The article discusses a new technique called Retrieval-Augmented Generation (RAG), which aims to improve the performance of large language models like GPT-3 by incorporating external knowledge sources. RAG models first use a retrieval component to find relevant information from a knowledge base, and then use a generation component to produce the final output based on the retrieved information and the input prompt. This approach addresses the limitation of traditional language models that can only generate text based on their training data, without access to external knowledge. RAG models have shown promising results in tasks like question-answering, where they can provide more accurate and informative responses by leveraging external knowledge sources. However, challenges remain, such as ensuring the retrieved information is reliable and relevant, and efficiently integrating the retrieval and generation components. The article highlights the potential of RAG models to enhance the capabilities of AI language models and enable more intelligent and knowledgeable interactions.