Retrieval-Augmented Generation (RAG) is a technique that combines retrieval-based methods with generative language models to improve the accuracy and relevance of generated responses. In RAG, the model first retrieves relevant information from an external knowledge source (such as a database or document repository) and then uses this information to generate a more informed and contextually accurate response. This approach is particularly useful when the language model alone may not have sufficient knowledge or context to generate high-quality answers, especially for specialized or up-to-date queries.