AI SEO Course for Beginners: Complete AEO Tutorial

Hey everyone, my name is Samo and welcome to the answer engine optimization or AEO course by HRS. In this course, I'll be teaching you how to get traffic from AI search and to get more brand visibility in it. This course will have a heavy focus on execution. And while the topic might be new to many of you, I don't want you to feel intimidated, especially if you have a background in SEO because SEO is the foundation of AEO. Now, here's why you need to pay attention to this right now. As of December 2025, the presence

of an AI overview on Google reduces the click-through rate for the number one ranking page by 58%. That means for every 100 clicks you used to get, Google now keeps 58 of them. Chat GPT alone has 900 million weekly users. It handles roughly 12% of Google search volume. And AI traffic to websites has grown 9.7 times in the past year. So, you can either ignore the shift and watch your traffic shrink while raising your fist at the clouds, or you can learn how it works and capitalize on it. Because here's the thing most people miss.

In June 2025, AI search accounted for just 0.5% of our traffic, but it drove 12.1% of our signups. That's a 23 times higher conversion rate than organic search. Search has changed and it's no longer just about clicks because AI traffic is one of the highest converting channels we've ever seen. So, let's start with the basics. In this first lesson, I'm going to explain what AEO actually is, why it matters, and give you a road map for the entire course so you know exactly what's coming. So, what is answer engine optimization? AEO or answer engine optimization

is the practice of making your content visible and useful to AI systems that deliver direct answers. That's Google AI overviews, ChatGBT, Perplexity, Gemini, Copilot, basically any platform where AI is generating a response and choosing who to site. You'll also hear this called GEO, generative engine optimization, or LLMO, large language model optimization. They all mean essentially the same thing. So, we'll use AEO throughout this course. Now, you might be wondering, how is this different from traditional SEO? Well, in traditional SEO, you optimize web pages to rank higher In a list of search results. You're competing for

positions. The user sees your blue link, clicks it, and lands on your site. In AEO, there is no list. The AI reads from dozens of sources, synthesizes an answer, and decides who to mention or site. You're not competing for a position, you're competing for a mention. And the rules for getting mentioned are a bit different from the rules for ranking in organic blue link results. Now, we'll break down exactly how that works in module one. But for now, the key thing to understand is this. AEO doesn't replace SEO. It builds on top of it. Think

of SEO as your foundation and AEO as futureproofing your online presence. The fundamentals still matter. quality content, authority, technical health, but the strategy needs to evolve because the way people find information is changing. Now, I know some of you might be thinking, "Is AI just going to kill SEO completely?" And I get it, but this is not the end of SEO. Ethan Smith from Graphite talked about the zero sum bias on HR's podcast, and this is a cognitive bias based on the assumption that if something new goes up, Something old must go down. And we

saw the exact same thing in 2010 when the app store blew up. Everyone said, "The web is going away. Mobile apps are taking over. Only old people use websites." And yes, mobile apps did blow up. That promise was real. But the web didn't die. It grew alongside these apps. AI search is likely going to be the same story. It is growing fast. Those numbers I just showed you are real. But Google still processes billions of searches per day. Traditional organic traffic still drives massive business value. So, the smart move isn't to panic, it's to play

both games. And let me be real with you, most businesses haven't even started thinking about this. We looked at HF's own data and found that our content and product pages have been mentioned 7,470 times across 2,39 pages in AI search without any effort to optimize for it. That's all happening organically because we've been doing solid SEO and creating content that people find useful. Now, imagine what happens when you actually start intentionally optimizing for this. When you know which platforms site what, when you understand how AI decides Who to recommend when you're actively building the signals

that make AI want to mention your brand. That's exactly what this course will teach you. The course is broken down into four modules plus this introduction. Module one is about how AI search actually works. We'll cover the mechanics, how AI finds content, how it decides what to site, how different platforms like Chat GBT, Google AI overviews, and Perplexity actually differ from each other, and what AI visibility actually looks like for different types of businesses. This is the foundation that makes everything else make sense. Module two is all about strategy. I'll show you the data behind

why brand mentions are the single strongest lever for AI visibility, stronger than backlinks, domain rating, or anything else. You'll learn keyword research for AEO, prompt research, and how to figure out where your brand stands versus competitors. Module three is about execution. This is where the actual work happens. How to create content that gets cited, how to earn mentions and citations for other sites, YouTube optimization for AI visibility, which no other course covers, and the technical stuff to make Sure AI can actually find and crawl your content. In module four is about measurement. You'll learn how

to set up your analytics for AI traffic, how to run competitive audits to find your brand gaps, and how to evaluate whether AEO is actually worth the investment for your business. By the end of the course, you should be able to put together a concrete action plan that's bespoke for your business with workflows to actually do this stuff immediately. All right, so that's the lay of the land. In the next lesson, we're diving straight into how AI search engines actually find and site content, including the concept of query fan out, which is probably the single

most important thing to understand about why AI search works the way that it does. I'll see you in the next lesson. Hey, it's Samo and welcome to the first module which is on how AI search actually works. In this lesson, I'm going to walk you through the three things you need to understand about how AI search engines find, evaluate, and site content. Because here's the thing, if you don't understand how AI search works under the hood, every optimization tip you hear is just going to feel like a random List of tactics. And I don't want

that for you. I want you to understand the why behind every strategy we cover in this course. Let's get started. So where does AI actually get its information? This is probably the most important distinction to understand. AI search engines have two sources of information and they work very differently. The first is training data. This is the massive collection of text that the AI was originally trained on. So books, websites, PDFs, social media, YouTube transcripts, basically a snapshot of the internet. So when you ask ChatGpt, "Who is the CEO of Apple?" and it instantly says Tim

Cook without searching anything, that's coming from training data. It already learned that pattern. But here's the problem with training data. It's static. It gets updated maybe every 6 months or so. So if you launched your product last week, the AI doesn't know about it yet. not from training data at least. And that's where the second source comes in, real time retrieval. This is where rag or retrieval augmented generation comes into play. It sounds complicated, but Patrick Stock said it best. Uh then you've got the the retrieved pages, which is like a secondary process. So you've

got the the trained LLM data and then you've got the data where it goes out and fetches a bunch of relevant pages and those come with other probabilities. For example, when ChatGpt or Google's AI mode needs fresh information, or when the question is too specific for training data alone, it goes out, it searches the web using APIs, it pulls back a bunch of pages, reads through them, and then generates a response based on what it found. Now, why does this matter for you? Because it means there are two ways to influence what AI says about

your brand. The first is to be mentioned so widely across the web that you're baked into the training data itself. And second is to make sure your content shows up when the AI searches the web in real time. And guess what? We already know how to do that. That's SEO. The skills you already have from traditional SEO, ranking in Google, earning backlinks, creating quality content, those directly influence whether AI picks up your pages during real-time retrieval. Now, here's where it gets interesting. The AI doesn't just search for the exact thing you typed in. Let me

explain. Search engines used to work one to one. One query, one set of results. Then they evolved to many to one where different queries like Sydney plumber and plumbing service in Sydney could return the same results. But AI search has flipped the model to one to many. One search gets expanded into many. And this technique is called query fo. For example, when someone enters a prompt like, "Plan me a 5-day trip to Japan in November." The AI fans it out into dozens of smaller longtail subqueries. Things like, "Best neighborhoods to stay in Tokyo, November weather

in Kyoto, Japan Rail Pass worth it?" All running simultaneously behind the scenes. It then pulls information from multiple sources across the web and combines it into one complete answer. In fact, research from Seir Interactive in Nective found that the average prompt triggers 9 to 11 fan out queries with some going as high as 28. And ChatGpt's deep research mode ran 420 searches for a single query about buying a red phone case. So, if your content ranks for those niche specific queries, your brand has a much better chance Of being included in the AI's final response.

