Definition

Retrieval-augmented generation is a method where a generative system retrieves external information before producing an answer.

Expanded definition

Retrieval-augmented generation, often shortened to RAG, can ground an answer in documents, web pages, databases, or other sources. For AEO/GEO, it reinforces why source quality and source clarity matter. The technical layer matters because AI systems need usable context, but technical access is not the same as good interpretation. Clear entities, current pages, consistent claims, and useful source context still determine whether the answer helps a buyer.

Why it matters

When systems retrieve sources before answering, the quality, freshness, and clarity of those sources can shape the output.

Example

A system retrieves a support article and a product page before summarizing whether a tool supports a specific integration.

Common mistake

Assuming retrieval makes every generated answer complete, current, or source-faithful.

Diagnostic question

If a system retrieved this page, would it find a clear, current, and buyer-useful answer?