What is RAG?
Introduction🚀
RAG is a popular method that improves accuracy and relevance by finding the right information from reliable sources and transforming it into useful answers.
Large Language Models are trained on a fixed dataset, which limits their ability to handle private or recent information. They can sometimes "hallucinate", providing incorrect yet believable answers. Fine-tuning can help but it is expensive and not ideal for retraining again and again on new data. The Retrieval-Augmented Generation (RAG) framework addresses this issue by using external documents to improve the LLM's responses through in-context learning. RAG ensures that the information provided by the LLM is not only contextually relevant but also accurate and up-to-date.
There are four main components in RAG: