Retrieval-augmented generation (RAG)
An AI method that fetches relevant sources at answer time and generates a response grounded in them.
In depth
What it really means
RAG is how many AI answer tools stay current and accurate: instead of relying only on what the model memorized, they retrieve relevant documents at query time and generate the answer from those sources. That is why some answers come with citations.
For marketers, RAG is why your live, well-structured content can show up in AI answers. If your page is retrievable and clear, it can be pulled in and cited.
Best practices
- Keep content crawlable and retrievable.
- Structure pages so the relevant passage stands alone.
- Keep facts accurate, since retrieval surfaces them directly.
- Cover topics clearly enough to be the retrieved source.
- Use clean headings and chunks that map to questions.
FAQs
What is retrieval-augmented generation?
An AI method that fetches relevant sources at answer time and generates a response grounded in them, rather than relying only on training data.
Why does RAG matter for marketers?
Because your live, retrievable content can be pulled into an answer and cited, which is how you earn AI visibility.
Is that why AI answers have citations?
Often, yes. RAG retrieves real sources, which the tool can then cite in its answer.
How do I make content RAG-friendly?
Keep it crawlable, accurate, and structured so the relevant passage is clean and self-contained.
Does RAG reduce hallucinations?
It helps, because grounding answers in retrieved sources gives the model real facts to work from.
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