RAG-Retrieval-augmented generation (RAG)

RAG-Retrieval-augmented generation (RAG) is an AI framework that combines the capabilities of large language models (LLMs) with traditional information retrieval systems, such as databases. RAG optimizes the output of an LLM by allowing it to reference an authoritative knowledge base outside of its training data sources before generating a response. This process can help LLMs overcome limitations like parametric memory, access real-time data, and provide up-to-date responses.