Customer Service Scene Special Optimization
The following optimization strategies are recommended for the special needs of customer service scenarios:
- Area fine-tuning: Add customer service discourse templates (e.g., return and exchange process, complaint handling, etc.) to the training data, with a recommended ratio of ≥ 301 TP3T. can be passed
./make_dataset/add_template.pyscript injection - Multi-Round Dialogue Optimization: Enable dialog status tracking, set in api_service.py
enable_memory=trueand configure up to 7 rounds of contextual memory - emergency transfer to a human being: add keyword trigger rules (e.g. "switch to manual", "complaint", etc.) on the AstrBot configuration page to automatically switch to a manual seat.
Performance metrics should focus on 1) Response latency ≤ 2 seconds 2) Intent Recognition Accuracy ≥ 90% 3) Customer Satisfaction CSAT ≥ 4.5. can be adopted by thetest_model.py --scenario=customer_serviceConducting specialized tests
This answer comes from the articleWeClone: training digital doppelgangers with WeChat chats and voicesThe
May not be reproduced without permission:AI productivity tools " How to optimize the effectiveness of WeClone's autoresponder in customer service scenarios?
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