The acronym behind why AEO works at all. Here’s RAG, explained for marketers, not engineers.
RAG, or retrieval-augmented generation, is the technique where an AI system retrieves relevant sources and uses them to generate its answer — rather than relying only on what it learned in training. For marketers, RAG is the reason AEO works: because the engine pulls in live sources to answer, your content can be one of those sources and get cited, even though it wasn’t part of the model’s original training. RAG is the bridge between your website and the AI’s answer.
You don’t need the engineering details, just the implication: when an engine uses RAG, being retrievable and clearly relevant puts your content in front of the model at the moment it answers.
A language model on its own answers from what it absorbed during training — a fixed snapshot that can be outdated and that doesn’t include your latest content. RAG adds a step: before answering, the system retrieves relevant, current sources from the web or a database, then generates its answer using them. So the answer combines the model’s language ability with fresh, specific, retrieved information — and your content can be part of that retrieved set. Engines like Perplexity lean heavily on this approach, which is why they cite live sources for nearly everything.
RAG is what makes your AEO efforts pay off in real time. Because the engine retrieves to answer, your current content can be cited the moment it’s relevant — you don’t have to wait to be absorbed into a future model. But it also means the basics are non-negotiable: if your content can’t be retrieved and read, RAG can’t use it, and you’re absent from the answer. Understanding RAG clarifies why retrievability, clarity, and trustworthiness are the foundation: they determine whether your content makes it into the retrieved set the answer is built from.
Retrieval-augmented generation — when an AI retrieves relevant live sources and uses them to generate its answer, instead of relying only on training data. It's why your content can be cited even though it wasn't in the model's training.
Because it lets your current content be cited in AI answers in real time. The engine retrieves to answer, so being retrievable and relevant puts you in front of the model at the moment it responds.
That your content can be retrieved and read. If it's blocked or unreadable, RAG can't use it and you're absent from the answer — which is why retrievability, clarity, and trustworthiness are foundational.
We test whether your content makes it into the sources AI retrieves to answer — the retrieval set that RAG builds every answer from.