Fine-Tuning Challenges in AI Models

This chapter explores the complexities of customizing large language models through techniques such as reinforcement learning from human feedback (RLHF) and supervised fine-tuning. The discussion emphasizes the balance between aligning model behavior with human preferences while recognizing the inherent risks and limitations involved in fine-tuning efforts. Additionally, the chapter highlights the evolving roles within organizations and the need for improved collaboration and incident management frameworks as AI technologies become more integrated.