What is the RAG technique for LLM?

Retrieval Augmented Generation (RAG) is an advanced technique designed to enhance the performance of large language models (LLMs) by integrating external knowledge sources during the text generation process. This method addresses common challenges faced by LLMs, such as factual consistency and the tendency to "hallucinate" information.

RAG represents a significant advancement in leveraging LLMs for knowledge-intensive tasks, ensuring that generated content is both relevant and reliable by integrating real-time information retrieval into the generation process.