In the world of GenAI, you’ll often come across the term RAG (Retrieval augmented Generation). Essentially, RAG is about giving additional relevant information (context) to large language models (LLMs) along with a query to help them generate better and relevant responses. Setting up a basic RAG system isn’t too complicated, but it often falls short in delivering highly accurate responses. One of