The RAG Process: What is Retrieval-Augmented Generation?
Retrieval-augmented generation, or RAG, is a cutting-edge approach that boosts the output of large language models (LLMs) by integrating external contextual information through information retrieval. By combining LLMs with data from external sources like Wikipedia, RAG enhances the quality of responses, making them more accurate and context-aware. This advancement in natural language processing (NLP) allows for adaptive, contextually relevant output without requiring retraining, ensuring reliable and timely responses.