Generative Engine Optimization (GEO) is the term coined inside the SEO industry for the same shift Answer Engine Optimization describes from a different angle: large language models now sit between your buyers and your site, summarizing and recommending instead of linking. GEO is the work of getting your brand to surface inside those generated answers.
GEO vs AEO
The two terms describe overlapping problems with different lineages. AEO comes from the answer-engine framing (ChatGPT, Perplexity, You.com). GEO comes from the SEO research community, popularized by the Princeton paper on Generative Engine Optimization and adopted by Search Engine Land. The metrics, the techniques, and the platforms are the same. Pick whichever vocabulary matches the team you are talking to.
What GEO research found
The original GEO paper showed that specific edits, citations from authoritative sources, quoting numbers, adding direct quotes, and structured statistics, increased visibility in generative answers by up to 40 percent on average. Those findings hold up in real-world tests on ChatGPT and Perplexity. The lesson is that AI assistants are not opaque: their preference for citation-rich, evidence-dense pages can be measured and improved against.
Where GEO becomes operational
GEO becomes a practical programme when you can run the same buyer questions before and after a content edit and observe whether your mention rate moves. That requires sampling the same prompt many times per model, because LLM answers vary across runs. A one-off check is not GEO. Continuous measurement is.