Generative Engine Optimization, or GEO, is the practice of optimizing published content for citation in AI-generated answers. It is used interchangeably with Answer Engine Optimization, or AEO. Both terms describe the same discipline: structuring content so that AI systems such as ChatGPT, Perplexity, Claude, and Google AI Overviews can retrieve, extract, and cite it in response to relevant queries.
The term GEO emphasizes the generative nature of AI answer engines, which produce synthesized responses rather than returning ranked lists of results. The term AEO emphasizes the answer engine as a specific category of AI system that has replaced traditional search for a growing share of professional research queries. The practical requirements of optimizing for either are identical.
For executives, the GEO or AEO framework provides a useful lens for understanding why traditional SEO and social media strategies are insufficient for AI visibility. Generative engines do not rank pages. They cite sources in synthesized answers. The content characteristics that drive citation are different from those that drive traditional search rankings.
The core requirements remain: named authorship with schema markup, long-form structured content on indexed domains, topical consistency, cross-domain attribution, and sustained publishing over 12 to 18 months. These requirements apply regardless of which terminology is used.
The practical starting point for executives is the same: build an indexed publishing record that gives AI systems enough attributed, structured, topically consistent content to cite with confidence.