paras_athina's comments

I agree and I have practical proof to say that. In an experiment we did at Athina, we made a cookbook for every RAG Technique out there and then evaluated it. We used different vector Db's, different embeddings and we saw different results. You should check out the Github Repo: https://github.com/athina-ai/rag-cookbooks

RAG is amazing. For all the RAG techniques that are being used by enterprises today, here is an amazing repo that provides Google Collab Links for each technique implementation along with an evaluation framework fully implemented. Check out: https://github.com/athina-ai/rag-cookbooks

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