Turn a strategy document into a first draft of OKRs
Most OKR-drafting sessions stall because the blank page feels too abstract. This prompt takes the strategy or priorities document you already have and extracts concrete Objectives and Key Results from it, saving the 90-minute whiteboard session.
You are a chief-of-staff helping a team translate strategy into measurable OKRs for the upcoming quarter.
Context I'm giving you:
- Strategy or priorities document: {{STRATEGY_DOC}}
- Team or function this applies to: {{TEAM_NAME}}
- Quarter and year: {{QUARTER_AND_YEAR}}
- Number of Objectives to aim for (usually 2-4): {{TARGET_OBJECTIVE_COUNT}}
Your task:
1. Read the strategy document and identify the 2-4 most important bets or outcomes described. Name each as a candidate Objective — a qualitative, inspiring statement of direction. Do not use metric language in the Objective itself.
2. For each Objective, draft 2-3 Key Results. Each Key Result must: (a) be measurable with a number or percentage, (b) describe an outcome not an activity, (c) be achievable within the stated quarter, and (d) include a suggested baseline if one can be inferred from the document, or flag [BASELINE NEEDED] if not.
3. After drafting, flag any areas where the source document is ambiguous or silent — these are gaps the team will need to resolve before finalising the OKRs.
4. Output the draft OKRs in a clean table: Objective | Key Result | Suggested Target | Baseline or Gap Flag.
Do not invent initiatives that aren't grounded in the source document. If the document is too vague to produce measurable KRs, say so explicitly rather than padding with generic metrics. {{STRATEGY_DOC}}{{TEAM_NAME}}{{QUARTER_AND_YEAR}}{{TARGET_OBJECTIVE_COUNT}}
How to use this prompt
- Copy the prompt above (Copy button on the top-right).
- Replace each
{{VAR}}with your own value. Variables:{{STRATEGY_DOC}}{{TEAM_NAME}}{{QUARTER_AND_YEAR}}{{TARGET_OBJECTIVE_COUNT}}. - Paste it into one of the recommended tools below.
- Iterate: tighten constraints in the prompt if the output is generic.
Why this prompt is structured this way
The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.
Pair this prompt with a tool
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