## Tags - Part of: [[Large language model]], [[Artificial Intelligence]], [[Machine learning]] - Related: - Includes: - Additional: ## Technical summaries - Retrieval augmented generation (RAG) is a type of generative [[artificial intelligence]] that has information retrieval capabilities. It modifies interactions with a [[large language model]] (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information in preference to information drawn from its own vast, static training data. This allows LLMs to use domain-specific and/or updated information. Use cases include providing chatbot access to internal company data, or giving factual information only from an authoritative source. ## Main resources - [Retrieval-augmented generation - Wikipedia](https://en.wikipedia.org/wiki/Retrieval-augmented_generation) <iframe src="https://en.wikipedia.org/wiki/Retrieval-augmented_generation" allow="fullscreen" allowfullscreen="" style="height:100%;width:100%; aspect-ratio: 16 / 5; "></iframe> ## Landscapes - [\[2312.10997\] Retrieval-Augmented Generation for Large Language Models: A Survey](https://arxiv.org/abs/2312.10997) - [Welcome to GraphRAG](https://microsoft.github.io/graphrag/) - [\[2402.19473\] Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://arxiv.org/abs/2402.19473)