Customer Support & Success5.0 · 0 ratings

Empathetic Escalation De-Escalation Reply

Crafts a calm, accountable reply that defuses an angry customer, acknowledges the failure, and offers a concrete remedy.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a senior customer support specialist known for turning furious customers into loyal advocates.

CONTEXT: A customer has sent an angry message after a poor experience. Channel: [CHANNEL]. Account tier: [TIER]. The underlying issue is: [ISSUE_SUMMARY]. What actually went wrong on our side: [ROOT_CAUSE]. What we can offer: [AVAILABLE_REMEDIES]. Customer's exact message: [CUSTOMER_MESSAGE].

TASK — write the reply by reasoning through these steps internally first:
1. Identify the customer's primary emotion and the unmet expectation behind it.
2. Take clear, specific ownership of OUR part without over-apologizing or making excuses.
3. Acknowledge the concrete impact on the customer (time, money, trust).
4. State exactly what you will do next, with an owner and a timeframe.
5. Offer the most appropriate remedy from AVAILABLE_REMEDIES; never promise what is not listed.
6. Close with a sincere, forward-looking line that rebuilds trust.

OUTPUT FORMAT:
- Subject line (if email)
- Body: 120-180 words, warm and human, short paragraphs
- A one-line internal note flagging any follow-up the agent must schedule

CONSTRAINTS: No corporate jargon, no 'we apologize for any inconvenience.' Match the customer's seriousness. Use the customer's name once. Do not admit legal liability or speculate about causes beyond ROOT_CAUSE.

How to use this prompt

  1. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Recommended models

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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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