In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), Retrieval-Augmented Generation (RAG) stands out as a groundbreaking framework designed to enhance the capabilities of large language models (LLMs). By leveraging external knowledge bases, RAG significantly improves the accuracy and relevance of generated content, ensuring that the