Retrieval-Augmented Generation(RAG) is the process of making a large language model (LLM) to reference a custom knowledge base outside its training data before generating a response. As we all know, LLMs are trained on vast volumes of data with billions of parameters thereby possessing powerful natural language capabilities. RAG extends these capabilities of LLMs to specific domains or an organization’s internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.