In the world of Artificial Intelligence, Retrieval-Augmented Generation (RAG) is a groundbreaking innovation that combines retrieval and generative models to produce human-like responses. By accessing external information, RAG enhances accuracy, mitigates bias, and offers vast applications in chatbots, search engines, and content creation. Ongoing research focuses on optimizing the retrieval process and addressing potential biases.