Product ManagerMeeting Notes & Decision Sync#PM#spec review#decision

Spec Review · Decision-Hypothesis-Objection 3-Column

Capture spec review discussion as "decisions - hypotheses - objections" 3-column for future traceability.

Pain addressed · Pain #1: AI spec-review notes nail 'we decided X' but drop 'why' and 'who disagreed' — two weeks later nobody remembers the reasoning behind the call.

Spec Review · Decision-Hypothesis-Objection 3-Column

Prompt·Advanced
Also available in 中文 →
You're a PM skilled at preserving decision context. Convert this spec review meeting into a "Decisions - Hypotheses - Objections" 3-column record, so the rationale is traceable 2 weeks later.

# Feature under review
{feature_under_review}

# Raw discussion
{paste_raw_discussion}

# Attendees by role
{attendees_by_role}

## Col 1: Decisions Made
| Item | Resolution | Decided by |
- Specific decisions, not "we discussed"
- Resolutions as verb statements
- Named decision-maker

## Col 2: Hypotheses Behind
For each decision:
- **Decision**: brief restate
- **Core hypothesis**: user/data/tech assumption
- **Risk**: what if the hypothesis is wrong
- **Validation plan**: post-launch metric + when to evaluate

## Col 3: Dissents & Concerns
By "raised by / specific point / how we responded":
- Don't smooth dissent ("we'll consider")
- For each: "why not adopted now" or "re-review on X date"
- Strong unconvinced parties: tag "⚠️ Strong objection, archive"

## Col 4: Follow-ups
Checkbox list: [ ] Owner — action — due date.

# Rules
- Don't transcribe everyone's words
- Document objections — that's the whole point
- Validation plans need a metric + time
- ≤ 600 words
Saves ~60 min per use

Open directly on your favourite AI

One click copies the prompt and opens the platform in a new tab — just paste (⌘/Ctrl + V).

Workflow · 5 steps

  1. 1

    During the review, have someone (you or a PM intern) capture talking points in real time. Process within 4 hours or the context evaporates.

  2. 2

    Paste the raw record, label attendees by role, fill placeholders.

  3. 3

    When the 4-column doc comes back, audit Column 3 (Dissents) hardest — did the model actually surface objections, or did it smooth them out?

  4. 4

    Column 2 validation plans tend to be too abstract. Add a real metric and a date yourself.

  5. 5

    Send the doc to all attendees and explicitly @ the dissenters: 'Did I capture your objection accurately?'

Verify before you ship

AI's "almost right" is the #1 pain. Tick these off so reviewers can't find the obvious holes. (Checkbox state stays local — we don't track it.)

Done 0 / 5 checks

Why this prompt works

This prompt isn't a generic “write me a Meeting Notes & Decision Sync”. It folds in the concrete context a Product Manager actually faces (target reader, info density, deliverable format). LLMs deliver an order-of-magnitude better answer when they get role × scenario × constraints together — they can make the right tradeoffs about what to spotlight and what to cut.

When to use

Best moment: you know roughly that you need to do a Meeting Notes & Decision Sync, but you're stuck on “how do I even ask the AI?” Muscle memory has no template. Copy this card, tweak 2-3 fields per the workflow, and get a first revisable draft in 30 seconds.

Common pitfalls

  • Send without tweaking placeholders · the draft reads as default-GPT and your reviewer spots it in 3 seconds.
  • Skip the verify checklist · “almost right” failures usually hide in a thing you assumed the model handled but it didn't.
  • Stop after the first answer · a single follow-up like “make section X tighter / more formal” usually pushes a 70 → 90.

Variants & extensions

Want to use this card more aggressively? Three extensions:

  • Swap languages: both EN/ZH prompts ship in the card — use the EN version when shipping to a Western audience.
  • Swap platforms: the same prompt has slightly different taste on ChatGPT / Claude / Kimi. The recommended-platform pill marks the best fit.
  • Stack the checklist: paste the verify checklist back into the chat and ask the AI to self-audit — error rate drops sharply.
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