Reinforcement Learning from Human Feedback (RLHF) bridges the gap between raw model behavior and human-aligned performance by integrating human judgments into the training loop. Expert annotators provide ranked or scored feedback on generated outputs, guiding iterative reinforcement updates to shape model responses. This expert-verified pipeline ensures AI systems learn preferences, reduce harmful or erroneous content, and follow desired instructions more reliably. Scalable RLHF workflows combine diverse feedback sources, rigorous evaluation, and adjustable reward models, fostering improved alignment across domains—from conversational agents to creative generators. Ultimately, RLHF empowers organizations to deploy safer, more relevant, and ethically grounded AI solutions that resonate with human values.