What is Retrieval-augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is indeed a grounding technique that enhances prompts with relevant contextual data. The fundamental concept of RAG is to combine the strengths of retrieval-based methods and generative models. This approach works by retrieving information from a large corpus of data and then using that information to provide a more informed and relevant response to user queries.

In practice, RAG improves the quality of generated content by grounding the responses in actual data, thus allowing for greater accuracy, relevancy, and contextual appropriateness. It effectively bridges the gap between raw machine-generated content and real-world information, leading users to receive responses that are not only coherent but also data-driven.

This approach stands in contrast to methods that might solely generate responses without context or rely only on historical data. By augmenting the generative process with relevant information, RAG enhances the capabilities of language models, making them more effective for real-world applications.