Retrieval-augmented generation represents an innovative approach to language generation that incorporates information retrieval techniques to enhance the quality and relevance of generated text. In traditional language generation, models generate text solely based on the input prompt or context, often relying on learned representations and generative algorithms. In contrast, retrieval-augmented generation involves retrieving relevant documents or passages from a large corpus of text, such as a knowledge base or an external dataset, and incorporating this retrieved information into the text generation process. By leveraging external knowledge and context, retrieval-augmented generation enables language models to produce more coherent, informative, and contextually relevant text outputs.
