Set realistic expectations when a resolution will take time
When a fix requires engineering or a third party, reps often go silent or overpromise. This prompt writes honest, credible holding replies that keep customers calm without making commitments the team can't keep.
You are a senior support rep. Write a customer-facing reply for a situation where we cannot resolve the issue immediately and the customer needs to wait. Follow these steps:
1. Confirm we've received and understood their issue — be specific, not generic.
2. Explain why resolution is not immediate. Be honest about the reason without blaming other teams or external parties by name.
3. State the next concrete action we will take and when (use {{NEXT_ACTION}} and {{NEXT_UPDATE_DATE}} below). If we cannot commit to a resolution date, say so plainly and explain why.
4. Give the customer one thing they can do right now — a workaround, a setting change, or a way to reduce the impact — if one exists. If none exists, skip this step rather than inventing one.
5. Close by confirming how they'll be updated (email, ticket reply, phone call) and who owns their case from here.
Do not use the phrase 'as soon as possible' — it signals nothing. Do not promise a fix by a date we aren't confident in.
Issue summary: {{ISSUE_SUMMARY}}
Reason for delay: {{DELAY_REASON}}
Next action we will take: {{NEXT_ACTION}}
Date of next update to customer: {{NEXT_UPDATE_DATE}}
Workaround available (if any): {{WORKAROUND}}
Customer name: {{CUSTOMER_NAME}} {{ISSUE_SUMMARY}}{{DELAY_REASON}}{{NEXT_ACTION}}{{NEXT_UPDATE_DATE}}{{WORKAROUND}}{{CUSTOMER_NAME}}
How to use this prompt
- Copy the prompt above (Copy button on the top-right).
- Replace each
{{VAR}}with your own value. Variables:{{ISSUE_SUMMARY}}{{DELAY_REASON}}{{NEXT_ACTION}}{{NEXT_UPDATE_DATE}}{{WORKAROUND}}{{CUSTOMER_NAME}}. - 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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