After implementing the stages of the RLHF pipeline, the focus shifts to assessing the resulting model's alignment and preparing it for practical use. This chapter provides guidance on evaluating models trained via human feedback, understanding their behavior, and navigating deployment considerations.

We will examine methods for measuring alignment, encompassing specific metrics, human evaluation protocols, and automated benchmarks. Additionally, you will study how to analyze model changes during RL tuning, perform safety assessments through techniques like red teaming, and understand the computational costs and scalability factors. The chapter concludes with practical aspects of deploying RLHF-tuned models.