Introduction Artificial Intelligence has come a long way from simple decision-theoretic systems to highly advanced models such as LLMs (Large Language Models), which show impressive performance in tasks such as generating new text, summarization, and response to questions. These models suffer from a serious limitation in the sense that they are highly dependent on pre-trained data and lack the capability to know anything in real-time or in domain knowledge. This problem was tackled by a new approach called Retrieval-Augmented Generation (RAG). This approach is an advancement in the skill set of Generative Artificial Intelligence in the sense that it makes use of the combination ofinformation retrieval and text or language generation. Understanding Retrieval-Augmented Generation Retrieval-Augmented Generation is a state-of-the-art AI architecture that seamlessly unifies two key components, which are a retrieval system and a generation model. In contrast to most AI models that ...