If you've ever searched for something and gotten an AI-generated answer that cited specific sources, you've seen Generative Engine Optimization in action. The sites that get referenced aren't just lucky—they've structured their content in ways that AI systems can easily parse and understand.
So what exactly makes content "AI-readable"? It starts with how you build your pages. Most websites are constructed using HTML, and there's a big difference between HTML that just makes things look right versus HTML that actually communicates meaning. When you wrap your blog post title in a generic container versus marking it up as an actual article with a proper heading, you're sending very different signals to the AI systems that are crawling your site.
The technical term for this is semantic markup, but really it's just about being explicit. Instead of creating a bunch of boxes that happen to contain your content, you're labeling each piece: this is the main article, this is when it was published, this is the author information, these are the major sections. AI tools like ChatGPT or Perplexity can then build an accurate mental model of what your content contains and how it's organized.
Think about how these AI systems actually work. When someone asks ChatGPT a question and it searches the web for sources, it's not just looking at the words on your page. It's trying to understand the document structure. Where does the main content start? What are the key points? When was this written? Who wrote it? Pages that answer these questions clearly through their markup get cited more often because the AI can confidently pull accurate information from them.
The difference between sites that get cited and sites that get ignored often comes down to how information is prioritized. AI systems typically pull heavily from your opening paragraph, your conclusion, and whatever you've marked as headings. If you bury your most important point three paragraphs down in the middle of a wall of text, there's a good chance the AI will miss it entirely. But if you lead with your main argument, organize supporting points under clear headings, and tie it all together at the end, you're making the AI's job easy.
Publication dates matter more than you might think. AI systems are trying to figure out which information is current and which might be outdated. A page with a clearly marked publication date gets evaluated differently than one where the AI has to guess based on context clues. The same goes for author information—pages with clear attribution are seen as more trustworthy sources.
Here's something interesting: the way you structure your headings creates a kind of outline that AI systems follow when they're summarizing your content. If you jump from a main heading straight to a sub-sub-heading, or if you use heading tags just to make text bigger, you're breaking that outline. AI systems expect a logical hierarchy, and when they find it, they can generate much more accurate summaries of your content.
There's also a more advanced layer you can add called microdata that essentially provides a translation layer between your content and what AI systems understand. It uses standardized vocabulary from schema.org to mark up things like article titles, author names, and publication dates in a way that's completely unambiguous. This isn't strictly necessary, but it removes any guesswork about what each element represents.
The trap a lot of people fall into is treating their website like it's 2010. They use generic containers for everything, skip semantic elements because they can achieve the same visual result with CSS, and don't bother with things like publication dates or author markup because they assume humans will figure it out from context. Humans might figure it out, but AI systems won't—or worse, they'll guess wrong.
Testing your GEO is straightforward. Ask ChatGPT to summarize your article and see if it captures your main points accurately. Search for your topic in Perplexity and check whether your site gets cited. Look at Google's AI Overview results for keywords you care about. If you're consistently getting overlooked or misrepresented, that's a sign your markup needs work.
The fix usually involves going back through your content and replacing generic markup with semantic alternatives. Every blog post should be wrapped in an article element. Every article should have a clear heading hierarchy. Publication dates should be marked up with time elements that include machine-readable datetime attributes. Author information should use proper schema markup. It's tedious work if you've got a lot of existing content, but it's the kind of thing that compounds over time.
What's interesting about GEO is that it's not really about gaming the system. You're not trying to trick AI into citing you or manipulating results. You're just making your content's structure explicit instead of implicit. When you do that well, AI systems naturally gravitate toward your content because it's easier to parse, understand, and verify. The sites that do this consistently are the ones that end up being go-to sources for AI-generated responses.
The broader shift here is that content isn't just being read by humans anymore. AI systems are increasingly the intermediary between your content and your audience. That means thinking about two audiences when you publish: the person who might eventually read what you wrote, and the AI system that might cite it, summarize it, or use it to answer someone's question. Optimizing for both isn't that different—clarity, structure, and explicit meaning help everyone understand your content better.