Retrieval-augmented generation

Title: Retrieval-augmented generation
Authors: 
Venue: Wikipedia
Year: unknown
Canonical: wiki:retrieval-augmented-generation
URL: https://en.wikipedia.org/wiki/Retrieval-augmented_generation
Collector: wikipedia
OA: true

ABSTRACT
Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information from external data sources. With RAG, LLMs first refer to a specified set of documents, then respond to user queries. These documents supplement information from the LLM's pre-existing training data. This allows LLMs to use domain-specific and/or updated information that is not available in the training data. For example, this enables LLM-based chatbots to access internal company data or generate responses based on authoritative sources. The technique was first proposed in 2020 and has since become a widely adopted approach in modern AI systems.