GEO (Generative Engine Optimization) is the discipline of structuring a site’s content so generative AI engines —ChatGPT, Claude, Gemini, Perplexity— can extract, summarize and cite it with confidence inside their answers.
How is it different from AEO?
In practice, most of the industry uses AEO and GEO as synonyms, and so far there’s no academic consensus drawing a sharp line between the two. If there’s a nuance: AEO focuses on showing up as a direct answer to a question, while GEO focuses on how content is read, summarized and cited within a generated answer, whether or not it’s the only source.
Why it’s relevant in the context of AI
The term comes from a 2023 academic paper (“GEO: Generative Engine Optimization”), which showed that applying specific optimization techniques can increase a site’s visibility inside AI-generated answers by up to 40%. Today tools like is-agentic.com exist to audit how “readable” a site is for an AI agent — content structure, trust pages, agent instructions, and more. Infrastructure plays a role too: layers like Cloudflare define which AI bots can access a site’s content in the first place.
How we work with this at PENDZIUCH
In the projects we build —websites, PWAs, apps and custom systems— we structure content thinking about how both a person and an AI agent will read it: clear headings, concrete facts, real trust pages, and agent instruction files when relevant. We applied this first to our own site — you can read the real case in GEO and AEO Are Not the Same Thing.