention ("mentions: Dave, not in meeting").
  2. R8. Owner comes from content first: a named request ("Priya, can you…") beats the speaker label of "I'll do it".
  3. R9. Mark an owner low confidence when the evidence depends on a speaker label in an overlapping segment, a Room entry, or a Guest entry.
  4. R10. Low-confidence items show as "Suggested: Priya" and need organizer confirmation before any email goes out.
  5. R11. Room and Guest entries cannot receive email. The organizer can reassign to a verified person or leave it unassigned.
  6. R12. Log every model proposal, filter result and organizer edit. This is our precision measurement.

Handling the known limits

Both limits hit ownership, not decisions, so we contain them at the owner step.

LimitWhat goes wrongHow we handle it
Speaker labels wrong \~8% (crosstalk)"I'll take that" credited to the wrong personContent-first owner rule (R8); overlap segments flagged low confidence (R9)
Speaker labels wrong \~8% (shared room)One label covers several peopleRoom tier; items resolved to a room need a named person or stay unassigned (R11)
\~15% of meetings have uninvited joinersReal participant missing from roster, or matched to the wrong accountRoster from join log (R1); account match only (R3); Guests can own items but get no email
Invited but absentModel assigns to a name from the inviteExcluded from roster; hard filter (R7)

The error compounds per meeting. If each item's owner is wrong 8% of the time, a meeting with 5 action items has a 34% chance of at least one wrong owner (1 − 0.92⁵). That is why confirmation sits before email, not after.

Sales request: auto-email on meeting end

Decision: not in v1 as requested. We ship one-click organizer send now, and gated auto-send in phase 2 once measured precision earns it.

Why not now. An email to the wrong owner is the most visible failure this feature can have. With the error rate above, roughly a third of 5-item meetings would email someone a task that isn't theirs. With external guests, it may land outside the customer's company. Early trust is hard to win back.

What Sales gets instead.

  • v1: the organizer gets a "Summary ready" notification within 5 minutes and sends with one click. Goal: sent within 30 minutes (see metrics).
  • Phase 2: a workspace setting to auto-send at meeting end, but only items that are high confidence and owned by a Verified attendee. Everything else still waits for the organizer. It ships only when owner precision on high-confidence items is ≥ 97% for 4 consecutive weeks.
  • Auto-send never emails Guest or Room entries, or anyone outside the customer's domain, unless an admin enables it.

Design notes

The review screen must make checking owners faster than writing notes.

  • Decisions first, then action items grouped by owner, then Unassigned.
  • Low-confidence owners look visibly different ("Suggested") and are editable inline from a roster picker, never free text.
  • Each item plays its transcript clip on click.
  • The Send button states who gets email: "Send to 4 owners and 7 attendees".
  • Empty state: if no decisions or actions are found, say so plainly rather than inventing any.

Rollout

PhaseScopeExit criteria
InternalHuddle's own meetingsZero non-attendee assignments; precision logged
Beta50 opted-in companies≥ 97% owner precision on sent items; ≥ 40% sent within 30 min
GAAll 3,000 companies, organizer sendBeta targets held for 4 weeks
Phase 2Gated auto-send settingSee Sales section

Open questions

  • [ ] Is the join log's signed-in identity reliable enough to call "Verified"? Engineering to confirm.
  • [ ] Who organizes when the invite creator didn't attend? Proposed: first Verified attendee from the invite.
  • [ ] Do customers need to disable summaries per meeting for sensitive calls? Legal and Sales to confirm.
  • [ ] Retention: how long do we keep drafts and the evidence log?

What a PM had to fix

  1. 1Invented evidenceVerify or remove the claimTargeted repair

    Source: Scenariothe attendee list comes from the calendar invite

    What we’d changeTreat the join log as a dependency to confirm, not an input that exists today. Say what proves attendance, and don't promise zero non-attendee assignments until that's settled.

  2. 2Constraint missedRestore the constraintTargeted repair

    Source: ConstraintSummaries must never assign an action to someone who was not in the meeting.

    What we’d changeApply the roster check to organiser edits and the final send as well, not only to the model's output.

