Cost Control Solutions for Smart Customer Service Scenarios
Policy configuration with LlamaFarm can effectively reduce AI customer service operating costs:
- Hierarchical Response Strategy: Configure the main model in strategies.yaml to use gpt-3.5-turbo, switching to gpt-4 for complex problems only
- Cache High Frequency Q&A: Enable the -use-cache parameter to cache historical responses to reduce API calls
- Local knowledge base preferred: Set the -rag-first parameter to retrieve the knowledge base before invoking the model
Typical Configuration Example:
- customer_support policy:
- primary: gpt-3.5-turbo
- fallback: claude-haiku
- temperature: 0.7 # Appropriate increase in creativity
Monitoring suggestion: periodically run uv run python models/cli.py audit -days 30 to generate usage reports
This answer comes from the articleLlamaFarm: a development framework for rapid local deployment of AI models and applicationsThe
May not be reproduced without permission:AI productivity tools " How to achieve cost optimization of AI models in customer support scenarios?
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