What is RAG and How Does it Work?
RAG is the reason an AI assistant can answer a question about something it never memorized, and then tell you where the answer came from. It retrieves passages from external sources, passes them to the model, and asks it to write based on that evidence. For anyone doing GEO, this is the machinery that determines whether your content is evidence or invisible. This article explains what RAG is, why it matters, how it works from a GEO perspective, its benefits and limitations, and how to write content that RAG systems can retrieve and cite.
By Klara Pelhe
klarapelhe.rs/en/what-is-rag-and-how-does-it-work
What is RAG and How Does it Work?
TL;DR
- RAG stands for retrieval-augmented generation. The model looks things up before it answers instead of relying only on what it memorized during training.
- The pipeline is four moves: retrieve relevant passages, augment the prompt with them, generate an answer grounded in that text, and cite the sources.
- It exists to fix two problems: hallucinations and the knowledge cutoff. Grounding answers in retrieved text makes them checkable.
- Content is retrieved as chunks, not pages. Documents get split into smaller units, embedded, and matched by similarity, so a single strong section can be pulled without the rest of the page.
- Reranking is the second gate. Systems often retrieve many candidates, then rerank down to the few best before generation.
- For GEO, RAG is the whole game. If your chunk is not retrieved, it can’t be cited, no matter how good the page is.
- You win by writing self-contained, factual, clearly structured passages that survive being pulled out of context.
What Is Retrieval-Augmented Generation (RAG)?
Retrieval-augmented generation is a technique that lets a language model look things up in an external knowledge source before it answers, rather than relying only on what it learned during training.
When a question arrives, the system first retrieves the most relevant passages, then augments the prompt by placing those passages alongside the question, and finally generates an answer grounded in that retrieved text.
The simplest way to hold it in your head: RAG turns a closed-book exam into an open-book one. The model is still the reader, but now it has the right page in front of it.