Generative Engine Optimization (GEO) is the wider practice of getting your content, structure, and technical signals in order so AI systems can find you, read you correctly, and represent you accurately. AEO is the part of it that deals with citations.
GEO is the umbrella. It covers whether AI crawlers can reach your pages, how cleanly your content parses, whether your facts stay consistent wherever they appear, and whether you get cited when it counts. Answer optimization is the visible tip of that, and it only works because the groundwork underneath it holds.
People use the two terms interchangeably, and in conversation that's harmless. The line worth drawing: AEO is about the answer on the page, while GEO is about everything an AI system can access and knows about you.
The distinction bites the moment your answers are good but nothing happens. If a crawler can't reach the page, if the model can't corroborate your claim, or if it's working from a stale picture of your company, the citation never comes. GEO is the layer that makes AEO possible, the difference between tuning one page and being legible to AI systems as a whole.
In practice the work runs from the plumbing up: crawlability first (robots rules, llms.txt, pages that render for bots), then schema markup for structure, a consistent name and description so a knowledge graph forms a clean picture of you, the trust signals behind source authority, and finally the answer-level AEO on top. You might fix crawlability and publish an llms.txt file before writing a single citable line, because none of the answer work pays off if the engine can't retrieve the page to begin with.
Related terms
- Answer Engine Optimization (AEO): the citation-facing slice of GEO
- llms.txt: an emerging way to steer AI crawlers
- Semantic Search: how engines match meaning, which your content has to satisfy