What is RAG (Retrieval-Augmented Generation)?

Definition

RAG is a technique that extends an LLM by allowing it to retrieve information in real time from external sources before generating its response. Perplexity and Bing Copilot operate on this principle. It's what enables an LLM to cite up-to-date sources.

"Pure" LLMs are constrained by their training cutoff date. RAG (Retrieval-Augmented Generation) solves this by adding a retrieval step: before generating a response, the system fetches relevant documents from a database or the web, then injects them into the LLM's context.

Impact on brand visibility

In a RAG system, the retrieved sources directly influence the generated response. Being present in the sources indexed by the engine (strong SEO authority, structured content, press mentions) increases the probability of being cited.

What this means for GEO

Optimizing for RAG means optimizing to be selected as a relevant source. Factual clarity, informational density, and consistency between title and content are determining signals.

No. ChatGPT in its standard version does not use RAG: it responds solely from its training data. Perplexity and Bing Copilot use RAG systematically. Google AI Overviews uses a hybrid approach combining training data and real-time web search.

The keys are factual clarity (verifiable and precise information), informational density (substantial content per paragraph), and consistency between title and content. Content that directly answers a question in its opening paragraphs has a stronger chance of being selected as a source.

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Related terms

GEO / AEO

LLM (Large Language Model)

An LLM (Large Language Model) is an artificial intelligence model trained on large amounts of text to generate, understand, and synthesize natural language. ChatGPT, Claude, Gemini, and Mistral are LLMs. They form the foundation of AI answer engines.

GEO / AEO

GEO (Generative Engine Optimization)

GEO refers to all practices aimed at optimizing a brand's visibility in responses generated by LLMs such as ChatGPT, Claude, or Perplexity. The goal is not only to drive direct traffic, but to be cited as a reliable reference in AI-generated answers.

GEO / AEO

Query fan-out

Query fan-out is the mechanism by which an LLM breaks down a complex question into multiple complementary sub-queries to interrogate different sources before synthesizing a final response. It is a core mechanism of RAG systems like Perplexity.