RAG Architecture

RAG, which stands for Retrieval Augmented Generative, is a cutting-edge natural language processing model that combines the strengths of both retrieval-based and generative models. This innovative approach allows RAG to effectively retrieve relevant information from a large knowledge base and then generate coherent and contextually appropriate responses. By integrating these two techniques, RAG is able to enhance the quality and accuracy of its outputs, making it a powerful tool for various applications such as question answering, conversational agents, and content generation.

Retrieval-based models excel at quickly finding relevant information from a vast pool of data, while generative models are skilled at producing human-like text. RAG leverages the strengths of both paradigms by first retrieving a set of candidate passages from a knowledge base using a retriever component and then generating a response based on the retrieved information using a generator component. This dual-stage process enables RAG to provide more informative and contextually relevant answers compared to traditional models.

Moreover, RAG incorporates advanced techniques such as dense retrieval, which enables it to efficiently search through large datasets to find the most relevant information. By combining the benefits of retrieval and generation, RAG can handle a wide range of tasks that require both understanding of context and the ability to generate coherent responses. This makes RAG a versatile and powerful tool in the field of natural language processing, with the potential to revolutionize how we interact with AI systems and access information.