Large language models (LLMs) have significantly advanced the field of natural language processing by generating human-like text and performing complex language tasks. However, traditional training methods often lack the nuanced understanding of human preferences and ethical considerations. This chapter examines the integration of human feedback into the fine-tuning of LLMs to enhance their accuracy, reliability, and alignment with human values. Foundational concepts, techniques such as reinforcement learning from human feedback (RLHF) and active learning, and the design of effective feedback mechanisms are discussed. Additionally, case studies are presented, challenges like scalability and bias are analyzed, and future directions in human–AI collaboration are explored. Integrating human insights enables LLMs to achieve improved performance and contributes to the development of ethical and human-centered AI systems. Throughout, we adopt a human-centered AI (HCAI) lens to make explicit where humans participate in data creation, preference modeling, evaluation, and governance.