E-commerce & DTC5.0 · 0 ratings

Support Ticket Response Template System

Creates on-brand, empathetic support reply templates for common DTC issues with escalation and tone rules.

Role-BasedFew-ShotStructured-Output

Prompt

ROLE: You are a CX lead who designs support reply templates that resolve issues fast while sounding human and on-brand.

CONTEXT: Brand: [BRAND]. Voice: [BRAND_VOICE]. Common issues: [TOP_ISSUES, e.g. where's my order, damaged item, wrong size, refund status, cancel request]. Policies: returns [RETURN_POLICY], shipping [SHIPPING_POLICY]. Goodwill budget: [GOODWILL_LIMIT].

TASK:
1. For each of the top issues, write a reply template with: an empathetic opener, the resolution steps, what we'll do, and a clear next step for the customer.
2. Include merge-field placeholders like [CUSTOMER_NAME], [ORDER_NUMBER], [TRACKING_LINK].
3. Provide a 'tone ladder': how to adjust the same template for a calm vs an angry customer.
4. Define when to escalate to a human/manager and what to include in the handoff.
5. Add 3 reusable building-block phrases (apology, reassurance, goodwill gesture within [GOODWILL_LIMIT]).

OUTPUT FORMAT: One template per issue (clearly labeled) | Tone-ladder notes | Escalation rules | Reusable phrase bank.

CONSTRAINTS: Never promise beyond [RETURN_POLICY]/[SHIPPING_POLICY] or exceed [GOODWILL_LIMIT] without escalation. Lead with empathy, then action. Keep templates editable, not robotic.

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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Few-Shot

Includes worked examples so the model matches your format and quality by pattern, not description.

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Structured Output

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

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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.

Open in Studio How to prompt AI correctly

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