Retrieval-augmented generation (RAG) is an AI framework designed to enhance generative AI models by integrating them with external knowledge sources and retrieval mechanisms, resulting in more accurate and contextually relevant responses. RAG consists of two main components: a retrieval system that fetches pertinent information from an external knowledge base, such as vector embeddings stored in a vector database, and a generation system that creates responses based on this retrieved data. Originating from the quest to improve question-answering systems, RAG is a significant advancement that offers benefits including accuracy, trust, time and cost efficiency, and customization. It is employed in various applications like conversational chatbots, personalized recommendation systems, legal research, and financial analysis, providing up-to-date information and enhancing user engagement. The RAG model is characterized by its ability to combine the output of pre-trained large language models (LLMs) with retrieved information to produce final responses, and despite potential challenges, it remains a robust tool for improving the quality of LLM-generated outputs.