RAG is an architecture that retrieves relevant source material before generating an answer and puts it into the language model's context. The source can be the web, documentation or an internal database.
What is RAG?
The system first converts the query and the documents into a form suited for search, picks the relevant parts and passes them to the model along with the prompt. The model then writes the answer from the context it was given.
RAG can make answers more current and cut some errors, but it doesn't guarantee the truth. Retrieval can pick the wrong source, and the model can misread the material.
Why RAG matters for SEO
Web answer engines can use a similar principle to find source material. Accessible, clear content has a better chance of being found, though the exact architecture of a product may not be public.
Citations aren't automatically part of every RAG system. The product has to keep the source links and show them. The model itself doesn't guarantee that transparency.
How to prepare content
Use descriptive headings, direct answers, clear definitions and continuous passages. Add the date, the author, sources and your own proof wherever a claim depends on them.
Don't chop content into isolated sentences just for the machine. A passage has to stay clear to a person and include the conditions that keep a claim from being pulled out of context.