GEO for AI Search and Agent-Ready Websites
This episode breaks down how businesses must optimize for three audiences: human visitors, AI interpreters, and autonomous buying agents. It covers clean markdown, llms.txt, schema markup, robots.txt checks, and practical GEO auditing to improve visibility in ChatGPT, Claude, Gemini, and other generative engines.
Chapter 1
The Three Audience Web and Clean Markdown Menus
Ido
Picture this scenario. A prospective client is evaluating your enterprise software product right now, comparing features, checking security specs, even asking about pricing. But they have never actually visited your site. They are doing all of this inside ChatGPT, or Claude, or Gemini.
Ben cohen
Yeah, it, it, it is the zero click buyer behavior, right? They never hit your homepage.
Ido
Exactly. Which means suddenly every company is building for three completely different audiences. You have got the human visitors who want the pretty visuals, you have got the LLM interpreters scanning for raw context, and now you have got autonomous buying agents that need structured data to actually take action. And most sites are, uh, frankly failing the second two.
Ben cohen
Well, because traditional web design is built for human eyes. It is all heavy JavaScript frameworks, nested div tags, popups, hero animations.
Ido
Right, it is like a, uh, imagine an ornate, fancy restaurant dining room. Big velvet curtains, dim lighting, elaborate physical presentation. A human sits down and goes, wow, nice atmosphere. But an AI crawler is not looking at the decor. It just wants to read the menu. And if that menu is buried under ten layers of visual styling, the model has to work ten times harder to figure out what you actually sell.
Ben cohen
That token efficiency angle is so critical. If you strip away all that template bloat and deliver clean markdown, or use an llms.txt file, you are handing the model a plain text menu. It can instantly extract your pricing, your service specs, your core entity definitions without getting lost in the code junk.
Ido
Mm, totally. And if it cannot extract those signals easily, what happens?
Ben cohen
You get skipped! I mean, I, I see this all the time with site owners. They spend millions on SEO, but then you look at their robots.txt file and they are actively blocking GPTBot, or ClaudeBot, or Google Extended. They literally put up a closed sign for AI engines. If you block those crawlers, you drop off the candidate shortlist instantly. You do not even get cited in the generative answer.
Ido
Yeah, and that is where platforms like LLMagnet come in. Instead of just guessing whether your brand is visible, LLMagnet tracks actual AI bot traffic. It shows you when Claude or ChatGPT is crawling your pages, which specific prompts your brand appears in, and how your visibility score changes over time.
Ben cohen
Right, because traditional rank tracking is basically obsolete for AI search. AI models are stochastic, they calculate probabilities for text generation. So you are not tracking whether you are rank number three on Google anymore. You are tracking retrievability and share of voice across prompt answers.
Ido
And, and this shift goes way beyond just being cited in a summary. We are moving from passive content discovery to active agent readiness. Like, if you implement proper schema markup, like FAQ schema or Person schema, you are giving structure to your data. So when an autonomous purchasing agent comes along to book a service or fill out a form on behalf of a user, it actually knows how to interact with your site layout.
Ben cohen
FAQ schema is huge for that. It is literally handing the model a question and answer pair on a silver platter. When someone asks Perplexity a specific technical question, the engine can pull that exact three sentence answer from your FAQ schema because it does not have to guess what the paragraph means.
Chapter 2
The Practical GEO Audit and Common Pitfalls
Ben cohen
So if someone is listening to this right now and thinking, okay, how do I actually audit my site for generative engine optimization today? What is the checklist?
Ido
Step one is embarrassingly simple. Open your website domain slash robots.txt right now. Make sure you are not explicitly disallowing GPTBot, ClaudeBot, PerplexityBot, or Google Extended. You would be shocked how many managed hosts block them by default under generic security rules.
Ben cohen
Yeah, check that immediately. Step two, validate your structured data. Run your core post URLs through Google's Rich Results Test tool. Look for Article schema, check that dateModified is enabled so models see your content is fresh, and verify your Author Person schema.
Ido
Right, because AI models care deeply about who wrote the piece. If your WordPress site still lists admin or webmaster as the author, that is a huge trust penalty for E E A T signals. You need a real human name connected to social profiles and structured author markup.
Ben cohen
And step three of that initial audit is manual prompt testing. Pick five to ten core sales prompts that your ideal customer would type into ChatGPT, Gemini, or Perplexity. Run those exact prompts every week and record whether your brand gets mentioned, or if your competitor gets cited instead.
Ido
Now, let us talk about the traps people fall into when they start doing this, because there are some big ones. The biggest temptation right now is using plugins to auto generate hundreds of low quality, AI generated pages or category descriptions. People think, oh, more pages equals more net for AI crawlers.
Ben cohen
Oh, absolute disaster. The models detect that thin, repetitive text super quickly and just ignore the entire domain. Quality and clean structure beat sheer volume every single time.
Ido
Another massive mistake is schema hallucination, or contradiction. Like adding JSON LD schema that says your product costs fifty dollars, but the actual visible text on the page says ninety nine dollars, or says contact us for pricing. When the schema contradicts the on page text, language models flag it as untrustworthy and drop your citations.
Ben cohen
It has to match perfectly. Now, I, I know you are big on llms.txt as a foundational standard, Ido, but I want to push back a little on how much weight people put on it.
Ido
Oh, really? Why?
Ben cohen
Because some site owners treat llms.txt like it is a magic ranking file from 2002, like meta keywords used to be. They think if they just put a markdown file on their root directory, boom, ChatGPT will recommend them first. But off page signals, like brand mentions on Reddit, articles on trusted third party publications, and general entity consistency across the web, matter way more for winning citations in LLM responses than just a local text file.
Ido
Well, sure, it is not a silver bullet by itself. But think of llms.txt as the clean architectural blueprint. Off page mentions build your brand reputation, absolutely, but when the bot actually arrives at your digital doorstep, the markdown map ensures it does not get confused by heavy page builders like Elementor or Divi that clutter your source code with thousands of unnecessary lines.
Ben cohen
Fair point. It reduces friction for the crawler, but the reputation still has to be there across the wider web.
Ido
Exactly. They work together.
Ben cohen
So if you want one concrete action step for today, take fifteen minutes. Open ChatGPT, Perplexity, and Gemini. Type in your single most important product search prompt, like best enterprise CRM for logistics, or whatever your category is. See who gets cited. If it is not you, you know your technical foundation and AI visibility need work.
Ido
Run that test today. Good chatting, Ben.
Ben cohen
Talk soon.