One fascinating method in the fast-developing field of artificial intelligence that improves the capabilities of large language models (LLMs) is Retrieval Augmented Generation (RAG). This method yields more accurate and contextually appropriate responses by enabling AI to access and use fresh or recent data that is not part of its training set. This post will go over some of the main ideas behind RAG and explain how important tools like vector databases and embeddings are.
What Is Retrieval Augmented Generation (RAG)?
An AI approach called Retrieval Augmented generating (RAG) combines generating capabilities with retrieval techniques. In contrast to conventional LLMs, which only use prior knowledge, RAG systems are able to retrieve current data from outside sources. Because of this, they are especially helpful for applications that need up-to-date and thorough data, such as tailored recommendations, real-time question answering, and news summaries.