Reinforcement learning from human feedback (RLHF) is an advanced fine-tuning technique that aligns foundation models with human preferences, significantly impacting the usability and performance of AI systems like OpenAI's ChatGPT and Anthropic's Claude. It addresses the challenge in reinforcement learning of defining complex goals by using human feedback to guide model decisions, making it a cost-effective and scalable solution. RLHF improves model helpfulness, accuracy, and reduces biases, as demonstrated by models such as InstructGPT, which outperform their predecessors in truthfulness and toxicity benchmarks. The process involves collecting demonstration data for initial fine-tuning, gathering human feedback to train a reward model, and optimizing the model through reinforcement learning algorithms like Proximal Policy Optimization. RLHF enables a wide range of applications, including support agents, content generation, and sentiment detection, marking a significant advancement in AI development.