Retrieval and RAG

What is RAG?

Retrieval-Augmented Generation

RAG is an architecture in which a system retrieves relevant documents or passages and adds them to the context before generating an answer. It enables the use of external, recent or private information.

What RAG means in GEO

In a RAG pipeline, publishing is not enough. A page must be discoverable, enter the candidate set and survive reranking.

Why it matters

Optimize for the stages before generation: discovery, retrieval, reranking and passage clarity.

What to check in practice

Identify the stage where the source disappears: access, initial retrieval, reranking, generator context or citation selection.

How it connects to other concepts

RAG belongs to retrieval and rag. It is best analyzed alongside related terms because AI visibility depends on several stages and signals rather than one isolated optimization.

Common mistake

Blaming final copy when the document never reached the model that writes the answer.

Turn these concepts into metrics

Bee LLM tracks mentions, citations, position, sentiment and competitors across major AI engines.

Start free