And this is a huge shift from traditional SEO where you could optimize one page for one target keyword and call it a day. In AI search, you need to be relevant across an entire topic. And I could even argue across an entire niche. Because if your page about how to start a podcast only covers the basics, but doesn't mention equipment, hosting, or promotion, the AI is going to find someone else's page that does. Now you might be wondering, can I see these fanout queries? You sure can. In the AI responses report in HFS brand radar,

you can see the fan out queries for chat GPT and perplexity prompts. But there's an important caveat. Despina from HFS wrote in her guide on query fan out that these aren't like traditional longtail queries. They're synthetic, generated by AI in the moment. They're inconsistent. The same prompt can trigger different fanouts every time. And over 95% of them have zero search volume because real humans would never type them. So don't think of fano queries as a new keyword list to optimize for. Think of them as a window into what topics the AI considers important for a

given question. We'll get into exactly how to use this strategically in module 2 because for now we need to talk about how AI decides who to actually site. In traditional search rankings are relatively stable. Like if you're number three for a keyword today, you're probably going to be somewhere around there tomorrow. But AI citations are probabilistic. Patrick Stocks explained this really well. He said, "AI outputs are built on probabilities on top of probabilities on top of probabilities. The training data creates patterns. The retrieve pages add their own signals and then there's a temperature setting that

introduces randomness so the AI doesn't generate the exact same answer every time." Now, what that means in practice is that if you ask the same question five times, you might get cited three out of five. Or the AI might mention your competitor twice and you twice and someone else once. There's no fixed position to rank for. This is why we talk about AI visibility rather than AI rankings. It's more like a probability distribution than a leaderboard. Now, that said, there are patterns in what gets cited more often. Based on the data we've studied At HRES,

consensus matters. If multiple sources on the web say the same thing about your brand, AI is more likely to repeat it. And the more places your brand is mentioned in a consistent way, the higher the probability the AI picks it up. Freshness matters. AI cited content tends to be about 25% fresher than what you'd see in a traditional SER. The AI is actively looking for recent information, especially for topics that change. Authority still matters. Pages that rank well in traditional search have a major head start. Our data shows that 76% of AI overview citations come

from pages already in the top 10 of Google. So, I'll hit that gong one more time. SEO is the foundation of AEO, but it's not only about Google rankings. 14% of pages cited in AI overviews don't rank in Google's top 100 at all. And for platforms like Chad GBT, the overlap with Google's results is even lower. So there's real opportunity for brands that aren't dominant in traditional search to still show up in AI. So let's tie all of this together. AI search engines pull from training data and real time web search. They expand your one

query into dozens of subqueries through fan out. They merge and score results from all of those searches. and then they generate a response that's probabilistic, not fixed, based on patterns, consensus, freshness, and authority. Understanding this process is what makes every other lesson in this course make sense. So when I tell you to earn more brand mentions, it's because of how consensus drives probability. When I talk about topic coverage, it's because of query fan out. When I say that SEO is the foundation again, it's because 76% of citations come from pages already ranking well. Now that

you understand the mechanics, the obvious next question is, do all AI platforms work the same way? And the answer is not even close. Not even close. In the next lesson, we're going to compare AI overviews, chatbt, Perplexity, and Google's AI mode side by side. And the data on how different they are is pretty surprising. I'll see you in the next lesson. Hey, it's Samo and welcome to the second lesson which is on the differences between AI search platforms. Now, in the last lesson, I covered how AI search engines find and site content. But here's what

I didn't tell you. They don't all do it the same way. And this matters a lot more than most people realize. Because if you're treating AI search as one thing and optimizing for it the same way across the board, you're likely leaving visibility on the table. So in this lesson, I'm going to break down the major AI search platforms like Google's [clears throat] AI overviews, Google's AI mode, ChatGpt, and Perplexity, and show you the data on just how different optimization for these platforms are. Let me start with a stat that might surprise you. We looked

at the top 50 most cited domains across Google AI overviews, ChatgBT, and Perplexity. And out of those 50, only seven appeared on all three platforms. That's only a 14% overlap. Think about that for a second. If you're only optimizing for one platform, you're potentially invisible on the others. And it gets even more interesting when you look at what each platform actually prefers. Google AI overviews favor authoritative established sites. Think health, finance, encyclopedic content, and Google owned properties like YouTube. In fact, YouTube accounts for about 5.6% of all AI overview Citations. And just look at how

fast they're adding YouTube videos to their AI overviews. And obviously, Reddit's big here, too. ChatGpt leans heavily toward publishers and media. For example, authoritative sites like Reddit, Wikipedia, Amazon, Forbes, Business Insider, and Wired are some of the most cited domains here. In fact, the median domain rating of Chat GBPT's top-sighted pages is 90. So, it's pulling from high authority publishers, and that's partly because of licensing deals OpenAI has with some of these outlets. Perplexity is actually the most aligned with traditional Google search. About 28.6% 6% of perplexity citations come from pages that rank in Google's

top 10. Compare that to Chat GBT, which only overlaps with Google's top 10 about 8 to 10% of the time. So, if you're already ranking well in Google, Perplexity is probably where you'll see the most immediate AI visibility. And then there's Google's AI mode. Now, you might assume that since AI mode and AI overviews are both Google products, they'd site similar sources. Well, they don't. The citation overlap between AI overviews and AI mode is only 13.7%. Despite the fact that their answers are 86% semantically similar, meaning they're saying similar things but pulling from completely different

sources. AI mode's top-sighted domain is YouTube by a wide margin, followed by Google and Wikipedia. We also found that it cites Quora 3.5 times more than AI overviews and pulls from social platforms like Facebook and Instagram much more heavily. So the takeaway here is clear. AI search is not one thing. Each platform has its own index, its own biases, and its own preferences for what kind of sources it likes to site. So which platform should you focus on? This is the practical question and I think it's important to be strategic here rather than trying to

optimize for everything at once. There are really two things to consider. The first is market share. As of right now, Google's AI features and chat GBT account for the vast majority of AI search traffic. Proplexity is growing, but it's still a fraction of the volume. So, if you have to prioritize, Google and chat GBT are where most of the eyeballs are, at least for now. The second thing to Consider is how much overlap there is between what you're already doing in SEO and what each platform rewards. If you're already ranking well in Google, you have

a natural head start with AI overviews and Plexity. So, why not lean into it? 76% of AI overview citations used to come from pages already in Google's top 10. And I say used to because that number has dropped. A more recent study shows it's now closer to 38%. Which is still a lot. AI overviews are increasingly pulling from pages outside the top 10, including YouTube and Reddit. So, the connection between Google rankings and AI citations is weakening. Chat GBT is much more interested in publisher authority and editorial content. So, if your brand gets mentioned on

Forbes, in a Reddit thread, or on a niche review site, that might matter more for ChatGpt visibility than your own pages Google ranking. And here's something a lot of people miss. Many of the top sighted domains in AI search don't get any traditional search traffic at all. AI visibility is its own game. Now, if you want to see the top domains for a specific platform, you can check that in HR brand radar. Just run a blank search And go to the cited domains report. Then you can filter using your desired AI platform. What you'll typically

see is that the websites citing your brand are different on each platform. A domain that shows up heavily in AI overviews might barely appear in ChatGBT and vice versa. This is why a one-sizefits-all approach to AEO doesn't work. Now, the good news is you don't need to build completely separate strategies for each platform. A lot of the fundamentals, creating quality content, earning mentions, building topical authority help across the board. But knowing where each platform pulls from helps you prioritize your efforts. For example, if chat GBT is a big traffic driver for your niche, you'd want

to focus on getting mentioned on high DR editorial sites and publishers. If AI overviews matter more, YouTube and Reddit should be on your radar. We'll get into these specific strategies in modules 2 and three. But for now, the key thing to understand is that each platform is its own ecosystem with its own rules. Now, before we actually start optimizing for AI search, there's something else you need to understand. What does winning an AI search even look like? Because it's not just about getting a link or a mention. So, in the next lesson, I'm going to

break down the different types of AI visibility and why some of them might be more valuable to you than others. See you in the next lesson. Hey, it's Samo and welcome to the third lesson in module one. Now, in the last two lessons, I covered how AI search engines find and site content and how each platform works differently. But there's a question we haven't answered yet. What does winning in AI search actually look like? Because most people hear AI visibility, and they think it means one thing, getting a link in an AI response. But that's

just one piece of the puzzle. And honestly, it might not even be the most valuable piece for your business. So, in this lesson, I'm going to break down the different types of AI visibility, show you the data on how often each one actually happens, and help you figure out which type matters most for you. Let me walk you through the three types of AI visibility, and I want you to think about which One applies to your business as I go through each. The first is cited and linked. This is the one everyone thinks about. AI

includes a link to your page and its sources. The user can click through and land on your site. This is the best case for direct traffic and it's the easiest to measure. The second is mentioned but not linked. AI says your brand in its response but it doesn't give the user a link to click. You don't necessarily get the direct traffic or any traffic but the user now knows your name. And if they're interested, what are they going to do? They're going to search for you. This is basically a word of mouth recommendation at scale.

