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. 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. If you want to learn more about it along with a code implementation of every technique, you should check this repo out: https://github.com/athina-ai/rag-cookbooks