Skip to main content
RAG (Retrieval-Augmented Generation) is the technique of combining LLM text generation with real-time web retrieval. Instead of relying solely on training data, a RAG-enabled system searches the web for relevant sources, then uses those sources to generate a grounded response with citations.

​
Why it matters

RAG is how Perplexity and ChatGPT (in search mode) produce answers that reference current web content. For brands, RAG creates an opportunity that purely training-data-based models do not: recently published content can influence AI answers within days or weeks, rather than waiting months for a training data refresh. It also means that web content quality, crawlability, and freshness directly affect whether a brand is cited.

​
How it applies in practice

Cited tracks both RAG-enabled platforms (Perplexity, ChatGPT search mode) and training-data-primary platforms (Claude, Gemini). The brand visibility patterns differ between these two groups — RAG-enabled platforms respond faster to content changes but show more run-to-run variance because retrieval results change between sessions.