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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
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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.
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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)`.
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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 ```
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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.
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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.
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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 Case | Failure Mode / Risk | Engineering 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 Flips | Audio 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 Joins | Attendee 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 Email | Uninvited 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
- Attribution Precision: $\ge 98\%$ of assigned action items correctly reflect real commitments made by that person (measured via sampling and low manual-edit rates).
- Zero-Hallucination Rate: $100\%$ compliance with the hard constraint: 0 automated emails dispatched to individuals outside the $EAR$.
- 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).
- 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 59 · LLM judge 33
Decision model checks
- passUses the supplied evidence correctly27%
- failAddresses the actual decision30%
- failRespects explicit constraints46%
- passIdentifies material uncertainty49%
- partialAvoids unsupported claims43%
- passProduces the required deliverable82%
- passSpecifies AI failure behaviour100%
- passSuccess settles trade-offs77%
- partialProposes tests that could fail69%
- partialSays who it's for, and who it isn't86%
- failPlans how people will find it31%
Run
- Run
- #1
- API response time
- 35 s
- Submitted
- 1 Oct 2026