What is GEO? The SEO vs AEO vs GEO difference gets asked often enough that it's worth answering plainly: search engine optimisation, answer engine optimisation and generative engine optimisation are frequently used as if they were the same discipline wearing three names. They aren't. Each targets a different surface, and building for one does not automatically deliver the others.
SEO: appearing in the results
Traditional SEO optimises for a ranking position on a search results page — crawlability, page authority, internal linking, technical performance and backlinks all feed one outcome: showing up, and showing up above the alternatives.
AEO: becoming the extracted answer
Answer engine optimisation targets a narrower prize: being the text lifted directly into a featured snippet, a People Also Ask box, or a search engine's AI-generated overview. It rewards a specific format — question-shaped headings, a complete answer in the first sentence or two, and structured data (FAQPage, HowTo) that tells the extraction system exactly what it's looking at.
GEO: being the source a model names
Generative engine optimisation is different again. How do LLMs choose sources to cite? When someone asks a language model to recommend a vendor, explain a regulation, or compare two approaches, the model is drawing on sources it has learned to trust and can verify — and it tends to cite material that is unique, rather than a restatement of what a hundred other pages already say.
That means GEO rewards a different kind of content investment: original data nobody else has published, named authorship with real credentials, and an entity that's corroborated across multiple independent sources — a company's own site, its LinkedIn presence, review platforms, and third-party mentions all telling a consistent story.
Why the distinction matters practically
A page can rank well and never get cited by an AI system, and a page can get cited without ranking particularly high — the mechanisms are genuinely separate. A content strategy built only around keyword targeting captures the first outcome and leaves the second to chance. Building for all three means treating structured data, original research and entity consistency as core requirements of the content programme, not optional extras layered on afterward.