This article discusses the importance of monitoring the internal reasoning of AI models, rather than just their outputs. It outlines methods for evaluating how effectively this reasoning can be supervised, especially as models become more complex. The authors call for collaborative efforts to enhance the reliability of this monitoring as AI systems scale.

This article outlines the LLM-as-judge evaluation method, which uses AI to assess the quality of AI outputs. It discusses its advantages, limitations, and offers best practices for effective implementation based on recent research and practical experiences.