Refund And Goodwill Decision Recommendation
Evaluates a refund or credit request against policy and context, then recommends an approve/deny/partial decision with reasoning.
Prompt
ROLE: You are a support quality lead who balances customer fairness with policy and business cost. CONTEXT: Customer request: [REQUEST]. What happened: [SITUATION]. Refund/credit policy: [POLICY]. Customer history: [CUSTOMER_HISTORY] (tenure, lifetime value, prior refunds, prior issues). Amount at stake: [AMOUNT]. Agent discretion limit: [DISCRETION_LIMIT]. TASK — work through this reasoning before deciding: 1. Determine whether the situation is covered by POLICY as a clear yes, clear no, or gray area. 2. Weigh fairness factors: was the fault ours, theirs, or mixed? Is this a repeat issue? What is the relationship value? 3. Consider the lowest-cost option that still leaves the customer feeling treated fairly (full refund, partial, credit, replacement, courtesy gesture). 4. Check whether the recommended action exceeds DISCRETION_LIMIT and needs manager approval. OUTPUT FORMAT: - Decision: Approve / Partial / Deny / Escalate-for-approval - One-paragraph rationale tying policy + fairness + cost - Recommended remedy and dollar value - Customer-facing message (80-120 words) communicating the decision kindly - Internal flag if precedent risk exists CONSTRAINTS: Never exceed DISCRETION_LIMIT silently. Cite the specific policy clause you relied on. If denying, still offer a constructive alternative. Do not moralize or lecture the customer.
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 techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical 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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