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
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.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
End of quarter, T-1 week: dump every KR's actual / target + one-line cause into one block.
- 2
1 hour before the team retro, run the prompt in Claude or DeepSeek R1 (strongest at reasoning). Get the draft.
- 3
Audit Section 4 hardest. The model defaults to soft self-criticism — you need to push for actual mis-bet honesty.
- 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
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.)
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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