AEO vs GEO

AEO and GEO get used interchangeably in most marketing content, which is close enough for casual conversation but wrong in a way that matters if you're deciding what to actually optimize for.

Answer Engine Optimization is the umbrella term. It covers any system that returns a direct answer instead of a list of links: a classic Google featured snippet, a voice assistant reading back one fact, an AI Overview, or a chatbot's response. The common thread is the format, a direct answer, not the underlying technology producing it.

Generative Engine Optimization is narrower. It specifically targets systems that generate an answer by synthesizing information from multiple sources, the way ChatGPT, Perplexity, Gemini, and Google's AI Overviews actually work. A GEO-optimized page isn't necessarily trying to be the single verbatim answer; it's trying to be one of the sources an AI system pulls from and credits when it builds a synthesized response.

The practical difference shows up in what you're optimizing for. Pure AEO tactics (a concise, extractable definition in the first sentence, FAQ schema, a question-form heading) are aimed at being lifted whole. GEO adds a layer on top: being cited alongside other sources, having claims specific and checkable enough that a model treats them as a reliable input, and building the kind of topical authority that gets a domain trusted as a source across many related questions, not just one exact-match query.

In practice, almost every tactic that helps with one helps with the other, which is exactly why the terms blur together. The distinction is worth knowing mainly so you don't assume optimizing for a single featured-snippet-style answer (AEO) automatically means you'll get cited inside a longer, multi-source ChatGPT response (GEO), or vice versa. They're correlated, not identical.