Here’s an AI search secret: the brands getting cited the most by AI tools have trained them to do so.
Does that mean they had a hand in developing training data for tools like ChatGPT?
Not at all.
What it does mean is that they’ve trained LLMs (large language models) to closely associate their brands with topics related to their products and services.
As an example, imagine that you provide a fintech SaaS product on your website.
Training the AI would mean that when a user prompt contains terms related to fintech, your brand’s website (and content) is one of the first places that LLMs check to generate an answer.
In other words, when LLMs think about fintech, they think about your brand.
That’s the type of training that we’re talking about here.
Writing for AI, on the other hand, is more akin to on-page SEO. It entails adding things like schema markup and semantic HTML to make your content as easy as possible for LLMs to cite and retrieve.
Yet, making your content easy to cite doesn’t mean that LLMs will want to cite it.
Training the AI is where you make optimizations to get LLMs to associate your brand with certain topics. This will actually improve your visibility on AI search tools and lead to more citations and recommendations.
How can you train AI tools to favor your brand?
Keep reading to find out!
Here’s what we’ll cover in this article:
Writing for AI versus training AIs
How you can train LLMs to associate your brand with your industry
Creating influence through an airtight content strategy
How AI Systems ‘Learn’ From Your Content
First, let’s briefly discuss how LLMs pull information and ‘learn’ from online content. That will make the distinction between writing for an AI and training an AI even clearer.
AI chatbots and search assistants (Claude, ChatGPT, Perplexity) aren’t able to crawl and index the internet the way search engines like Google do.
Because of this, LLMs use things like web scrapers, plugins, and APIs (application programming interface) to access content that’s already been indexed by engines like Google and Bing.
They also prefer machine-readable sources, meaning websites that have things like semantic HTML and schema markup in place.
How do they determine which content is relevant, trustworthy, and worth citing?
LLMs don’t use the link graph (i.e., a graph of all the interlinked websites online), so they have no concept of a PageRank-style authority system. This is why metrics like Domain Authority (DA) and Domain Rating (DR) are ineffective for AI SEO.
This AI Overview provides some insight:
Backlink authority influences traditional search rankings, which feed the ecosystem that LLMs draw from, so backlinks still have an indirect influence on AI search visibility.
The trust signals that directly influence LLMs are:
Brand mentions
Topical authority
Topical relevance
Freshness
Content quality
User sentiment and social proof
Content that exhibits these signals is more likely to appear in AI-generated summaries, overviews, and recommendations.
AIs are able to learn which brands to trust through repeated exposure to these signals.
That brings us to the concept of ‘training AIs’ to become brand advocates.
What ‘Training the AI’ Really Means
The trust signals mentioned above also serve as AI training signals.
The more you include these training signals in your content, the more you’ll train AI tools and LLMs to view your brand as relevant and authoritative.
Ideally, you want to become a go-to source for LLMs generating prompts related to your products and services.
The way to do that is to generate powerful brand mentions, develop topical authority, and touch on the other trust/training signals that we mentioned.
Speaking of that, let’s take a closer look at each training signal.
Brand mentions
According to research, brand mentions are an extremely important trust signal to LLMs (along with branded search volume and branded anchor text).
Since LLMs are able to infer meaning and context, they look for branded web mentions when evaluating sources to cite.
If a brand is mentioned on lots of authoritative websites and publications (both mainstream and niche appropriate), it’s more likely to get mentioned by LLMs.
As an example, let’s consider two fictional domains in the fintech niche.
Domain A has brand mentions on The Financial Times, American Banker, and the fintech section of Venture Beat.
Domain B has brand mentions on numerous news sites with high DA scores, but they don’t cover fintech or finance.
Because of the irrelevance (remember, LLMs don’t consider DA or DR scores), Domain A is far more likely to get cited by AI search tools like ChatGPT.
Here’s proof:
Remember, they will not only pay attention to the brand mention itself, but also the surrounding text to understand the context.
As a result, you need to generate brand mentions that are:
Relevant – Your mentions should appear on websites related to your industry
Positive – Other websites need to talk about your brand in a positive light
Authoritative – Things like direct recommendations (brand A offers the best X product) and links to helpful resources (free tools, guides, etc.)
Topical authority
While LLMs don’t consider domain authority, they DO pay attention to topical authority.
If a domain consistently covers the same topic with detailed, high-quality content, there’s got a strong chance that AI search tools will cite it.
In fact, there are times when LLMs prefer to cite sites with topical authority over mainstream ‘legacy’ websites. For instance, if a site has built topical authority through consistent content in the fintech industry, a tool like ChatGPT might cite it over a site like Wikipedia.
Ways to build topical authority include:
Creating content clusters – A content cluster consists of a pillar page (an overview of the main topic) and cluster pages (posts that tackle related subtopics). To learn more, this guide contains helpful information on topic clusters.
Multi-channel content – Instead of just blogging, make your clusters multi-channel by including videos, infographics, and social media posts.
