AI & GEO
Retrieval-Augmented Generation (RAG)
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What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is an AI architecture that combines a large language model with a retrieval system that fetches relevant documents from an external knowledge base or the live web before generating a response. Rather than relying solely on training data, a RAG system grounds its output in freshly retrieved content, reducing hallucinations and enabling up-to-date answers.
AI search products such as Perplexity and Google's AI Overviews are built on this retrieve-then-generate pattern. For SEO, this means that being indexed, crawlable, and authoritative directly influences whether a site's content is retrieved and cited. In practice, pages with factual, specific, well-structured information tend to be easier for a retrieval step to match and quote.
Why it matters for SEO
The retrieval step is where an AI search engine chooses which pages it can draw on and credit in its answer. Understanding RAG means recognizing that traditional crawlability and indexation remain prerequisites for AI visibility: content that is never retrieved can never be cited.
Example
A user asks an AI search tool "what is the ISA allowance this tax year?". The system first runs a search, retrieves a handful of pages (for example the official GOV.UK page and two finance blogs), passes the relevant passages to the language model, and the model writes an answer grounded in them with links back. A page that was never retrieved in the first step cannot be cited in the second.
Free tools for Retrieval-Augmented Generation (RAG)
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