What Is GEO? Generative Engine Optimization, Explained
Somewhere in the last two years, a chunk of "googling" quietly became "asking ChatGPT." When people ask a chatbot for tool recommendations, comparisons, or explanations, the chatbot names a handful of brands. If yours isn't one of them, you're invisible in that conversation — no matter how good your SEO is.
GEO — Generative Engine Optimization — is the practice of making your site accessible, understandable, and citable to AI engines. That's the whole definition. The rest of this article is about what that actually means in practice, minus the hype.
How AI answers are built
When ChatGPT, Claude, or Perplexity answers a question, two things feed the result:
- Training data — what the model learned about the world and which brands are associated with which topics. You can't directly edit this.
- Retrieval — for questions that benefit from fresh sources, the engine fetches from the live web (search indexes, its own crawler, or both) and uses what it finds to shape the answer.
Both paths share one property: garbage in, garbage out. If AI crawlers can't fetch your site, or fetch it and find 300 characters of readable text inside 4MB of JavaScript, you don't exist to the model. GEO starts with being legible.
GEO vs SEO: what stays the same
A lot, actually. The fundamentals transfer:
- Say what you do, in plain words. Models match concepts to phrases. A homepage that never literally states "we build invoicing software for freelancers" won't be retrieved for that need.
- Get mentioned where engines already read. Directories, comparison posts, "alternatives to X" lists, Reddit threads. Mentions in training and retrieval corpora are how brands get into answers.
- Structure content as direct answers. A page that poses the question in a heading and answers it in the first sentence is easier to quote than a 2,000-word narrative.
What changes: rankings become mentions. In classic SEO you fought for position 3 instead of position 8. In AI answers there is no position — your brand is either named or it isn't. That binary is why a small site with one crisp sentence can beat a big brand with a vague one.
What AI engines can actually see
Three gates decide whether an engine can even consider your site:
- Crawl access.
robots.txtrules for GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and CCBot. Blocking these — sometimes accidentally, via an inherited template or a CDN bot-protection default — is the single most common self-inflicted wound we see. - Plain text. Engines largely read HTML as text, not pixels. If your value proposition is rendered by client-side JavaScript after a 5-second spinner, models may never see it.
- Structure. One H1 that states the topic, sequential headings, schema.org for hard facts. Structure is how a model figures out what's the claim and what's the nav bar.
Where llms.txt fits
llms.txt is a community-proposed spec: a Markdown file at your site root that describes what the site is and lists its key pages. It is not an official web standard, and engine support varies — we cover the honest details in our llms.txt guide. The short version: it costs ten minutes, it standardizes the "about" information engines otherwise have to guess, and the exercise of writing it is useful on its own.
What you can do this week
- Run a free AI visibility check — see your crawl access, llms.txt, structure, and whether a model mentions you, in one report.
- Fix whatever is mechanically broken: unblock AI bots you want cited, fix heading hierarchy, put the value proposition in plain HTML text.
- Write down what your site is, in one sentence, and put that sentence on the homepage.
- Get listed in 2–3 places engines already read for your category.
- Re-run the check in a week and see what moved.
That's GEO. Not magic — legibility, access, and mentions. The tools to check all three are free; the fixing is mostly an afternoon.