AI Customer Service Bot

Build an intelligent customer service bot that can answer questions from your product documentation and handle common support queries.

Difficulty: beginner
Duration: 45 minutes
Category: Customer Support
Prerequisites:
  • Completed "Building Your First Chatbot" tutorial
  • Completed "Creating Knowledge Bases with RAG" tutorial
  • Basic understanding of customer service workflows
What You'll Build:
  • A fully functional customer service chatbot
  • Knowledge base integration for product documentation
  • Automated response system for common queries
  • Escalation handling for complex issues

AI Customer Service Bot

Project Overview

Customer service is evolving with AI-powered solutions that can handle routine inquiries 24/7. In this project, you'll build a comprehensive customer service bot that can:

  • Answer questions from your product documentation using RAG
  • Handle common support queries with pre-trained responses
  • Escalate complex issues to human agents when needed
  • Track customer satisfaction and conversation quality

This project demonstrates real-world application of AI in customer support, combining knowledge retrieval with intelligent conversation management.

Setting Up Your Knowledge Base

Create a comprehensive knowledge base from your product documentation that will power your customer service bot's responses.

1 Upload Product Documentation

Upload your existing product documentation, FAQs, and support materials to Dify. The system will automatically process and chunk this content for optimal retrieval.

What to Upload:
- Product manuals and guides
- Frequently asked questions (FAQs)
- Troubleshooting documentation
- Policy documents (returns, warranties, etc.)
- Contact information and escalation procedures

Best Practices:
- Use clear, consistent formatting
- Include specific examples and scenarios
- Keep information current and accurate
- Organize content by topic or product line

Tips:
  • Upload documents in PDF, TXT, or DOCX format for best results
  • Break large documents into smaller, focused sections
  • Include keywords customers commonly use

Next Steps

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