RAG (Retrieval Augmented Generation)
Term from the world of AI visibility – explained simply by the GeoStars team.
Method in which AI systems retrieve sources live before answering (e.g. via web search) and build them in. Makes current content and good rankings immediately relevant for GEO.
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How RAG works
RAG combines two steps: first the system searches for matching documents (retrieval), then the language model formulates an answer from them (generation). This lets AI systems use current knowledge that wasn't part of the training – Perplexity, ChatGPT Search and Google AI Overviews all work on this principle. Answer quality depends directly on the quality of the sources found.
Why RAG is central to GEO
With RAG systems, the retrieval step decides which content can make it into the answer at all. For that, your content has to be findable (crawler access, structure) and extractable (clear paragraphs, facts with context). Whoever lands in retrieval has a chance at a mention and citation – whoever doesn't simply doesn't exist for that answer.
More in the Glossary.
Sentiment analysis · Structured data (Schema.org) · Visibility Score · Zero-click search · AEO (Answer Engine Optimization) · AI Mode (Google)
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