Retrieval-Augmented Generation (RAG) Enhances AI Models with External Knowledge
Retrieval-Augmented Generation (RAG) is an AI architecture that integrates a retrieval system with a generative AI model to provide more accurate and contextually relevant responses. Unlike conventional large language models (LLMs) that rely solely on their pre-trained knowledge, RAG systems first retrieve pertinent information from an external knowledge source before generating a response. This external source can include various data types such as PDF documents, websites, internal policies, and databases. The process involves creating external data by chunking and embedding documents, retrieving relevant information based on a user's query, augmenting the LLM prompt with this retrieved context, and then generating a response. This method is particularly beneficial for applications requiring access to private, specialized, or frequently updated information, as it allows the AI to ground its answers in specific, current data.
