RAG

How does RAG decide whether to cite you?

Perplexity, ChatGPT browsing, Claude with web search, and Google AI Overviews all use retrieval-augmented generation. Rather than answering purely from memory, the engine first retrieves a set of documents from a live index, then writes its answer from those documents and cites them. That two-step shape is what makes GEO possible at all.

Your visibility in these engines is therefore a product of two things: whether your content is retrievable, meaning it is indexed, crawlable, and topically matched to the query; and whether, once retrieved, the model finds it useful and trustworthy enough to actually quote. You can be retrieved and still not cited if a competitor's page answers the question more cleanly.

Optimizing for both halves is the practical core of GEO: structure and technical health for retrieval, and clear, authoritative, directly-answering content for citation. GrowthManager builds pages with that two-step in mind and tracks which engines retrieve and cite you for each tracked query.

RAG also changes how freshness compounds. A traditional SEO page can rank for years off one strong publish. A RAG-targeted page benefits from continuous small updates: refreshed examples, current statistics, new comparison data points. Engines weight recency for many query types and the retrieval step penalizes pages whose timestamps drift months stale on time-sensitive topics. Plan for weekly content maintenance, not just initial publication, and watch citation share lift on the queries you keep current versus the ones you let coast.