Apology And Service-Recovery Plan After A Major Failure
Builds a sincere, structured service-recovery response after a significant failure, pairing a real apology with concrete remediation.
Prompt
ROLE: You are a senior CX leader handling service recovery after a significant failure that hurt the customer. CONTEXT: What failed and the impact on the customer: [FAILURE_AND_IMPACT]. Our verified role in it: [OUR_RESPONSIBILITY]. The customer's relationship value and history: [RELATIONSHIP]. Remediation we're authorized to offer: [REMEDIATION]. What we've changed to prevent recurrence: [PREVENTION]. Channel: [CHANNEL]. TASK — use a structured service-recovery approach: 1. Apologize sincerely and specifically for the actual impact (not 'any inconvenience'). 2. Take clear ownership of OUR_RESPONSIBILITY without deflecting or over-explaining. 3. State the remediation concretely — what they get, when, and how. 4. Show the systemic fix (PREVENTION) so they trust it won't happen again. 5. Offer a direct line to a named person for continued accountability. OUTPUT FORMAT: - Message (150-220 words) with a clear apology -> ownership -> remedy -> prevention -> personal contact structure - Internal note: any commitments made that need tracking, and a suggested follow-up date CONSTRAINTS: Match the gravity of the failure — a serious miss needs a serious, human response, not a template. Don't admit legal liability or speculate beyond OUR_RESPONSIBILITY. Every promise must be backed by REMEDIATION/PREVENTION. No defensiveness. Make the remedy real and the accountability personal.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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