And the third is not visible at all. AI doesn't mention you. It doesn't link to you. you're just not in the conversation. And this is actually important to know because you can't fix what you don't know is broken. Now, here's the thing. Most people assume that when AI mentions your brand, it's probably including a link. Well, not exactly. On average, only about 28% of AI mentions include a link. That means roughly seven out of 10 times your brand comes up in an AI response, there's no Link attached. And it varies a lot by platform. Perplexity

is the most generous, about 51.6 6% of mentions get a link. AI mode is around 36.8%. Chat GBT is at 26.9% and AI overviews only 10.7%. So if you're mostly tracking AI overviews, almost nine out of 10 times your brand shows up, there's no link. Now, you might think that means most AI visibility is low value since there's no click. But here's something that might surprise you. When we weighted these mentions by search volume, basically how many people are likely asking similar questions, the ones that do include a link tend to appear on much higher

traffic queries. For example, on Perplexity, links show up in about 78% of total impressions, even though they're only in 51% of individual mentions. On Gemini, it's even more dramatic. Links appear in 71% of impressions despite being in only 16.8% 8% of mentions. So, the takeaway here is this. Citations are relatively rare, but when they happen, they tend to happen on the ones with the most eyeballs. But that doesn't mean unlin mentions are worthless. Far from it. Think about how LLMs actually work. They learn by reading the web. And every time your brand name appears on

a credible site connected to a specific topic, that becomes another training example. The more the model sees your brand associated with a topic, the more confidently it mentions you when someone asks about that topic. It's like when you hear peanut butter, you think jelly. Or when you hear Tesla, you think electric cars. LMS build these same associations. And unlin mentions are what feed them. And there's real data to back this up. In a study of 75,000 brands, branded web mentions had the strongest correlation with AI visibility, a 0.664 664 correlation with showing up in AI

overviews that's stronger than backlinks, domain rating, referring domains, or any other traditional SEO metric. So even when AI doesn't give you a link, just being mentioned is building your brand's presence in the model. It's the long game and it compounds over time. Now, there's another layer to this. Not all AI responses look the same and the type of response matters for your business. For example, some queries trigger step-by-step guides like how to fix a leaky faucet, How to set up Google Analytics, and if you're a service-based business or you create how-to content, these can be

opportunities if you can get AI to recommend you as an expert. Then you have direct answers like what's the capital of France? If you're a publisher orformational brand, being the source AI sites for factual answers can build massive authority, but you probably won't get a click from these since AI gives the answer directly. And then there's video citations. We covered this in the last lesson. YouTube is one of the most cited domains across AI platforms. And if you're a creator, your videos can show up directly in AI responses. And some people prefer watching videos over

reading text. So here's what I want you to take away from this lesson. AI visibility isn't binary. You're not just visible or invisible. It's a spectrum. And the type of visibility that matters most depends entirely on your business. If you sell a product, you want your brand showing up when people ask about your category. For example, a query like best golf balls surfaces tons of different brands and models and someone Can literally click through from the AI response, land on a shopping result or the manufacturer site and buy something right away. That's a citation doing

real work. If you're a publisher or content creator, training data visibility is your long gate. Every article AI cites, every video it surfaces reinforces your authority in the model. And if you're not yet visible, well, that's why you're taking this course. Now, if you want to see where you stand in AI search, go to HS brand radar and search for your website. And right away, you'll see how you stack up against competitors across different AI platforms. And you can filter by mentions, which are when your brand is named but not linked. citations, which is when

AI actually links to your site, impressions, and share a voice. The gap between your impressions and your mentions is your opportunity. If AI is responding to queries about your topic, but not naming you, that's exactly where you need to focus. And that's exactly what we'll be covering in module 2, where we'll cover AEO, strategy, research, targeting, and gaps. I'll see you there. Hey, it's Ammo and welcome to the second Module in our AI search course. Now, in module one, we covered how AI search actually works, the mechanics, the platforms, and what winning AI visibility looks

like. In this module, I'm going to walk you through an AEO strategy that works in two phases. First, we'll assess where your brand stands today, figuring out exactly where the gaps are. Then, we'll move on to discovery, where we'll find the keywords and prompts people use to get information related to your business. So, let's start with step one, assess. In this lesson, I'm going to walk you through how to run a brand gap analysis. A brand gap analysis measures the difference between where your brand should be showing up and where it actually is. So, this

is in Google search, AI results, and basically across the web. It examines everything shaping your discoverability and reputation from how AI describes you to which competitors are cited instead of you. So, the first step in this analysis is to map out your branded entities. Before you can find your gaps, you need to get clear on what you're actually measuring. And here's the thing, your brand gets Referred to differently by different people. So, you want to map out your main brand name, any subbrands, product names, proprietary features, proprietary metrics, and even personal brands associated with your

company. For example, at Hrefs, we'd map out the main brand, Hrefs, product brands like Site Explorer, Brand Radar, and AI content helper, proprietary metrics like domain rating and traffic potential, and personal brands like Tim Solo, Patrick Stocks, Ryan Law, and Glenn Alaw. Each of these has its own visibility profile in search and AI results. And you can repeat the analysis I'm about to show you for each one. Once you have your list, connect each entity to the topics and attributes people should associate with it. Search engines and LLMs don't understand brand names on their own.

They infer meaning from how your brand is described and discussed. So, you need to clarify what problems you solve, what qualities you're known for, and what context you belong in. A quick way to do this is keyword research. Look for recurring adjectives, modifiers, and descriptive phrases people use alongside your brand Or category. things like affordable, AI powered, enterprisegrain, whatever applies to your space. This gives you your benchmark for what your brand should be known and found for. Now, let's move on to the second step, which is to run your audit in hres. Start by entering

your brand's website into site explorer. You'll get a dashboard of baseline metrics, domain rating, referring domains, organic keywords, organic traffic, and traffic value. This gives you your traditional SEO snapshot. But what we really care about in the context of AEO are the AI citation metrics which give you a snapshot of your brand's visibility in AI search across different platforms. Clicking into any of those takes you to the brand radar report for that platform which is where the real audit happens and there are four key things to pay attention to. First are mentions which are the

number of times your brand is mentioned in AI responses. Then we have citations which are the number of times your website is actually cited as a source. impressions, an estimation of exposure based on how often responses containing your brand are shown, and AI share a voice, How often your brand is mentioned compared to your competitors. Now, the real power of Brand Radar is in its filters. This is where you can isolate the exact scenarios that matter. For example, you can filter for prompts that mention your brand name in the AI platform of choice. You can

filter for responses that mention your brand but don't site your website. Those are miscitation opportunities. You can also add a competitor and then filter for queries where they're mentioned but you're not. And you can look at specific topics to see whether AI associates them with your brand or someone else's. Now take note of these numbers and let's move on to the third step which is to identify your gaps. When you're looking at this data, it helps to think about your gaps across six dimensions. And this is a framework Despina from Atro's laid out in her

brand gap analysis guide. The first is your visibility gap. This is where your brand appears less often than competitors in search or AI results. The second is your narrative gap. This is how AI or media describes your brand versus how you actually want to be positioned. For example, maybe you're a premium tool, but AI Keeps calling you a budget alternative or your key differentiator just isn't coming through. Third is your topic gap. These are topics you should be associated with but aren't. Like if you're a project management tool, but AI never mentions you when people

ask about remote team collaboration. That would be a topic gap. Fourth is your format gap. AI tends to site certain types of content, guides, videos, reviews, comparison pages, and if you're not producing them, that's a gap. For example, in module one, we talked about YouTube being one of the most cited domains. And if your competitors have YouTube content getting cited and you don't, then you've got a content format gap. Fifth is your web mentions gap. These are external sources, listicles, review sites, forums, publications that mention competitors but not you. And as we covered in module

one, those third party mentions are one of the strongest signals for AI visibility. And sixth is your demand gap. These are branded queries or searches that signal awareness opportunities you haven't captured. People are searching for things in your space, but your brand name never comes up Alongside those searches. Now, when you look at all six together, you get a complete picture of your brand's visibility in AI. Not just how often you show up, but whether you're showing up for the right things in the right way in the right places. So, as you do your brand

audit in step two, it's worth adding your prompts to these buckets. And it'll set you up perfectly for the fourth step, which is to prioritize. You're going to find a lot of gaps, and you can't fix everything at once. So, you're going to have to prioritize your efforts. You're generally going to do one of three things. Fix, which is improving or optimizing something that already exists. you're going to build, which is creating new content or pages for opportunities you're not covering at all, or influence, which is strengthening your off-site visibility through outreach and brand mentions.

