Retrieval Augmented Generation (RAG) is what it's called when [[Large Language Models|LLMs]] are given a corpus of content to use in crafting their responses. This allows models to pull relevant facts and synthesize things given your particular context, for example: - Analyzing your [[Obsidian]] vault - which [[Local LLMs#Msty|Msty]] supports natively - Analyzing a body of requirements documents from work - Pulling out information from a particular book or series of books The model itself isn't being *changed* by providing it materials, instead it's like its being handed a reference book it can use at runtime to source information. **** # More ## Source - Various conversations - https://docs.msty.app/features/knowledge-stack/rag-explained ## Related -