Claims the judge couldn’t find in the brief

  • There is a join log that records who actually joined.
  • The system can distinguish between verified, guest, and room joiners (identity tiers).
  • The join log's signed-in identity is reliable enough to call 'Verified'.

Grades and run details

Decision model 73 · LLM judge 75
Decision model checks
  • failUses the supplied evidence correctly16%
  • passAddresses the actual decision99%
  • passRespects explicit constraints69%
  • passIdentifies material uncertainty88%
  • failAvoids unsupported claims43%
  • passProduces the required deliverable80%
  • passSpecifies AI failure behaviour100%
  • passSuccess settles trade-offs95%
  • partialProposes tests that could fail56%
  • passSays who it's for, and who it isn't85%
  • partialPlans how people will find it86%
Artefacts
Run
Run
#1
Time to output
80 s
Submitted
24 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 6

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 3.5 Flash-Lite · Gemini

The PRD commits to a design but does not state what result or condition would change that answer.

Opus 5.5 · Claude

Commits to a clear answer (organizer review, no auto-email in v1) and states the precision condition that would change it.

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 3.5 Flash-Lite · Gemini

The constraint that actions must never be assigned to non-attendees is enforced via verification and fallback to unassigned.

Opus 5.5 · Claude

Respects the non-attendee constraint with a hard roster filter and organizer review; the PRD is within the requested length and for the right audience.

Success settles trade-offsRightWrongRight
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 3.5 Flash-Lite · Gemini

It defines success metrics with targets and time frames but gives no explicit trade-off rule (e.g., precision vs. coverage).

Opus 5.5 · Claude

Defines success metrics with targets and time frames, and gives an explicit trade-off (gated auto-send only after precision threshold, accepting lower coverage).

Proposes tests that could failRightWrongRight
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 3.5 Flash-Lite · Gemini

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

Opus 5.5 · Claude

Rollout gates have numeric thresholds (0 non-attendee assignments, ≥97% precision), measurement windows (4 weeks), and clear actions (proceed or not).

Says who it's for, and who it isn'tMixedWrongRight
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 3.5 Flash-Lite · Gemini

It names target users (Engineering Manager, Account Executive) but does not explicitly exclude any group or make a scope decision from an exclusion.

Opus 5.5 · Claude

Names organizer, action owner, and attendees as users; explicitly excludes unrecorded meetings, live summaries, and task integrations.

Plans how people will find itWrongWrongRight
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 3.5 Flash-Lite · Gemini

It lacks a staged rollout, user discovery plan, and identification of teams that need to be ready (support, sales).

Opus 5.5 · Claude

Includes a staged rollout (internal, beta, GA), a discovery mechanism (organizer notification), and mentions Sales and Legal as teams that need to be ready.

All got wrong 2

Uses the supplied evidence correctlyWrongWrongWrong
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 3.5 Flash-Lite · Gemini

The output adds 'enterprise and mid-market' to the company count and invents pain-point consequences not in the brief.

Opus 5.5 · Claude

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.

Avoids unsupported claimsWrongWrongWrong
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 3.5 Flash-Lite · Gemini

It presents the consequences of manual note-taking (dropped tasks, misalignment) as established fact when the brief only states people write their own notes.

Opus 5.5 · Claude

Presents the join log and identity tiers as established facts without labelling them as assumptions, when the context provides no support.

All got right 3

Identifies material uncertaintyRightRightRight
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 3.5 Flash-Lite · Gemini

It names the 8% speaker error and 15% uninvited participant rates, bounds them, and specifies how they are handled (UI flags, fallback).

Opus 5.5 · Claude

Names unknowns like join-log reliability, organizer fallback, and sensitive-call handling, and says how to resolve them.

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 3.5 Flash-Lite · Gemini

The PRD is in the requested form for engineering and design leads, within the word limit, and contains enough detail to start work.

Opus 5.5 · Claude

Delivers a complete PRD with goals, metrics, requirements, design notes, and rollout, usable by engineering and design leads.

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 3.5 Flash-Lite · Gemini

It specifies low-confidence flags, unassigned fallback, error banners, and a review window for auto-email.

Opus 5.5 · Claude

Specifies low-confidence marking, unassigned fallback, no email to unverified entries, and empty-state handling.

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