An experimental approach to model comparison based on GPT-Load
AI model selection requires a scientific evaluation system, which is included in the AB testing program provided by GPT-Load:
- traffic diversion: Creation of experimental groups in the management interface, proportional allocation of requests to GPT-4/Gemini-Pro/Claude-2 (supports dynamic adjustment)
- data analysis: Built-in Prometheus metrics collection to compare key metrics such as response latency, error rate, token consumption, etc. across models
- Results replay: Batch test different models with the same input using the request recording feature (Redis must be enabled)
Procedure: 1) Add all the keys to be tested; 2) Create an experimental policy and set the triage rules; 3) View the monitoring panel via grafana. A content generation platform uses this method, and within two weeks, it determines the cost-effective advantage of Claude-2 in long text scenarios, saving about $12k in trial-and-error costs.
This answer comes from the articleGPT-Load: High Performance Model Agent Pooling and Key Management ToolThe
May not be reproduced without permission:AI productivity tools " How to quickly validate the difference in effectiveness of different grand models in real business?
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