Generative Engine Optimization (GEO)
Generative engine optimization is the practice of shaping content so it is selected, quoted, and cited inside the answers produced by generative AI systems such as ChatGPT, Gemini, and Google AI Overviews, rather than only ranking as a blue link in a results page.
GEO is a near-synonym for answer engine optimization, and in everyday use the two terms describe the same goal: earning a place inside an AI-generated answer instead of a list of links. The slight distinction is one of emphasis. GEO foregrounds the generative model that writes the response, while AEO foregrounds the answer surface the user reads. The practical work overlaps almost entirely, which is why most teams treat them as one discipline with two labels.
Both reward the same kind of content, because generative systems and answer engines both retrieve passages, weigh how trustworthy they are, and stitch the strongest ones into a reply. That favours material that is extractable (clear claims a model can lift cleanly), corroborated (the same fact stated consistently across independent sources), and unambiguous about who is making the claim. Hidden text, keyword padding, or trying to inject brand names into a prompt do not survive this process and can read as manipulation.
The honest caveat is that GEO offers far less feedback than classic SEO: there are no reliable rank positions, citations vary between models and even between sessions, and a passage that gets quoted today may be dropped tomorrow. Treat it as influencing odds, not guaranteeing placement, and measure it through citation appearances and referral traffic rather than a single tracked rank.