Write a stakeholder update after an unexpected scope change
Scope changes make stakeholders anxious. The update you send in the first 24 hours shapes whether they trust your team or start micromanaging. This prompt drafts a message that is honest without triggering panic.
You are a communications advisor helping me write a stakeholder update about a scope change. Here is my context:
Project name: {{PROJECT_NAME}}
Original scope or commitment: {{ORIGINAL_SCOPE}}
What changed and why: {{WHAT_CHANGED}}
Impact on timeline, budget, or deliverables: {{IMPACT}}
What I am doing about it: {{RESPONSE_PLAN}}
Stakeholder audience (role, seniority, how informed they were before): {{STAKEHOLDER_CONTEXT}}
Follow these steps:
1. Identify the single most important thing this audience needs to know first. Lead with that, not with background.
2. Draft a message in this structure:
- Opening: what changed and when you found out (one sentence)
- Impact: what this means for scope, timeline, or budget — be specific with numbers or dates where I gave them
- Response: what the team is doing and by when you will have a firmer answer
- Ask: one clear ask from the stakeholder, if any (decision, budget approval, patience)
3. Keep the message under 200 words. Use plain sentences. No bullet points unless the impact section has three or more items.
4. Write a one-line subject line for email.
5. Note if there is information I should not include in writing (e.g., unconfirmed vendor issues) and suggest how to handle it verbally instead. {{PROJECT_NAME}}{{ORIGINAL_SCOPE}}{{WHAT_CHANGED}}{{IMPACT}}{{RESPONSE_PLAN}}{{STAKEHOLDER_CONTEXT}}
How to use this prompt
- Copy the prompt above (Copy button on the top-right).
- Replace each
{{VAR}}with your own value. Variables:{{PROJECT_NAME}}{{ORIGINAL_SCOPE}}{{WHAT_CHANGED}}{{IMPACT}}{{RESPONSE_PLAN}}{{STAKEHOLDER_CONTEXT}}. - 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.
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