Operations & ContentKPI / OKR Review#OKR#retro#review

Ops OKR Review · More Than Just Scoring

End-of-quarter OKR review — don't just score. Use score + reason + learning + handoff. 1 hour for a retro the team actually reads.

Pain addressed · OKR retros degenerate into 'fill in the scoring template'. Real learning is skipped, and next quarter steps on the same rake.

Ops OKR Review · More Than Just Scoring

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Also available in 中文 →
You're my ops OKR retro partner. Help me turn this quarter's OKRs into a retro doc the team will actually read.

# This quarter's OKRs + completion data
{paste_okrs_with_completion}
Per KR, tag [score 0-1.0][actual vs target][one-line cause].

# Quarter context (what happened that affected OKRs)
{quarter_context}

# Team retro pain points (3-5)
{team_retro_themes}

# Output five sections

## 1. Overall judgment (50 words)
One-sentence judgment of the quarter. Not just average score — include "our biggest learning".

## 2. KR-by-KR audit (60 words per KR)
- KR name + score + actual / target
- Real cause for hit/miss (not "market conditions" cop-outs)
- What did we learn from this KR (specific, not abstract)
- Carry over or drop? If carry over, what does the revised version look like?

## 3. Systemic learnings (3, 60 words each)
Patterns across KRs, not single-KR things:
- e.g. "we always over-estimate launch traffic"
- e.g. "cross-team dependencies take ~30% longer than planned"
Each with one concrete change for next quarter.

## 4. The one thing we got wrong (60 words)
The most important mis-bet to own this quarter. Not "I'm a perfectionist" — actual "we bet X, turned out Y, should have done Z". Honesty is the retro's soul.

## 5. Carry / kill / new (one each, 30 words)
- Carry: continue but improve
- Kill: this quarter we did it; we shouldn't next quarter
- New: a fresh bet emerging from this quarter's learnings

# Boundaries
- KR scores must be honest. All 0.7+ = your OKRs were too soft.
- The "got wrong" section cannot be skipped — retro fails without it.
- Total ≤ 600 words. Team should finish in 30 minutes.
Saves ~90 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

    End of quarter, T-1 week: dump every KR's actual / target + one-line cause into one block.

  2. 2

    1 hour before the team retro, run the prompt in Claude or DeepSeek R1 (strongest at reasoning). Get the draft.

  3. 3

    Audit Section 4 hardest. The model defaults to soft self-criticism — you need to push for actual mis-bet honesty.

  4. 4

    At the retro, send the doc. Spend 30 minutes on reading + 30 minutes discussing systemic learnings. Don't go KR-by-KR — that's wasted time.

  5. 5

    Carry/kill/new becomes seed input for next quarter's OKR planning. Don't lose it.

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 KPI / OKR Review”. It folds in the concrete context a Operations & Content 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 KPI / OKR Review, 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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