Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.

Retrieval augmented generation (RAG) is an architecture for optimizing the performance of an artificial intelligence (AI) model by connecting it with external knowledge bases. RAG helps large language models (LLMs) deliver more relevant responses at a higher quality.

RAG (Retrieval-Augmented Generation) is an AI framework that enhances AI responses by integrating real-time data retrieval with language generation models.

In a nutshell, Retrieval Augmented Generation (RAG) optimizes the outputs of Large Language Models (LLMs) by cross-referencing external knowledge bases. This is done without the need for retraining these models, which makes RAG a cost-effective and efficient approach to enhancing AI solutions.

Retrieval-augmented generation (RAG) represents a groundbreaking advancement in artificial intelligence that combines traditional information retrieval systems with large language models (LLMs).