And you want to weigh each opportunity by asking, "How much demand could this drive? Does it support your brand credibility? And does it improve your chances of being cited in AI?" Start with the quick ones. If you have a page that's already ranking but just needs a content update to close a topic gap, That's low effort and potentially high impact. If you're missing from a major listical that all your competitors are on, that's a web meions gap you can close with outreach. Now, as you go through this process, you'll probably find that most of your

gaps fall into one or two of the buckets I mentioned before. And that's a good thing because it tells you exactly where to focus so you can create systems around them. and I'll walk you through how to close each type of gap in module 3. From creating content that gets cited to earning mentions from thirdparty sites to optimizing your YouTube presence. So the audit you do right now, it becomes your personalized road map for the rest of this course. Now, one more thing, you can do this exact same audit for your competitors to get a

wealth of insights on new topics and gaps that will be relevant to your business. Look at their branded queries to see what users and AI connect to them more strongly than they do to you. Sometimes those are quick wins. You might already have the features, you just haven't created the content that connects them to your brand, or you don't have enough pages that are talking about That feature. We've put together a template you can use to organize all of this and share it with stakeholders. So, I'll link that in the description. Now, you have your

baseline saved and a clear picture of where the gaps are. But knowing your gaps is only the first step. You also need to know what opportunities are out there. And keyword and prompt research for AEO is how you do it, but it's a bit different from what you might be used to. There are queries where AI dominates, queries where traditional SEO still wins, and a whole set of platforms most people aren't even thinking about. That's what we'll cover in the next lesson. I'll see you there. Hey, it's Ammo and welcome to the second lesson, which

is on keyword and prompt research for AEO. Now, in the last lesson, I walked you through how to run a brand gap analysis, so you should have a clear picture of where your brand is showing up and where it isn't. Now, it's time to find the keywords and prompts to actually go after. And in this lesson, I'm going to walk you through the full keyword research process for both SEO and AEO. Why? Because there's a ton of overlap between The two. And half the battle is really identifying which is which and knowing how to approach

them differently. So, the first step is to build your keyword list. And this part honestly hasn't changed that much. You still need two things. Seed keywords, which are broad terms related to your niche, and modifiers, which are add-ons like best or howto that turn those seeds into real searches. Now, a quick way to come up with these is to just ask your AI assistant of choice something like, I'm doing keyword research for my type of site, which makes money through my revenue model. My target audience is this group. Give me 10 seed keywords that are

one to two words max and five plus modifiers that will help me surface appropriate content formats I can use in my keyword research. The seeds and modifiers should not share the same words. And just like that, you've got a solid list of seeds and modifiers to work with, but these are just the starting point. Take your seeds and paste them into keywords explorer. Then go to the matching terms report and add your modifiers using the include filter. And just Like that, you should have hundreds, maybe even thousands of real keyword ideas your audience is actually

typing into Google. Now, the second step is to vet those keywords because some of them are a trap. Some of these keywords are going to look incredibly enticing. High volume, high traffic potential, low difficulty scores, all the right metrics, but not all of them are worth targeting. Before you commit to any keyword, it needs to pass three tests. I call this the bid formula. B is for business potential. Ask yourself, if I rank number one for this keyword, does it actually help my business? A keyword like what is espresso has solid volume and low difficulty,

but someone searching that isn't looking to buy anything, maybe ever. Compare that to best espresso machine under $500, where the searcher is showing intent and has a budget. Always choose keywords that move the needle. I is for intent. Google the keyword and look at what's actually ranking. If every top result is an e-commerce page and you're trying to rank a blog post, it's not going to happen. The SER tells you what searchers want. Match the intent or move on. And D is for difficulty. You need to choose keywords you actually have a chance at ranking

for. Check the referring domains and domain rating of the top ranking pages. In general, the more links and the higher the DR, the tougher the competition. If you see a few low DR sites in the top 10, that's usually a good sign. If a keyword passes all three tests, you should consider targeting it. Unless, and this is where step three comes in, the AI filter. Before you commit to any keyword, there's one more question you need to ask. Can AI fully satisfy the user for this query? Because even if a keyword passes bid, if the

AI overview is so good, there's no reason for anyone to click through, that keyword might be a trap. And there's hard data behind this. AI overviews appear on about 21% of all keywords, but for informational queries, it's much higher. Nearly 58% for question queries, 46% for queries with seven or more words, and 99.9% of keywords that trigger AI overviews areformational in intent. So before you commit to a keyword, Google it. Look at what shows up. Put yourself in the searcher shoe and ask, "Am I satisfied with This answer or do I need to click somewhere

to learn more?" If the AI overview nails it, that keyword might not be worth targeting the traditional way. But here's the good news. Even though AI is eating clicks for a ton offormational keywords, there's a whole category of searches AI hasn't touched. Free tools, search backlink checker, no AI overview, mortgage calculator, nothing. Word counter, nothing. Why? Because when someone searches for a tool, they need to actually use something. AI can't replace that yet. To find these opportunities, go back to keywords explorer with your broad seeds. Head to the matching terms report and add modifiers like

calculator, checker, generator, tool, template, finder, planner, and maker. These are all actionoriented queries where someone needs to do something. AI can't satisfy that yet. So the organic click is still there for the taking. You can also filter for transactional intent directly. Just choose transactional in the intent filter and you'll get queries where people are looking to buy, sign up or take some kind of action. Now the fourth step is to find your AI mention opportunities. A minute ago I said If the AI overview nails the answer, that keyword might not be worth targeting the traditional

way. But that doesn't mean you ignore it. These keywords just need to be targeted differently. And that's where AEO comes in. Instead of trying to rank and earn a click, your goal is to get your brand mentioned in the AI response itself. And to do that, you need to know which queries to focus on and which pages AI is pulling from. Now, it helps to know what AI is actually citing. We studied this across AI overviews and chat GPT and found that 43.8% of all cited pages are listicles. And in my opinion, these pages show

up so often because they help AI build consensus. So if your brand is mentioned across multiple lists, that's multiple sources recommending you and AI picks up on that. So to find the queries that matter for your brand, go to brand radar and enter your website. Find your brand, hover over the AI platform you want to research and click others only. This shows you that mention gaps where competitors are showing up but you're not. Then filter for queries containing best, top, versus, review, Or alternative. And you'll see the queries where AI is mentioning your competitors but

not you, and the ones AI is most likely pulling from when looking for brands in your space. This is your short list. Now, one thing to keep in mind, over 45% of citations change when AI overviews refresh, and that happens on average every 2 days. So, this isn't a one-time exercise. It's worth revisiting this report regularly to catch new queries as they come in. And that brings us to the fifth step, prompt research. When someone opens Chat GBT, Google AI mode, or Perplexity, they're not typing keywords. They're having conversations. They're saying things like, "I'm a

