Retrieval_ Augmented Generation (RAG)

RAG (Retrieval-Augmented Generation) is an AI framework that combines the strengths of traditional information retrieval systems (such as search and databases) with the capabilities of generative? large language models. (LLMs). By combining your data and world knowledge with LLM language skills,?grounded generation?is more accurate, up-to-date, and relevant to your specific needs.

RAGs operate with a few main steps to help enhance generative AI outputs:?

·???????? Retrieval and pre-processing:?RAGs leverage powerful search algorithms to query external data, such as web pages, knowledge bases, and databases. Once retrieved, the relevant information undergoes pre-processing, including tokenization, stemming, and removal of stop words.

·???????? Grounded generation:?The pre-processed retrieved information is then seamlessly incorporated into the pre-trained LLM. This integration enhances the LLM's context, providing it with a more comprehensive understanding of the topic. This augmented context enables the LLM to generate more precise, informative, and engaging responses.?

RAG offers several advantages augmenting traditional methods of text generation, especially when dealing with factual information or data-driven responses.?