What is GEO?Generative Engine Optimization, explained

If you have asked ChatGPT for a tool, a supplier or a place to start, you have seen the shift. The engine writes one answer and names a few sources. Generative engine optimization, GEO, is the work of making your brand one of those named sources. It is also called AI search optimization, the name this blog uses, or AEO (answer engine optimization).

This guide defines the term, places it next to SEO, and shows where to begin.

#What it means

AI search optimization is the practice of getting your brand mentioned, cited and recommended inside the answers AI assistants write. The unit of visibility is a sentence in a composed answer, and the short list of sources attached to it.

Three outcomes sit underneath. Mentioned: the model says your name. Cited: the model links your page as a source for its claim. Recommended: the model puts you forward as a strong option, beyond a passing reference. The work aims at all three, because being named without being recommended is a weak result, and being recommended without a citation is fragile.

#Why AI search changes the game

Classic search returns a list. A person reads down it, forms a judgment and clicks. Ranking position is the prize because attention drops fast below the first few results.

An AI assistant collapses that. It reads many sources for you and returns one composed answer. With no list to scan, "rank one" loses its meaning. The question becomes binary first, then qualitative: are you in the answer at all, and if so, how are you framed?

That one change cascades. The content that wins is content a model can use and trust. The authority that counts is visible across many independent places. And the measurement that matters tracks presence in answers, more than position on a page.

#AI search and SEO, side by side

Google's position is plain: "optimizing for generative AI search is optimizing for the search experience", which for its own search means SEO. Its AI Overviews and AI Mode run on its core ranking and quality systems, retrieve pages from its index to ground each answer, and can run several related searches at once to compose it (Google). ChatGPT, Perplexity and Claude run their own search crawlers and indexes. Most brands need both disciplines, and the differences are worth stating plainly.

The overlap is real: both reward genuine expertise, clear structure and a site the crawlers can reach. The difference is where AI search effort goes: earning references beyond your domain, publishing first-hand content, and checking each engine's answers one by one.

#The first moves that put you in the answer

A handful of moves start the work, and most of them compound.

1. Let the search crawlers in. Each engine's search crawler needs access in robots.txt; training crawlers are a separate choice. How to get cited by AI assistants lists them engine by engine.
2. Publish what no other page says: your numbers, your tests, your customers' words. Google's guide says unique, useful content will likely matter more, over time, than anything else it suggests.
3. Answer the real question first, in plain language, then expand, with a clear heading for each topic.
4. Earn references where models already read: a roundup, a community thread, a comparison written by someone else.
5. Measure presence in answers. Track whether the engines name you, how often and how they frame you, and react when the picture changes (measuring AI search visibility).

Google also lists what you can skip for its search: llms.txt files and other "AI" files, which Google Search does not use; cutting pages into small chunks; rewriting your style for AI; and chasing inauthentic mentions. Other systems may read llms.txt, and keeping one does not affect Google Search.

#Where AI search goes from here

The AI answer layer is young, and the sources it leans on in many categories are still forming. That is the opening. Brands that show up early, answer clearly and earn genuine references can become the trusted source for their category.

Obi, an AI Search Employee, does that work for founders and GTM teams. It starts from your ideal customer profile and picks the buying questions those customers ask. On a paid pack, it reads the answers on ChatGPT, Gemini and Google AI Overviews every week, with Claude and Perplexity as options. It proposes the strategies for the gaps it finds, drafts the comparison pages, FAQ pages and articles with you in your AI assistant, from your data, and proposes Reddit replies and media pitches for you to post and send. You review and publish.