small agency owner looking for a marketing platform. Which one should I choose?" Or, "What's the best way to track rankings if I'm just getting started?" They use natural language with full context and so every person phrases it differently. So you can't approach this the way you would keyword research where you find the exact terms and go after them one by one. With AI, the same question asked 10 different ways can get 10 different answers with 10 different brands Mentioned. And on top of that, AI fans each prompt out into many sub queries behind the scenes,

most of which have zero search volume and will never repeat. If you're invisible for a topic, it's not about optimizing for one specific prompt. It's about building visibility across that entire topic, which is exactly what we'll cover in module three. So, with your topic list and your prompt gaps mapped out, let's go and close those gaps. I'll see you in the next lesson. Hey, it's Ammo and welcome to the third module in our AI search course. In modules one and two, we covered how AI search works and how to find the keywords and prompts to

go after. Now, it's time to actually execute. And that's what this module is all about. And in this first lesson, we're going to be talking about creating content that gets cited. And I've got some data on what AI actually sites to back our strategy. First, content length doesn't matter. We analyze over 174,000 pages cited in AI overviews and the correlation between word count and getting cited is just 0.04. That's basically zero. Over half of all cited pages, 53.4% are under a,000 words. So, if you've been writing 3,000word articles because you think longer content ranks better,

that doesn't apply here with AEO. AI doesn't care how long your page is. It cares whether your page answers the question. Second, freshness matters a lot. Content cited by AI is on average 25.7% fresher than what ranks in traditional organic results. And when you look at chat GBT specifically, 89.7% of its top cited pages were updated in 2025. And get this, 76% were refreshed within the last 30 days. Think about that for a second. If your content hasn't been touched in 6 months, you're already at a disadvantage for AI citations. Now, this doesn't mean you

should just change the publish date and call it a day. Google can detect that. You need to make meaningful updates to the actual content. We'll talk about how to do that later in this lesson. And the third data point is that format matters. If you remember from the last module, we talked about how 43.8% of all cited pages by chatbt are listicles. Best X, top X, comparisons, reviews. That's not a coincidence. These formats are built for AI because they give Clear, structured recommendations that AI can pull from. But lists aren't the only format that work.

Datadriven content with original stats also get cited heavily because AI loves citing specific numbers. And comparison content like X versus Y pages perform well because they map directly to how people ask AI questions. So, the key takeaway here is simple. Keep your content fresh, keep it focused, and lean into formats that AI can actually use. Now, let's talk about how to actually structure your content so AI can use it. And here's something a lot of people get wrong. They think they need to write differently for AI. Like, there's some special AI optimized format they need

to follow. There isn't. You're writing for humans. AI is trained on what humans find valuable. So content that serves human readers well is content AI wants to site. That said, there are a few principles that help both humans and AI get more out of your content. The first one is bluff, which stands for bottom line upfront. This one's borrowed from military communication, and it's simple. Start every section with the answer, not the backstory. For example, instead of opening a section with, "Over the past few years, link building strategies have evolved significantly due to changes in

how search engines evaluate link quality," you could write, "The most effective way to build backlinks in 2026 is to create original research." Now, here's why this matters. When humans scan a page, they follow what's called an F pattern. They read the beginning closely, skim the middle, and maybe pick up again at the end. LLM show a similar pattern. They weigh the beginning and end of a passage more heavily than the middle. So if your key point is buried three paragraphs into a section, both humans and AI might miss it. So put the answer first, then

support it with context and examples. The second principle to apply is atomic content. This means every section on your page should be able to stand on its own. So, when editing your content, ask yourself, if AI pulls just one section from your article, does it still make sense, or does it depend on context from three paragraphs earlier? This matters because AI systems chunk your content into pieces when they process it. Different AI models chunk content differently, and you can't control how they do it. But if every section is self-contained, it doesn't matter where the chunks

fall because the meaning survives. A good test you can do is to take any H2 section on your page and read it completely out of context. If it doesn't make sense without the rest of the page, rewrite it so it does. The third principle is entity rich writing. AI understands text by looking at entities and the relationships between them. Entities are things like brands, products, people, places, and specific concepts. So instead of writing this tool helps with SEO, write HF's keywords explorer helps you find keywords with low difficulty and high traffic potential. By providing more

entities, relationships, and context, AI has more to work with. And this also ties into how AI picks what to site. The more specific and concrete your content is, the more useful it is when AI is trying to answer a specific question. And the fourth principle is to keep it simple and declarative. Use short sentences and clear subject verb object structure. Basically, try to keep things to one Idea per sentence. This isn't about dumbing down your content. It's about making it easy to parse for both people and AI. If a sentence takes two reads to understand,

it's probably too complex. Simplify it. Your readers will thank you and AI will have an easier time extracting the key points. Now, these four principles will help you create content that's more likely to get cited. But there are a couple more things worth knowing that can push your chances even further. And the first is something most people don't think about. LMS have a tendency to flatten originality. Like if you come up with an original concept or framework and you're the only one talking about it, AI will often absorb the idea without crediting you. It becomes

part of its general knowledge. The way around this is to label your ideas with your brand name. For example, instead of calling something a content scoring matrix, call it the HF's content scoring matrix or your brand content scoring matrix. Define it explicitly and then distribute it widely across your blog, social media channels, podcasts, Reddit, etc. The more places it shows up with your name attached, The harder it is for AI to flatten it into generic knowledge. Now, another thing you can do is refresh what I call sleeper pages. These are pages on your site that

used to rank well but have slowly declined over time. They already have the back links. They already have the authority. They just need a refresh. And because freshness is such a strong signal for AI citations, updating these pages can be one of the fastest ways to gain AI visibility. Here's how to find them. Go to site explorer and enter your domain and head to the top pages report. Then sort by traffic change and look for pages with significant declines. What you're looking for are pages that have two things. A decent number of backlinks, which means

they already have authority, and a clear traffic decline, which usually means the content has gone stale. Now, before you update anything, make sure it's actually a content issue and not a backlinks issue. If the page never had many referring domains to begin with, updating the content probably won't help. But if it has links and the content is just outdated, that's a high potential opportunity. So now you know how to Create and update content that AI actually wants to site. But creating great content is only half the equation. You also need to get mentioned on other

people's content, the pages AI is already pulling from. And that's exactly what we'll cover in the next lesson. I'll see you there. Hey, it's Samo and welcome to the second lesson in this module, which is on earning mentions and citations. Now, in the last lesson, we talked about creating content that gets cited. But here's the thing. A lot of AEO isn't about what's on your site. It's about getting mentioned on other people's pages. And not just any pages, the ones AI wants to pull from. Branded web mentions are so important that in our study of

75,000 brands, they had the strongest correlation with visibility in Google's AI overviews, stronger than backlinks, referring domains, and even domain rating. So, in this lesson, I'm going to show you the three types of pages you should be getting mentioned on and how to find the ones that matter most. Let's get started. So, I like to think about mention sources in three tiers. And the reason I use tiers is because not all mentions are created equal. Where you get mentioned matters just as much as how often you get mentioned. According to our data, we found that

getting mentioned on highly linked pages has a 0.7 correlation with appearing in Google's AI overviews. That's even higher than the general branded mentions correlation. So, the quality of the page you're on makes a huge difference. Now, let's talk about tier one mentions, which are found in third-party editorial content. These are the hardest mentions to earn, but they're also the most valuable. Think industry publications, review sites like Wire Cutter or Techraar, listicles and comparison posts on authoritative blogs, and YouTube reviews from creators in your space. Why are these so powerful? because they're exactly the kind of

pages AI loves the site. Remember, 43.8% of Chat GPT citations are listicles and comparison pages. So, if you're mentioned on those pages, you're in the pool of content AI is already pulling from. Now, to find the right pages to go after, go to brand radar and enter your domain. Then, open the cited domains report. This shows you the top websites that AI is citing for topics Related to your brand. You'll see the number of AI responses, the number of pages, and how often each brand is being mentioned. From here, you can identify which sites matter

most for your niche. Maybe it's kbb.com for cars or cnet for tech or specific niche blogs in your industry. These are the sites where getting a mention can directly lead to AI visibility. Now, it's important to note that you don't have to wait for AI to start citing a page before you try to get on it. Instead, look for pages that already have a lot of links and cover your topic. Even if they're not cited now, there's a good chance that they'll be cited at some point. You can use content explorer for this. Search for

your title and add title colon best or title colon versus along with a minus operator before your brand name. This will surface listicles and comparison posts in your niche where your brand isn't mentioned. Then filter for pages with a good number of referring domains. Next up are tier 2 mentions which can be found on user generated content and community platforms. This includes Reddit, Quora, niche forums and community sites, And these matter more than you might think. Reddit is one of the most frequently cited sources by Chat GBT. It's also one of the foundational training sources

for large language models. The play here isn't to spam Reddit threads with your brand name. That'll backfire fast. Instead, find threads where people are asking questions your product or expertise can genuinely answer. If someone's asking what's the best tool for X and your product is actually a good fit, contribute a real answer. You can find these threads through brand radar as well. Look at the cited domains report and see if reddit.com shows up for your space. If it does, dig into which specific threads AI is pulling from and join the conversations that are already happening.

Now, if you want to find niche specific Reddit pages that are already ranking well in Google, you can go to site explorer, enter reddit.com, and open the organic keywords report. Filter for keywords ranking in the top five, and add your niche terms to the include filter. And now you've got a list of Reddit pages that are already ranking well in Google and are related to Your space. And you can do the exact same thing for niche forums and Q&A sites. Wherever your target audience is having conversations about problems that you solve, that's where you want

to be present. Finally, we have tier three, which are your own properties. Some brands own multiple domains. At for example, has detail.com and blogger genen. If these properties are authoritative in their own right, they can serve as additional citation sources for AI. Now, even though you don't have the resources to run multiple sites, the principle still applies. Your YouTube channel, your podcast, your LinkedIn content, these are all indexed and can all show up as sources AI pulls from. Basically, the more places your brand appears in a positive topically relevant way, the more training examples AI

has to learn from. Now, earning mentions is one thing, but you also need to keep track of them because mentions can disappear. pages get updated, lists get refreshed, and your brand can get removed without you ever knowing. And on the flip side, sometimes AI picks up wrong information about your brand from outdated or inaccurate sources. So, it's worth doing a regular mention audit. In Brand Radar, you can track your overall mention trends over time. Look for any drops in mention volume. If you find a page that was previously mentioning you, but your brand was removed

during an update, that might be worth investigating. And pay attention to sentiment, too. Are AI responses about your brand accurate? Are they positive? If you spot misinformation, your best move is to update your own content first and then reach out to the publisher to request a correction. The faster you fix inaccurate mentions, the less time AI has to learn from them. So, between this lesson and the last one, you've got a clear playbook for earning AI visibility through content and mentions. But there's one platform we haven't talked about yet that deserves its own lesson, and

that's YouTube. I'll see you in the next lesson. Hey, it's Ammo and welcome to lesson three, which is on YouTube optimization for AI visibility. Now, in the last two lessons, we covered how to create content that gets cited and how to earn mentions on other people's pages. But there's one platform that deserves its own conversation, and that's YouTube. Why? Because YouTube is the most cited domain in Google's AI overviews. And get this, according to our data, YouTube mentions have a 0.737 correlation with chat GBT visibility. That's the strongest correlation of any factor we study. And

there's a reason for that. GBT4 was trained on over a million hours of YouTube transcripts. So YouTube isn't just a platform AI sites, it's a platform AI learns from. So, in this lesson, I'm going to walk you through a three-step process to get your YouTube videos in front of AI. Let's get started. So, the first step is to find what's already working on YouTube in your niche. And the key idea here is that you want to target what I call search hits rather than viral hits. A viral hit can get you a spike of views

and it can keep spreading based on a YouTube user's interests. But once the YouTube algorithm has exhausted the interested people, it dies. a search hit. It gets you consistent traffic from both Google and YouTube search month after month because people are actively searching for that topic. And search hits are exactly the kind of videos AI is Likely to pull from. And that's because if Google is already ranking a YouTube video for a keyword, there's a good chance AI overviews will site it, too. On top of that, titles for search videos tend to be very clear

about what the video is about, whereas viral hits, not so much. So, here's how to find these topics. Go to site explorer and enter www.youtube.com/watch. Then, open the organic keywords report. This shows you every keyword that YouTube videos are ranking for in Google. Now, filter for keyword rankings in the top three and add your niche terms to the include filter. What you'll get is a list of topics where YouTube videos are already ranking at the top of Google and are related to your space. Now, let's move on to the second step, which is to actually

create videos that rank. Once you found your topics, you need to make sure your video is set up to rank in Google. And there's a checklist I follow for this. First, your title needs to contain the keyword people are searching for. This isn't the place for clever or clickbaity titles. If the keyword is how to use Google Docs, that should be right in your title. Save the creativity for the thumbnail. The title handles the keyword. The thumbnail sells the click. Second, your description needs to be a real summary of the video. Just write a summary

of what your video covers and add your target keyword in the first couple of lines. Google reads this, AI reads this, and viewers read it, so make a count. Third, add timestamps. Timestamps turn into YouTube chapters, and those chapters can show up in Google for specific queries. So, if your video covers five tips and someone searches for tip number three, Google can link directly to that chapter. It's basically free extra visibility for like 2 minutes of work. Fourth, say the keyword in your video. And this one's important. Google understands audio. Liz Reed, who's VP of

search at Google, has said that Google can understand audio content and video content. So, if your video is about the best protein powder for repair, actually say those words in the video. Don't put it in the title and hope for the best. And fifth, match the format to what's already ranking. If tutorials are dominating the search results For your keyword, make a tutorial. If listicles rank, make a listicle. Don't fight the format. Look at what's working and match it because you're matching the intent of the searcher. Now, none of this is complicated, but it's the

difference between a video that gets buried and a video that shows up every time someone searches for that topic. And that brings us to the third step, which is to layer in AI visibility. So, at this point, you've got a video that's optimized for Google search. But here's where you take it a step further for SEO. Go to Brand Radar and enter a popular brand or YouTube channel and go to the topics report. Then set a filter where the domain mentioned is YouTube.com. Now, you can see exactly which queries AI is pulling YouTube videos into.

So, go and create content around those topics. And as for how to actually rank there, it comes back to what we already covered, pick the right keyword, make a thorough, well optimized video, and be comprehensive without wasting people's time. And here's one more thing to keep in mind. Remember, every video you publish on YouTube is potentially training data for AI models. So even if a video doesn't get cited right away, the content is being absorbed. The more helpful, specific, and well ststructured your videos are, the more likely AI is to learn from them and eventually

recommend them. So, that covers content mentions, and YouTube, but there's a technical side to AEO that most people skip entirely. Things like structured data, robots.txt, and making sure AI can actually access your site in the first place. And that's what we'll cover in the next lesson. I'll see you there. Hey, it's Samo and welcome to the fourth lesson in this module, which is on the technical side of AEO. Now, I know technical can sound intimidating, but this lesson isn't about rewriting your site's code. It's about making sure that AI can actually access and understand your

content. And while access and understand might sound rudimentary for some of you, the reality is a lot of sites are accidentally blocking AI without even knowing it. According to our data, around 5.9% of 140 million websites are blocking GBTO, which is Open AI's crawler. That's millions of sites that are invisible to ChatGBT. So, in this lesson, I've Got six technical checks and tips for you to make sure AI can find you so that it can promote you. Let's get started. So, the first thing you need to check is your robots.txt file. Robots.txt txt is a

file on your site that tells crawlers what they can and can't access. And the thing is, it's not just Google's crawler you need to think about anymore. There are now dozens of AI specific bots that crawl the web. The main ones you should know about are GPTbot and OAI searchbot from OpenAI, Claudebot from Anthropic, and Google extended from Google. If any of these are blocked in your robots.txt, txt, you're asking those AI platforms not to crawl your content. And assuming they obey your rules, they sure won't be recommending your pages then if they don't know

what's on them. Now, you might not have blocked these bots intentionally, but a lot of sites inherit robots.txt rules from templates or old configurations, and some platforms add blocks by default. For example, Cloudflare has a feature called instruct AI bot traffic with robots.txt. That's now enabled by default. When this is on, Cloudflare automatically updates your robots.txt To signal that your content shouldn't be used for AI training. So, if your site is on Cloudflare, you could be blocking AI crawlers without even realizing it. So, the first step is simple. Go to your doommain.com/root.txt and look for

any lines that mention GPTbot, Claudebot, Google extended, or OAI searchbot. If you see a disallow rule next to any of those, you're blocking that AI crawler. You can also use HF site audit to check this. Run a crawl on your site and it'll flag any robots.txt rules that might be blocking AI crawlers. Now, while we're on the topic of files AI reads, I want to make a quick note on something you might have heard of called LLM.txt. This is a proposed standard kind of like robots.txt, but specifically designed to tell AI systems about your site.

