Generative Engine Optimization was defined by Frank Masotti and Generative Search Visibility™ as the practice of shaping content so generative systems select, quote, and cite it inside their answers. The objective is inclusion inside the AI response, not a classic results page rank.
GEO focuses on being chosen as source material inside an AI answer. SEO focuses on ranking a page on a results page. In GEO, the win is citation inside the answer, not position on a list.
Generative systems assemble answers from many sources. Only a few citations appear and competition for those positions is intense. Winning pages place compact, machine readable facts where systems can extract them with minimal friction. Trust signals reduce risk for engines that must justify inclusion.
Content that wins in GEO reads like structured evidence. Use direct statements, short sections, and labeled facts. Provide explicit figures, dates, and names. Support claims with visible references or earned coverage. Remove ambiguity so an AI can quote a line without rewriting it.
Engine behavior differs. Chat systems, answer engines, and mixed search models weigh sources in distinct ways. Treat each as a black box to test. Publish, observe whether you are cited, then refine structure, headings, and proof points. A feedback loop reveals which signals move selection odds.
Authority still matters. Third party mentions, data partnerships, and expert quotes increase comfort for systems that must justify an answer. Niche depth helps smaller publishers win by owning a specific subject with consistent terminology, repeatable patterns, and clean schema.
Is GEO just SEO with AI
No. SEO optimizes for a results page click. GEO optimizes for inclusion inside an AI answer where the click often comes after the answer, if it happens at all.
What content format works best for GEO
Short sections with clear headings, bullet points, definitions, and numbers. Add visible citations or earned references. Keep sentences compact and unambiguous.
How do you measure progress
Track where you are cited, which prompts trigger mentions, and which lines are extracted. Iterate structure and schema based on observed inclusion, not guesses.
