Retrieval Augmented Generation (RAG) is a technique used to improve the factuality and performance of large language models (LLMs). RAG combines a retrieval system with an LLM to provide context and factual information for generating output. The benefits of RAG include reducing hallucinations, accessing up-to-date information, and improving data security.
Table of contents
What is Retrieval Augmented Generation?From the Origins of RAG to Modern UsagePractical Tips for RAG ApplicationsClosing ThoughtsSort: