GEO
Also known as: Generative Engine Optimization, Generative search optimization
GEO (Generative Engine Optimization) is the set of content and technical practices that help a brand get mentioned accurately and cited as a source in answers generated by AI systems such as ChatGPT, Gemini, Perplexity and Google AI Overviews.
The term took off after a late-2023 research paper titled “GEO: Generative Engine Optimization”. In classic search, the goal is to rank high in a list of results. In a generative engine, the user often doesn’t see a list at all. They read one answer that a model has assembled from a handful of sources. GEO is about being part of that answer.
Why it matters
People now ask AI assistants the same questions they used to type into a search box: which lawyer, which clinic, which supplier. If your name isn’t in the answer, a good ranking on a results page doesn’t help that user much. GEO deals with questions like:
- Which prompts does the AI mention you in, and which does it give to a competitor?
- Is what it says about you accurate and current?
- Does it link to your site as a source?
GEO vs. SEO
GEO doesn’t replace SEO; it builds on it. A crawlable site, clearly written pages, structured data and mentions on trusted third-party sites help in both. The difference is in how success is measured. SEO tracks rankings and clicks. GEO tracks citation rate and share of voice in AI answers.
You will also see the same work called AEO or LLMO. The vocabulary hasn’t settled yet.
Example
Say a law firm never appears when someone asks ChatGPT for “an employment lawyer in Istanbul”. A GEO project starts by measuring that gap, then makes the firm easier for models to understand: pages that state its practice areas plainly, structured data, and consistent details across external sources. For a fuller introduction, read What is GEO?.