Retrieval-Augmented Generation (RAG)
The core AI architecture where the model retrieves live web content before generating its answer — the mechanism that makes citation possible.
Plain Language Definition
RAG is the process where an AI search engine looks up your website first, then uses that fresh information to construct its answer — rather than relying solely on its training data. When Perplexity or ChatGPT Search looks something up before answering, it is using RAG. Your content only gets cited in a RAG-powered answer if it was retrieved, ranked high enough, and deemed trustworthy.
Technical Definition
Architectural framework where user prompts trigger orchestrated query pipelines that fetch grounding documents from indexed corpora at inference time, which are then injected into the LLM’s context window to ground generated responses in retrieved evidence.
Why This Matters for AI Search Visibility
Every major AI answer engine runs on RAG or a similar retrieval architecture. Understanding RAG explains why content structure, passage-level clarity, and schema markup matter so much — these are signals that determine whether your document gets pulled into the retrieval pipeline that feeds the model’s answer.