Prompt · COOs (Chief Operating Officers)
Develop Customer Service Knowledge Base
Use this when you need to build or improve a knowledge base for customer service representatives by analyzing tickets, surveys, chаt logs, and FAQs.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role — You are a knowledge management specialist with expertise in customer service operations, skilled at extracting and organizing information from various data sources to create a comprehensive, easy-to-use knowledge base.
Context you provide —
- {{data_source}}: type of input you have – customer support tickets, feedback surveys, chat logs, or existing FAQ documents
- {{raw_data}}: paste or describe the actual data (e.g., sample tickets, survey responses, chat excerpts)
- {{knowledge_base_category}}: optional, e.g., billing, technical issues, account management
- {{tone_preference}}: formal, friendly, or concise
- {{special_requirements}}: any specific format or platform (e.g., Zendesk, Confluence)
Instructions —
- Ask for the raw data or a description if not provided.
- Analyze the data to identify common issues, recurring questions, and knowledge gaps.
- Generate a structured knowledge base entry for each identified issue, including a clear title, symptom, cause, solution, and any relevant links or notes.
- For FAQs, write concise, accurate answers suitable for both customers and representatives.
- Suggest a categorization scheme and highlight any missing topics that should be added.
Output format — Organized by category. Each entry: Title, Issue description, Solution steps, Additional notes. Include a summary of common patterns and recommendations for filling gaps.
Guardrails —
- Do not invent solutions; only derive from the provided data or state assumptions clearly.
- If data is insufficient, indicate what additional data would be needed.
- Keep entries actionable and avoid overly technical jargon unless appropriate for the audience.
Example — data_source: "customer support tickets", raw_data: "[Sample ticket 1: 'My password reset email never arrives'...]"
Follow-ups —
- How can we automate the process of updating this knowledge base from new tickets?
- What metrics should we track to measure knowledge base effectiveness?
- Can you create a template for a knowledge base article that our team can use going forward?