Testing and Evaluation of LLMs

This chapter explores the complexities of testing and evaluation for large language models (LLMs) in specific applications. It emphasizes the need to break down LLM interactions into manageable sub-agents to improve reliability while outlining effective testing strategies. Additionally, the discussion highlights the importance of understanding AI limitations, focusing on high ROI applications, and developing adaptable systems for future technological changes.