How do you evaluate LLM performance over time?

Evaluating LLM performance over time involves tracking metrics such as accuracy, relevance, and user satisfaction across different versions of the model. It is important to establish a baseline during initial deployment and then implement a continuous monitoring system to assess the model's outputs regularly. Techniques like A/B testing can be employed to compare different versions or configurations, while user feedback can highlight shifts in performance perception. Statistical methods like time series analysis can help identify trends, and data collection should include various contexts to ensure a comprehensive understanding of performance evolution.