For two decades, optimizing for search was simple: secure a place in the pack of ten. This notion is rapidly changing. An ever-growing percentage of queries are now satisfied within AI-powered chatbots, including ChatGPT, Perplexity, Google's AI Overviews, Claude, and Copilot, where the model will peruse several sources and generate the response. Being selected as one of these sources is a practice that is developing quickly and is called generative engine optimization.
Recall how a search concluded in the past. You would type in a query, sift through a list of links, and pick one. The website received traffic; the battle was centered around ranking higher, crafting an optimal title, or earning a citation, as the key was getting the click. Now, imagine typing in that query within ChatGPT or Perplexity. Instead of being offered a list of search results, the query would prompt the model to read from several of the most useful resources in a list of two to seven domains, and it would craft a written answer for you. Your site either becomes one of those resources, or the user will never see your site. There is no "next page" of search results you can work your way into.
GEO arose to capitalize on that change. The goal is not to "win" a rank on a list but rather to become a citation within an answer. What prompts an answer engine to cite your resource is somewhat distinct from what generates a click on a traditional search result list. An answer engine does not simply look at a list of search results. Instead, it scans your site to determine whether you have clearly stated enough of what you are doing so that it will be confident in repeating your content back to the user, linking you in the process. Optimizing for answer engines is the purpose of GEO.
To rephrase in nontechnical terms, GEO is optimizing your pages' structure so answer engines can crawl them, understand them, and choose to cite them as resources. You may also hear it called answer engine optimization, or AEO; these terms are used in a loose fashion and do not matter much for the work ahead. What is relevant is the goal that has changed: rather than only optimizing to be your audience's preferred choice when searching, you need to now also optimize for AI-generated answers.
The good news is that GEO overlaps significantly with traditional SEO, and a lot of the practices have existed already. It is difficult for an answer engine to cite something from which it cannot crawl or understand. This means that the fundamentals of GEO are the same SEO practices you know: having accessible URLs, content that sits in raw HTML rather than within JavaScript, clear headers, and structured data that defines what content a page contains. GEO adds some nuances on top of those basics, but it does not change them.
To be blunt, GEO and SEO share a lot in common. They rely on the same processes and principles. Three distinctions, however, remain significant. First, because a featured snippet citation is a one-time prize, quality of response and ease of citation matters more than any other content characteristic; it is far easier for an AI model to quote a page that provides direct, plain-language responses within an explicit hierarchy than a meandering one stuffed with keywords. Second, AI crawlers make up an entirely different bot fleet (with its own names and access rules) and as such, it is possible to change a robots.txt line you haven’t even considered touching your current Google rankings, and that decision could instantly remove you from the new AI search results. Third, most AI crawlers can’t run JavaScript; this means that a site rendering perfectly fine within your web browser could look like an empty void to the very bots you seek to impress.
None of this is to say that you should abandon SEO altogether; organic traffic will still drive the lion’s share of site visits for most of us, but also, the SEO work you’re already doing to optimise for Googlebot is generally good work for your new AI-powered friends as well. GEO, really, is just technical SEO extending onto a second surface, though with a few novel failure points to look for.
If you’d like a short checklist of what matters most, first it’s accessibility: the majority of AI search engines fetch pages using a named bot, such as GPTBot and OAI-SearchBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot (Perplexity), or Google-Extended (Google Gemini), and while it’ll take only one robots.txt Disallow line to block any of these, it’s surprising to discover how many websites inadvertently block AI crawlers entirely. Our guide to controlling AI crawlers via robots.txt provides the steps you’ll need to take on that front, and you can see which bots you’ve already allowed to access your site, by name, via the AI readiness checker.
Next, it’s retrievability: If you only serve your important content via JavaScript, bots with no JavaScript execution capabilities will render nearly nothing on your web page no matter how much you love the information they just missed. The bot’s view of your page’s rendered structure is discussed at do AI crawlers execute JavaScript?. After that, it’s readability: a page that has both a proper title tag and a logical heading hierarchy is far more likely to be understood by an AI model, and to surface as the basis of a good AI answer, than one that lacks such structural guidance; structured data also helps a search engine’s understanding and is recommended for this purpose. An LLMS.txt file, at least in its current iteration, is less of a priority for most sites. Finally, the one piece of this puzzle no amount of technical tweaks will affect is authority: citations, references and trust from across the web remain key determinants to whether an engine is likely to cite your pages over your competitor’s, and these remain slow-building assets.
Rather than starting with tracking your rankings or rewriting all your pages to appeal to some “search engine algorithm,” start by verifying that these search engines are even able to reach and read your pages, because that is by far where most sites lose out to their competitors. Run a crawl to confirm you’re not blocking AI crawl bots via robots.txt, verify that all of your important web page content renders when JavaScript is disabled, review your site’s schema markup to be sure you’ve added as much of it as you can, and clean up the HTML heading hierarchy of your site. With those fundamentals covered, the more advanced work, such as getting cited, growing your topical authority, and keeping your content up to date, will have a firmer foundation to build on. After this, you can read about how to get your site quoted in ChatGPT and Perplexity or compare and contrast these two search disciplines in GEO vs SEO.
It is the work of getting your pages picked up and cited by AI answer engines like ChatGPT, Perplexity, Google AI Overviews and Claude. Where classic SEO tries to rank a page in a list of links, GEO tries to make your content one of the few sources an AI model quotes when it writes an answer.
It overlaps more than it differs. Most of GEO rests on the same technical foundation as SEO — crawlable pages, clean HTML, good structure and schema. What changes is the goal: instead of a click from a ranking, you are after a citation inside a generated answer, which rewards clear, well-structured, genuinely useful content.
Not really. The first checks are things a crawler already covers: can the AI bots reach your site, is your content in the raw HTML, and is it marked up clearly. You only need dedicated AI-visibility monitoring once the foundations are solid, and most sites are not there yet.
No. Classic search is still where most traffic comes from, and the technical work that helps Google also helps the AI engines. Think of GEO as an extension of technical SEO into a second surface, not a replacement for it.