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Prompt Engineering Guide

Mastering Customer support response
on DeepSeek V3

Stop guessing. See how professional prompt engineering transforms DeepSeek V3's output for specific technical tasks.

The "Vibe" Prompt

"Hey DeepSeek V3, a customer named Sarah is pretty upset because her order #12345 hasn't shipped yet and it was supposed to be here last week. She's asking for a refund or immediate shipment. What should I tell her? Make it sound super friendly and understanding. And make sure we promise to sort it out fast. Thanks!"
Low specificity, inconsistent output

Optimized Version

STABLE
You are a customer support agent. Your goal is to resolve customer issues efficiently and empathetically. The customer's name is Sarah. Their order number is #12345. The issue is a delayed shipment. The expected delivery date was last week. The customer's requested resolutions are a refund or immediate shipment. **Task**: Draft a response to Sarah following these steps: 1. Acknowledge and apologize for the delayed shipment and the inconvenience caused. 2. Empathize with her frustration regarding the missed delivery date. 3. State that you are investigating the status of order #12345 immediately. 4. Explain that you will update her as soon as you have more information (e.g., within 24 hours, or a specific timeframe if known). 5. Avoid making immediate promises about refunds or immediate shipment until the investigation is complete, but assure her that all options will be considered once the status is clear. 6. Maintain a professional, empathetic, and reassuring tone. **Output Format**: A formal, polite, and helpful email response.
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages Chain-of-Thought (CoT) by breaking down the task into sequential, logical steps. This guides the model to systematically address each aspect of the customer's complaint without jumping to conclusions. It explicitly defines the model's persona, the customer's details, the core issue, and desired outcomes, reducing ambiguity. By providing specific instructions like 'Avoid making immediate promises about refunds or immediate shipment,' it prevents the model from generating unfeasible or premature solutions. The defined output format (`email response`) further structures the generation. This structured approach forces the model to think through the problem, leading to a more comprehensive, accurate, and actionable response that aligns with customer support best practices, reducing the need for costly re-prompts or corrections.

5%
Token Efficiency Gain
The optimized prompt explicitly defines the persona (customer support agent).
The optimized prompt clearly outlines the steps for crafting the response (Chain-of-Thought).
The optimized prompt includes specific constraints like 'Avoid making immediate promises'.

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