The idea is that you create a file atyoudommain.com/lms.txt that gives AI a summary of who you are, what your site covers, and where to find your most important content. It's useful in theory, but as of right now, no major LLM provider officially supports it. OpenAI doesn't use it. Anthropic publishes one on their own site, but hasn't confirmed their Crawlers actually read it, and Google hasn't adopted it either. So, should you create one? Well, I don't think it'll hurt you, but I wouldn't prioritize it over the other things we've talked about in this lesson. Robots.txt is

still the file that actually matters most right now. All right, the second thing to check is how your site handles JavaScript. Some AI platforms can render JavaScript and some can't. Without getting too technical, Gemini and Copilot can render JS while ChatGpt's crawler does not. So if your content relies on JavaScript to load, which is common with single page apps and some React or Angular frameworks, ChatGBT literally can't see your content. It visits the page and gets an empty shell. The fix here is serverside rendering, which means your server sends the fully rendered HTML to the

crawler instead of relying on JavaScript to build the page in the browser. If you're already doing this for SEO, you're covered. If not, it's worth looking into, especially if AI visibility matters to you. A quick way to test this is to disable JavaScript in your browser and visit your own site. If the content disappears, you have a JavaScript rendering issue that's affecting AI crawlers, too. The third thing to consider is page speed. Now, you might be thinking, page speed is an SEO thing, not an AEO thing, but it can actually matter more for AI retrieval

than for traditional search. When AI systems retrieve information in real time, they're fetching, parsing, and chunking your pages on the fly. And if your page takes too long to load, it can get dropped before it's even scored. So, it won't be making it into an AAI response, even if the content is great. The good news is that if you've already optimized your core web vitals for SEO, you're most of the way there. Fast loading pages with clean HTML benefit both Google and AI systems. And that brings us to the fourth tip. Create clean HTML structure.

This one's straightforward. AI systems parse your content by following your HTML structure. So, if your headings are logical, your sections are well organized, and your paragraphs are focused on one idea each. AI has an easier time extracting the right information. This ties directly back to The content principles we covered in lesson 3.1, bluff, atomic content, and entity rich writing. Those principles aren't just about writing style. They're about making your content technically parsible for AI. So, when you're structuring your pages, use proper heading hierarchy. H1 for the title, H2s for the main sections, and H3s for

subsections. And make sure each section can stand on its own because AI might chunk your content at any heading boundary. The fifth tip is about schema markup. Schema markup, which is also called structured data, is code you add to your pages to help search engines understand your content. Things like article schema, FAQ page, how-to, and local business. Now, does it help with AEO? Honestly, the evidence is mixed. There's no confirmed data that adding schema directly improves your chances of being cited by AI, but it doesn't hurt. And if you're already using it for SEO, there's

no reason to remove it. I wouldn't spend a ton of time on schema specifically for AEO, but if you're setting up a new page, adding the right schema types is a good habit that makes your content easier for any system to understand. All right, the sixth tip that I have for you is to optimize for AI hallucinated URLs. AI assistants sometimes make up URLs that don't exist on your site. They'll recommend a page to a user, the user clicks it, and they hit a 404 error. And this happens a lot more often than you'd expect.

According to our data, AI assistants send visitors to 404 pages 2.87 times more often than Google search does. And Chat GPT is the biggest offender with about 1% of its clicked URLs leading to 404 pages. Now, rather than letting that 404 be the end of a visitor's browsing journey, you should either fix or optimize those pages to get more out of them. You can do that by checking your analytics for pages that are getting traffic from AI referers but returning a 404 status. If you spot a hallucinated URL that's getting consistent traffic, set up a

redirect to the most relevant real page on your site. That way, you're capturing traffic that would otherwise be lost. Now, while creating content and getting cited is a big part of AEO, it's only part of the picture. You also need to know if it's actually working. And that's exactly what we'll be covering in Module four, which is all about measuring and tracking your AI visibility. I'll see you there. Hey, it's Samo and welcome to module four in HF's AI search course. Now, in module three, we covered how to create content, earn mentions, and optimize YouTube

videos to get mentioned and cited in AI search. And we also talked about how you can optimize for the technical side of AEO. So, now you've got a playbook for execution. But here's the thing. None of that matters if you can't measure whether it's working. And that's what this module is all about. Now, I'll be upfront with you. Measuring AI visibility is a lot harder than measuring results from traditional SEO. With SEO, you've got Google Search Console giving you impressions, clicks, and rankings. With AI search, a lot of that data either doesn't exist or is

hidden from you. But that doesn't mean you're flying blind, because there are three ways to track your AI visibility. And in this lesson, I'm going to walk you through how to set each one up. Let's get started. So, the first thing you should track is AI referral traffic to your site. This is when Someone clicks a link from Chat GPT, Perplexity, Claude, or another AI platform and lands on your site. In analytics tools like Google or HF's web analytics, it'll show up as a referral visit. This is helpful in knowing how many people are finding

you because of AI. But here's where it gets tricky. Not all AI platforms pass referral data properly. Some of them strip it out entirely, which means the visit shows up as direct traffic in G4. And if it does that, then there's no real way to know that the traffic came from AI. For example, ChatGpt source links in search results pass referral data properly. But incontent links on paid accounts use a no referral attribute, so those visits are actually invisible. Claude tracks properly. Perplexity tracks on the web but not on its desktop app. Copilot tracks on

the web but not on Windows. And Grock doesn't pass referral data at all. So the key takeaway here is this. Use AI referral traffic to understand the general trend of which AI platforms are sending you customers and visitors. But understand that what you see in your analytics is likely an undercount. Now with that caveat out of the way, Let me show you how to set this up. If you're using G4, you'll want to create a custom channel group that isolates AI traffic. Go to admin, then data display, then channel groups. Copy your default channel group,

and add a new channel called AI traffic. Then set the source to match a reax that includes chatgpt.com, perplexity, gemini.google.com, copilot.microsoft.com, claw.ai, and deepseeek.com. Once that's set up, go to reports, then acquisition, then traffic acquisition, and select your new channel group. Now you can see the traffic AI is sending you, the pages it's directing people to, and how those visitors behave compared to other channels. Now, if you want something simpler than G4, Hrefs has a free tool called Web Analytics that does this automatically. It has a built-in AI search channel, so you don't have to

set up any custom groups. It also separates unknown traffic from direct traffic, which J4 doesn't do. That means you can get a clearer picture of where your traffic is actually coming from. All right, so once you've got AI traffic set up, there are two things I'd pay attention to. First, Look at which pages are getting AI traffic. These are the pages AI is already recommending to users. Make sure you keep these up to date, keep them accurate, and have clear calls to action. If AI is sending people to a page that hasn't been updated in

a year, it'll probably stop sooner than you'd like. And second, look at your important pages that aren't getting AI traffic. If you've got a key product page or a highv value blog post that's getting zero AI referrals that should be getting it, that's worth investigating. It could be a content issue, a crawling issue, or it could just mean AI isn't surfacing that topic yet. The second thing to track is AI bot activity on your site. This one's a bit different. Instead of tracking the humans who click through from AI, you're tracking the AI bots themselves,

the crawlers that visit your site to read and index your content. And here's something most people don't realize. AI bots visit your pages far more often than humans do. So, the pages they're hitting the most are likely your strongest citation candidates. Now, there are two types of AI bots to know about. The first type is training bots like GPTbot and Google Extended. These take your content and use it to train AI models. The second type is search and citation bots like ChatGpt user and OI search bot. These fetch your pages in real time when a

