analysis of current situation
The traditional customer service system faces the problems of fragmented answers and lagging knowledge update, and the maintenance cost of manually labeling the Q&A pairs is extremely high.Deep Searcher's natural language understanding capability can break through this bottleneck.
Implementation pathway
- Knowledge base building phase::
- Import of product manuals, historical work orders, etc. into vector database
- utilizationdeepsearcher.offline_loadingModule batch processing PDF/Word documents - Model tuning phase::
- Configure large model parameters such as GPT-4o in example1.py
- Fine-tuning Prompt Templates with a Few Samples - Systems integration phase::
- Development of APIs to interface with existing customer service systems
- Setting Confidence Thresholds to Enable Human-Machine Collaboration
Key Enhancement Points
1. Multi-round dialog capability supports contextualization
2. Automatically learn new knowledge from updated documentation
3. Answers may be accompanied by snippets of source documents for manual review
typical case
A fintech company went live:
- Increase in first-time resolution rate by 281 TP3T
- Reduced training cycle 60%
This answer comes from the articleDeep Searcher: Efficient Retrieval of Enterprise Private Documents and Intelligent Q&AThe
May not be reproduced without permission:AI productivity tools " How to apply Deep Searcher in customer service system to realize intelligent Q&A accuracy improvement?
