What is RAGs?

Retrieval-Augmented Generation (RAG) is a technique for enhancing the knowledge of large language models (LLMs) with external, up-to-date information. It involves retrieving relevant documents or data from a knowledge base and incorporating that information into the LLM's prompt. This allows the model to generate more accurate, specific, and contextually appropriate responses, particularly when the model's pre-trained knowledge is insufficient or outdated. Common use cases include question answering, content creation, and chatbots where access to current and reliable information is critical.