PM Year-End Review · Product Judgment First
PM year-end isn't about features shipped — use 'shipped + learned + mis-bet + next' to show leadership your product judgment is compounding.
Pain addressed · PM year-end stage reviews fail on 'list of shipped features' — leadership actually cares about 'is your judgment growing / which mis-bet did you own?'
PM Year-End Review · Product Judgment First
You're my PM year-end review partner. Help me write a review leadership actually wants to read.
# Full year of PM work
{paste_yearly_work}
Includes shipped features / metric impact / user insights / mis-bets / team dynamics / personal growth. Order doesn't matter.
# What my manager expected of me (set at start of year)
{manager_expectations}
# Output four sections
## 1. Shipped + impact (120 words)
Not a feature list — "shipped X · user / business metric moved Y".
- Pick 3-5 highest-impact ships, one line + number each
- One story hook (how a user actually uses it / how the team built it) so the list has texture.
## 2. Mental model updates (2-3, 80 words each)
The year's most important product judgment updates:
- e.g. "I thought power users were the north star; reality showed medium-engagement was the lever"
- e.g. "I thought slow ship = failure; turned out shipping + failing per feature = compounding learning"
This is the PM's real year-end value, not the ship list.
## 3. The one mis-bet I own (80 words)
The year's most important product judgment to own:
- Not "engineering was 2 weeks late" execution miss
- Must reach "I bet X, should have bet Y, the root mistake was my judgment about Z"
- One line: what mental model did this teach me?
## 4. The one big thing for next year (60 words)
Not 5 things — one:
- Include: north star it serves / your hypothesis / kill criteria (when to abandon)
- One line: why this, not something else.
# Boundaries
- No "kept shipping" log filler.
- The mis-bet must reach judgment layer, not execution.
- The "one big thing" is exactly one. Five = you haven't decided.Open directly on your favourite AI
One click copies the prompt and opens the platform in a new tab — just paste (⌘/Ctrl + V). Recommended platforms are ordered by content fit.
Workflow · 5 steps
- 1
T-2 weeks before review: dump the year's shipped / metrics / mis-bets / personal growth into one block.
- 2
Run in Claude or DeepSeek R1. Fill the 2 placeholders.
- 3
Push hard for specificity in mental model updates. Abstract = nothing was really learned.
- 4
Audit the mis-bet yourself. It must reach judgment, not execution.
- 5
Send a draft to your manager 1 week before the review. Their reaction tells you whether you've genuinely grown as a PM.
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.)
Why this prompt works
This prompt isn't a generic “write me a Year-End Stage Review (CN-only)”. 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 Year-End Stage Review (CN-only), 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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