RAG (Retrieval-Augmented Generation)

Definition

An AI architecture that combines information retrieval from external sources with text generation by an LLM.

RAG (Retrieval-Augmented Generation) is an AI architecture that improves LLM responses by combining two steps: 1) retrieval of relevant information from external sources (web, databases), and 2) generation of a synthetic response by the LLM based on that information. This is the mechanism used by Perplexity, ChatGPT Search, and Bing Copilot. For GEO, understanding RAG is crucial because it determines how your content is selected and used to generate AI responses.

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Key Points

• Combines information retrieval and LLM generation
• Key mechanism behind Perplexity, ChatGPT Search, and Bing Copilot
• Understanding RAG helps optimize for GEO

Practical Examples

RAG in Perplexity

Perplexity retrieves the 10 most relevant pages for a query (retrieval), then its LLM synthesizes a response citing those sources (generation).

Internal RAG

A company sets up an internal RAG system that searches its documentation and generates personalized responses for its customer service.

Frequently Asked Questions

Why is RAG important for SEO?

RAG is the mechanism by which AI engines select and use your content. Understanding this process helps optimize your pages to be retrieved and cited in AI responses.

How do you optimize content for RAG?

Produce factual, well-structured, and easily extractable content. Standalone paragraphs with complete information are better retrieved by RAG systems than text requiring extensive context.

Related Terms

LLM (Large Language Model) Vector Search / Semantic Search GEO (Generative Engine Optimization)

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Last updated: 2026-02-07