Retrieval Augmented Generation uses text that is stored in a database to augment user prompts that are sent to a generative AI, like a large language model. The

Retrieval Augmented Generation uses text that is stored in a database to augment user prompts that are sent to a generative AI, like a large language model. The retrieval results are based on their similarities to the user input. The goal is to improve the output of the generative AI by providing more information in the input (user prompt + retrieval results.) For example, we can provide the LLM details from an internal knowledge base so it can generate responses that are specific to an organization rather than based on general information. It may also reduce errors and improve the relevancy of the model output, depending on the context.