Language models like GPT-4 have advanced in generating code and drafting documents, yet face challenges in safety and ethical considerations. Reinforcement Learning from Human Feedback (RLHF) is a common method for aligning these models with human values, though it struggles with scalability due to its dependence on human-generated feedback. Reinforcement Learning from AI Feedback (RLAIF) presents a novel solution by using another AI model to provide feedback, guided by a constitution that ensures outputs align with ethical and safety standards. This approach retains the benefits of RLHF, such as generating helpful outputs, while improving scalability and reducing subjectivity. RLAIF leverages AI to automate feedback processes, enhancing efficiency and maintaining ethical alignment, and employs advanced prompting techniques to refine AI-generated responses. Research suggests RLAIF-trained models perform comparably to those trained with RLHF, particularly in tasks like text summarization, making it a scalable alternative to traditional methods. The core of RLAIF involves a Preference Model that adheres to constitutional principles, ensuring AI outputs are ethical and safe while minimizing reliance on human feedback, ultimately aiming for responsible AI governance.