RAG and Grounding: Definition for AI Search

RAG (Retrieval-Augmented Generation) is an AI architecture in which a language model, before it generates an answer, retrieves a set of relevant documents or passages from an external knowledge base (the web, a database, a vector index), then builds its answer on top of those excerpts — a process called grounding, which ties the answer to verifiable sources rather than to the model's internal memory alone.

Why it matters for AI visibility

RAG is the core mechanism that decides whether your content can be cited by a generative engine. Understanding how it works explains directly which content gets picked — and which doesn't.

A RAG answer is built in two stages:

Stage 1 — Retrieval: the system finds the passages most relevant to the user's query. This retrieval can run through vector search (semantic similarity between the query and the indexed passages), classic keyword search, or a mix of both. This is the stage where your content either enters the candidate pool or it doesn't. Retrieval criteria reward semantic relevance, freshness, and technical accessibility (content visible in raw HTML, crawlers allowed).

Stage 2 — Augmented generation: the model receives the retrieved passages and uses them as context to write its answer. Out of the candidate passages, it keeps the ones that are most usable — self-contained, factual, sourced — to fold into the answer and cite as sources. This is the stage where structure, factual density, and attribution do their work.

Grounding refers specifically to the fact that the answer is "anchored" in the retrieved passages rather than generated freely from the model's memory. A well-grounded system cites its sources, is less prone to hallucination, and is easier to verify. That's why generative engines like Perplexity rely almost entirely on real-time RAG, constantly recrawling their active sources.

For a website, the takeaway is direct: to be cited, you first have to be retrievable (the technical side — robots.txt, server-side rendering, structured data), then selectable (the content side — self-contained passages, factual density, attributions).

A concrete example

A user asks Perplexity: "What are the security best practices for REST APIs?"

Retrieval stage: Perplexity queries its index and pulls back a dozen or so passages from a range of sources — OWASP documentation, technical blog posts, guides from specialist firms. The passages it retrieves are the ones whose content matches the query semantically and whose site is accessible to PerplexityBot.

Generation stage: out of the retrieved passages, Perplexity keeps the ones that are most usable for building a structured answer. A passage that opens with "The ten most common REST API vulnerabilities, according to the OWASP API Security Top 10 2023, are…" wins out over one that opens with "As we saw in the previous chapters, API security depends on several interdependent factors…" — because the first is self-contained and directly extractable, while the second needs context to make sense.

The result: the site whose content is structured for RAG extractability gets cited in the answer; the others don't, even when their content is just as good on the substance.

For the full picture, see the complete GEO guide.