Retrieval-Augmented Generation (RAG) is an AI technique used in natural language processing that combines the power of both retrieval-based models and generative models. RAG is a framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and to give users insight into LLMs. RAG works by integrating retrieval-based techniques with generative-based AI models. The main idea behind RAG is to combine LLMs with a separate store of content outside of the language model containing sourced and up-to-date information for the LLM to consult before generating a response for its users. In the RAG framework, the language model identifies relevant information in an external dataset after computing the embeddings of a user’s query. The LLM then performs a similarity search on the prompt and the external dataset, before fine-tuning the user’s prompt using the relevant information it retrieved. Only then is the prompt sent to the LLM to generate an output for the user. RAG allows language models to bypass retraining, enabling access to the latest information for generating reliable outputs via retrieval-based generation. RAG can be fine-tuned and its internal knowledge can be modified in an efficient manner and without needing retraining of the entire model.