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glossary/rag

What Is RAG (Retrieval-Augmented Generation)? | SEO AEO Specialist SEO & AEO Specialist

In short

RAG (Retrieval-Augmented Generation) is an AI architecture where a large language model retrieves relevant documents at query time and generates an answer grounded in them, enabling citations. Instead of relying only on what it memorized during training, the model pulls in fresh, specific sources before responding.

This retrieval step is why many answer engines can link to the pages they drew from. It also explains why your content's structure and clarity matter: if a system retrieves your page, it needs to lift a clean, self-contained passage to quote and cite. RAG connects directly to AEO and GEO, since being retrievable and quotable is exactly what gets you into AI-generated answers with a citation attached.

SEO AEO Specialist by Pyratz Corp helps you diagnose AND fix whether your content is easy to retrieve and cite, not just monitor it. Run a free audit of up to 50 pages (€0, no card).

For the full playbook, see AI Citations. Back to the SEO & AEO glossary.

01

FAQ

Why does RAG enable citations?

Because a RAG system retrieves specific documents before generating its answer, it knows exactly which sources informed the response. That link between retrieved content and generated text is what lets answer engines attribute and cite the pages they used, rather than producing an unsourced summary from training data alone.

How does RAG relate to AEO?

RAG is the mechanism behind many answer engines, so optimizing for it is central to AEO. If your content is easy to retrieve and offers clean, self-contained passages, a RAG-based engine is more likely to pull it in and cite it. Clear structure and relevance improve your odds of inclusion.

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