Short and sweet:
GEO, or generative engine optimization; the practice of optimizing content so it gets referenced, summarized, or cited by generative AI systems, a term often used interchangeably with answer engine optimization.
Emphasizes the generative nature of the response, an AI synthesizing an answer, rather than the specific platform doing the generating.
GEO and AEO describe largely the same underlying work, structuring and sourcing content so an AI model chooses to include it when generating a response, but the two terms emerged from slightly different angles. GEO grew out of academic and technical research studying how generative AI models select and weight sources, while AEO is more commonly used in day-to-day marketing conversation to describe the practical goal of getting quoted by tools like AI Overviews or chatbots. In practice, teams rarely need to pick one term over the other, since the underlying tactics, clear structure, credible sourcing, and directly answering likely questions, are the same either way.
A research paper studying how large language models select sources might use the term GEO to describe the optimization techniques being tested.
A marketing agency pitching a new service could brand it as either GEO or AEO depending on which term its target audience searches for more often.
A content team debating how to frame a new internal initiative might settle on GEO specifically because it more clearly signals the connection to generative AI outputs.