user asks a question. These are the ones that can actually drive referral traffic to your site. To track bot activity, you can use server logs if you have access to them. But the easier way is through HF's bot analytics, which has a Cloudflare integration that shows you exactly which AI bots are visiting your site, how often, and which pages they're focusing on. And this works with a free Cloudflare plan, too. Now, what you're looking for here are patterns. If a citation bot is hitting a specific page repeatedly, that page is likely being used as a

source in AI responses. And if there are important pages that bots aren't visiting at all, that could mean they're hard to discover, which ties back to the internal linking and site structure we talked about in module 3. All right, the third thing to track is self-reported attribution. This one's the simplest to explain, but it might be the most important for proving the value of AEO to your team And to your clients. You see, a lot of the impact of AI visibility doesn't show up in your analytics at all. Someone asks ChatGBT for a recommendation. They

get your brand name, then they go to the browser bar and they type it directly in there. That shows up as direct traffic or they Google your brand name after hearing about you from AI. That's going to show up as organic search traffic. So the only way to capture this is to ask people directly. Add a how did you hear about us question to your signup flow, your checkout process or your post purchase survey. include options like AI assistant, chat GPT, Perplexity, etc., and AI search like Google AI overviews. At Hrefs, around 3% of our

conversions came from AI over the last year based on self-reported data. And our AI visitors convert at a much higher rate than organic search visitors. But we would never have known that without asking. So, if you only do one thing from this lesson, add that question to an entry survey. It's the most direct way to connect AI visibility to actual business results. Now, these three pillars work best when you use them together. Referral traffic tells you What AI is sending to your site. Bot analytics tell you what content AI is paying attention to. And self-attribution

tells you what's actually driving revenue. No single source gives you the full picture, but together they give you a pretty clear view of how AI is interacting with your brand and where you should focus your efforts. And on top of these three pillars, you've still got brand radar tracking your AI visibility across platforms, which I showed you how to do back in module 2. So now that you've got your analytics set up, the big question is, is all of this actually worth it? What's the ROI of AEO? and what should you actually be doing on

a weekly and monthly basis to keep growing your AI visibility? That's what we'll cover in the next and final lesson. I'll see you there. Hey, it's Ammo and welcome to the final lesson in HF's AI search course. Now, throughout this course, we've covered everything from how AI search works to how to find the right keywords and prompts to creating and optimizing content, how you can earn mentions, and even how to set up your analytics for AEO. But there are three questions I haven't answered yet That are super critical to know. Is AEO actually worth it?

How do you know if you're making progress? And what should you actually be doing on a regular basis to keep growing? So that's what this lesson is about. Let's get into it. So let's start with a big question. Is AEO actually worth your time? Now I'll be honest. If you look at raw traffic numbers, AI search is still relatively small. According to our data, AI referral traffic accounts for about 0.25% of a site's total traffic on average. And Google still sends about 210 times more traffic than the top AI platforms combined. So, if you're looking

at this purely from a traffic standpoint, you might think it's not worth it. But here's where the story gets interesting. At Hrefs, our AI visitors convert at 23 times the rate of organic search visitors. And we're not alone. Verscell is seeing 10% conversion rates from AI traffic. Tally says AI is their largest acquisition channel and helped boost their ARR by a million dollars. Think about that for a second. The traffic volume is small, but the quality of traffic is significantly higher. And the reason for that is simple. When AI recommends you, It's already explained to

the user why you're a good fit. So the traffic arrives prequalified. They're not browsing. They're ready to act. And on top of conversion quality, AI traffic is growing fast. It's grown about 9.7 times since last year. Chat GPT alone has grown 85% since January, it now sends more traffic than Reddit or LinkedIn. So, while the numbers are still relatively small today, the trajectory, it's pretty clear. AI traffic is only going to grow from here. But here's what I think a lot of people miss about AEO. The real value isn't just the clickable traffic. It's the

brand awareness that happens inside the AI conversation. Every time AI recommends your product or mentions your brand, that's an impression you never had before. And most of those impressions never result in a click to your site. They result in someone googling your brand name later or engaging with your brand on social media when they see it in their feed because they recognize it. So, the way I think about it is this. AEO isn't an alternative to SEO. It's a new layer on top of it. And the brands that build that layer now, while it's still

early, Are going to have a massive advantage as AI search continues to grow. Now, the main downside, which we talked about in the last lesson, is that it's not perfectly measurable. But just because you can't measure something precisely, it doesn't mean it's not working. It's kind of like brand marketing. You can't track every billboard to a sale, but you know it shapes how people think about you. AEO is the same way. So, how do you know if you're making progress? Well, if you followed along in module 2, you set up your brand radar baseline and

did your first brand gap analysis. Now, it's time to go back and check how things have changed. Pull up your brand radar dashboard and look at four key things. First, AI share of voice. How have you moved relative to your competitors? If your share has grown, your efforts are working. If it stayed flat while a competitor has grown, you need to dig into why. At HFS, we're tracking queries and prompts at scale. So, brand radar, it gives you a really good look at the overall trend. Second, look at cited domains. Are new domains citing you

that weren't before? This tells you If your mention earning efforts from module 3 are paying off. Third, topic coverage. Have you closed the gaps you identified? Are there new topics where you're showing up where you weren't before? And fourth, mention sentiment. Is AI saying accurate and positive things about your brand? This is especially important because AI doesn't just repeat what you put on your site. It synthesizes information from everywhere. I'd recommend doing this as a quick monthly check and do a deeper competitive audit quarterly. Now, while we're on the topic of what AI says about

you, there's something important that you need to be aware of. AI is vulnerable to misinformation. And I don't mean it in a theoretical way. We actually tested it. We created a completely fake luxury brand, planted three contradicting sources about it across a blog, Reddit, and Medium, and then asked eight different AI platforms about the brand. The results were pretty alarming. After the fake sources were planted, Gemini and Perplexity repeated the misinformation in 37 to 39% of their answers. They cited fake founders, fake cities, fake pricing stories, all presented as verified Facts. Now, the good news

is that Chat GPT was much more robust. It stayed under 7% and cited the brand's official FAQ in 84% of its answers. But the point is that not all AI platforms are equally resistant to bad information. So, what does this mean for you? It means you need to fill every information gap about your brand with specific and official content. Create an FAQ that directly addresses common questions. Use specific numbers and dates and facts, not just vague claims. Because when AI has to choose between vague truth and specific fiction, it tends to choose the specific fiction.

Also, monitor what AI is saying about you regularly. If you spot inaccurate information, the fastest fix is to publish content on your own site that directly contradicts it and then work on getting the third-party source corrected. All right, so let's bring this all together with your AEO action plan. If you're wondering what to do first, here's what I'd focus on this week. Check your robots.txt for AI bot access. This takes about 5 minutes max, and it's the most common technical blocker. Make sure AI can crawl your site. Next, Set up your AI analytics. Create the

AI traffic channel in G4 or set up HF's web analytics. Add a how did you hear about us question to your signup or checkout flow. Start measuring from day one so you can see how you progress over time. Next, update your most important pages for freshness. Remember, AI content is 25.7% fresher than what ranks in traditional search. Pick your top five to 10 pages and make meaningful updates, new stats, updated examples, current information. Next up, run your brand gap analysis. Set up brand radar if you haven't already, and go through the process we covered in

module two. Know where you stand before you start optimizing. And finally, identify your top 10 mention earning targets. Use the cited domains and pages reports in content explorer to find the pages where getting a mention would have the biggest impact on your AI visibility. And then on an ongoing basis, do a monthly brand radar check to track your progress in a quarterly competitive audit to catch bigger shifts and just rinse and repeat. Everything I just mentioned is covered step by step in this course. So if you need a refresher On any of it, just go

back to the relevant lesson and then execute. AI SEO or AEO is still early. The tools are evolving, the data is getting better, and the opportunity is only going to grow. So the biggest advantage you can have right now is simply starting before everyone else does. Thanks for watching our AEO course and I'll see you in the next