Background to the issue
Traditional customer service systems rely on manually written dialog, which results in low response rates and lagging updates when faced with complex questions. With RAG technology, the latest knowledge base can be dynamically combined to generate accurate answers.
program of implementation
- Knowledge base building: Import data such as product documentation, historical work orders, FAQs, etc. to ensure coverage of all business scenarios
- Question and Answer Engine Configuration: Adjust search parameters (e.g. top_k=3) and prompt templates to control the precision of responses
- referencing mechanism: Enable the answer traceability function, each answer is automatically labeled with the location of the reference document
- Continuous optimization: Analyze high-frequency errors through conversation logs and target knowledge base content for replenishment
Effectiveness Verification
After implementation, it is recommended to set up an AB test to compare the resolution rate of traditional discourse with the RAG system. Typical customer cases show that the accuracy rate can be improved by more than 40%.
This answer comes from the articleRAG Web UI: Building an Intelligent Documentation Q&A System and Simply Building a Private Web-Side Knowledge BaseThe
May not be reproduced without permission:AI productivity tools " How to Improve Response Accuracy of Customer Service System with RAG Web UI?
