What is RAG (retrieval augmented generation)?

Here's a neutral and interesting summary of the text in one paragraph: Retrieval augmented generation (RAG) is a method that gives large language models (LLMs) access to external information they weren't trained on, allowing them to access new and up-to-date data without needing retraining. This solves the problem of LLMs having a cutoff date for their training data, which can lead to limitations in providing accurate answers. RAG uses an LLM and a database containing additional information, with the database being searched for relevant context before appending it to the initial prompt. The benefits of RAG include offering additional context, reducing AI hallucinations, and making it easier to deploy and update, but also increase overall costs and response time. With proper implementation, RAG has the potential to become the core of most AI products in the near future.