Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is the architecture where an AI model retrieves current documents and grounds its answer in them, which is why AI search cites sources.
Retrieval-augmented generation (RAG) is the architecture behind AI search: the model retrieves relevant documents from an index at answer time and grounds its response in them, instead of relying only on training data. RAG is why AI engines can answer current questions and cite sources.
How it works
When a query arrives, the system searches an index, pulls the most relevant passages, and feeds them to the language model alongside the question. The model composes an answer constrained by those passages and attributes them as citations. What gets retrieved determines what gets said, which makes retrievability the foundation of AI visibility.
Why it matters
Understanding RAG explains the entire GEO playbook. Content must be crawlable to enter the index, structured to be retrieved for the right queries, and credible enough to be cited in the final answer. Every GEO tactic maps to one of those three stages. Learn more about our generative engine optimization.
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