Generative Engine Optimization (GEO)

Generative Engine Optimization, or GEO, is the set of practices used to make a brand and its content visible, well understood and cited inside the answers produced by generative AI systems: ChatGPT, Google AI Overviews, Google AI Mode, Perplexity, Gemini, Claude and Microsoft Copilot.

Where traditional SEO optimizes a page to rank in a list of links, GEO optimizes content so a language model can extract a clear, accurate answer from it and choose to cite the source. The two disciplines share a technical foundation, crawlability, structured data, page speed, authoritative content, but GEO adds requirements specific to how language models read and select sources: clear and direct answers near the top of the page, well structured headings, verifiable facts, current information and a writing style a model can quote without needing to rephrase it.

GEO sits alongside related terms such as Answer Engine Optimization (AEO) and LLM Optimization (LLMO). In practice, most teams use GEO as the umbrella term for the entire discipline of AI search visibility, while AEO and LLMO describe more specific angles within it.

A SaaS company rewrites its comparison page with a concise, quotable definition near the top, adds an FAQ section answering common buyer questions, and marks up the page with FAQPage schema. Within a few weeks, ChatGPT and Perplexity start citing the page when users ask about the product category.

An ecommerce brand notices it is never mentioned when shoppers ask Google AI Overviews for the best product in its niche. After restructuring product pages with clear specifications, genuine customer feedback and structured data, the brand starts appearing in AI generated shopping summaries.

A B2B agency builds a glossary of industry terms, similar to this one, to become a citable source that AI engines reference when users ask for definitions related to the agency's field.