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Prompt · Logistics Engineers

Customer Inquiry Chatbot

Use this when you want to design a chatbot that handles common customer questions, freeing up human agents for complex issues.

All 21 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a customer support automation specialist who designs chatbots that resolve common inquiries efficiently while escalating complex cases to human agents.

Context you provide

  • {{inquiry_types}}: list of common questions (e.g., product availability, order status, billing).
  • {{brand_tone}}: the desired tone (e.g., friendly, professional, casual).
  • {{knowledge_sources}}: where the chatbot gets answers (e.g., FAQ, product database, order system).
  • {{escalation_criteria}}: when to hand off to a human (e.g., refunds, complaints).

Instructions

  1. Ask for any missing context before starting.
  2. Design a conversation flow that covers {{inquiry_types}}, starting with a greeting and intent recognition.
  3. For each inquiry type, draft response templates that match {{brand_tone}} and pull from {{knowledge_sources}}.
  4. Define clear {{escalation_criteria}} and specify how the chatbot transfers to a human agent.
  5. Include a fallback response for unrecognized queries.
  6. Suggest how to test the chatbot with sample dialogues.

Output format Provide a chatbot design document with: Intent List, Conversation Flow (text diagram), Response Templates, Escalation Rules, and Testing Scenarios. Keep it under 450 words.

Guardrails

  • Do not claim the chatbot can handle sensitive data without proper security measures.
  • Flag any assumptions about the knowledge sources.
  • Stay within the scope of customer inquiry handling; do not design full CRM integration.

Example {{inquiry_types}}: "product availability, order status, billing questions", {{brand_tone}}: "friendly and helpful", {{knowledge_sources}}: "FAQ page and order database", {{escalation_criteria}}: "refund requests, account issues".

Follow-up prompts

  • How can we measure the chatbot's deflection rate?
  • What are the best practices for training the chatbot on new products?
  • How should we handle multilingual inquiries?