Retrieval-Augmented Generation (RAG)
An AI architecture that combines real-time information retrieval from external sources with language model generation for more accurate responses.
Retrieval-Augmented Generation (RAG) is an AI system architecture that enhances language model outputs by first retrieving relevant documents or data from external knowledge sources, then using that retrieved context to generate more accurate and up-to-date responses.
RAG is the technical foundation behind AI search platforms like Perplexity and Google's AI Mode. When a user asks a question, the system first searches the web or a knowledge base for relevant content, then feeds that content to the language model as context for generating an answer.
For AEO, understanding RAG is important because it means AI visibility is not solely determined by training data. Brands can improve their AI search presence by creating content that is easily retrievable, clearly structured, and authoritative—since RAG systems actively select and prioritize sources during the retrieval phase.
Related Terms
Content Grounding
The process of anchoring AI-generated content in verified, factual source material to reduce hallucinations and improve accuracy.
AI Search Visibility
A measure of how often and how prominently a brand appears in AI-generated search results and answers.
LLM Optimization
The practice of adapting content and digital presence to be better understood, indexed, and referenced by large language models.
AEO Vision Content Team
Insights on AI search visibility, answer engine optimization, and brand discovery across ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode.
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