RAG (Retrieval-Augmented Generation)
Also known as: retrieval-augmented generation.
In plain English
RAG addresses two LLM weaknesses: knowledge cutoff (models don't know about recent events past their training date) and hallucination (models invent facts when they don't know). By feeding the model relevant documents at inference time, RAG keeps the answer grounded in real text and adds citation transparency. Most enterprise AI deployments in 2026 use some form of RAG — typically with a vector database holding internal documentation that the LLM consults per query.
Example
A customer support chatbot uses RAG: when a customer asks 'how do I reset my password', the system retrieves the relevant help article from a vector database and feeds it to the LLM. The model answers using that specific article, with citations, instead of hallucinating a generic answer.
RAG (Retrieval-Augmented Generation) in Vincony
Vincony's AI Search uses RAG under the hood — Perplexity Sonar Pro retrieves live web sources, then the LLM synthesizes them into a cited answer. The Knowledge Graph adds RAG over your own uploaded documents.
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