Expert interviews – Interviewing experts in your field signals credibility to LLMs, search algorithms, and target audiences. As a bonus, including Author and Contributor schemas will get LLMs to associate the experts you interview with your brand.
Original research – One of the best ways to build topical authority and generate backlinks is to publish original research in your field. Whenever websites in your field mention or link to your research, the cross-citation is a trust multiplier.
Topical relevance
Relevance is one of the most important deciding factors for LLMs when citing content.
As a result, irrelevant backlinks won’t improve your visibility.
That means your future link-building campaigns should focus on relevance above all else. Ensure that you guest post on related websites, appear on topically aligned websites, and get mentioned on relevant news sites.
Freshness
AI search tools want to provide the most up-to-date information for users, so they prefer sources that regularly update their content.
Ways that you can signal freshness to LLMs include:
Publishing content on a consistent schedule (like every Monday and Wednesday). AI tools can pick up on this rhythm.
Including schemas like dateModified or datePublished
Dynamic content blocks that say things like “last updated September 2025”
Content quality signals
The quality of your content matters now more than ever.
Google’s Gemini, which is responsible for AI Overviews, is trained on the company’s E-E-A-T quality rater system.
It stands for experience, expertise, authoritativeness, and trustworthiness.
Other LLMs, like ChatGPT and Perplexity, don’t use the E-E-A-T system outright, but they do measure extremely similar signals.
You’ll want to ensure that your content has:
Demonstrable experience and expertise (first-hand insights)
Author credibility and credentials
Topical authority
Trust and accuracy signals (consistent facts, disclaimers where appropriate, etc.)
User sentiment and social proof
Lastly, LLMs consider the user sentiment behind a domain before deciding to cite it. They check community forums like Reddit and Quora to see what people are saying.
Social proof, such as reviews, is massively important, too. AI search tools won’t recommend or cite brands with abysmal reviews, so reputation management is a must.
The more you include these types of signals in your content, the more LLMs ‘learn’ to trust your brand and associate it with relevant topics.
The Role of Structured Content and Backlinks
Next, let’s discuss making your content machine-readable by using structured data in your content.
This is writing for the AI, which is also an important aspect of the AI SEO equation.
When optimizing for AI search, you should both train AIs AND write for them to achieve the best results. That means producing content that contains training/trust signals but also formatting it in a way that LLMs can quickly parse and cite.
Think about it this way:
Training the AI is about getting LLMs to trust your brand and cite your content often.
Writing for the AI is about making your content machine-readable so that LLMs are more likely to parse, understand, and cite it.
Each requires the other to function properly. If you only write for the AI and don’t train it, LLMs won’t trust your brand enough to cite your content, even though it’s easy to parse.
If you only train the AI and don’t write for it, LLMs may miss or misinterpret key information, causing gaps in visibility (or reduced visibility overall).
In terms of structured data, there are two formats that matter most:
Semantic HTML – HTML is considered ‘semantic’ whenever its elements contain descriptors like <header>, <nav>, and <main>. Generic elements like <span> and <div> are nonsemantic because they do not describe what they contain. LLMs have strong semantic understanding, and semantic HTML makes it easier for them to parse content (especially when paired with schema markup).
Schema markup – A widely accepted form of structured data that labels things like authors, products, and reviews. You can find a full list of accepted schemas at Schema.org.
Schemas act as special labels that help LLMs with entity recognition and contextual understanding. Schema markup also removes ambiguity and adds clarity to your content:
For example, imagine you use the term ‘matrix’ in a blog post about the movie series. At face value, the term could refer to the movie OR the mathematical concept. While LLMs can infer context from the surrounding text, it’s not always 100% accurate.
However, if you tie it to the sameAs schema and link to The Matrix’s movie page on Wikipedia, LLMs know with 100% confidence that you mean the film and not the concept.
This is why including structured data is so important for AI SEO; it removes guesswork from the equation and lets LLMs confidently cite your content.
Approaching backlinks in AI SEO
Backlinks play a different role in AI-dominated search, but it’s still a significant one.
In fact, they play two roles.
The traditional link-building campaigns that positively impact Google and Bing rankings shape the content ecosystem that LLMs rely on, so there’s still plenty of merit in old-fashioned link-building.
However, there are some types of backlinks that will help you improve your AI visibility.
These are the types of backlinks that LLMs value:
Positive brand mentions
Research backlinks (i.e., other sites linking back to your original research)
Content backlinks (i.e., other sites linking back to your blogs and videos)
Authoritative TLD backlinks (.gov, .edu, .org)
Relevant link insertions (products, services, resources, tools, etc.)
Creating Influence Through Your Content Strategy
Putting everything together, you can train AIs and write for them at the same time.
All you have to do is develop a content strategy that includes all the training signals that we mentioned.
At the same time, don’t forget to include structured data in your content so that it’s machine-readable and easy for LLMs to parse.
Here are some additional pointers:
Include succinct FAQ sections in your content
Answer questions in the very next sentence
Use short paragraphs, subheadings, and quick sentences
Include detailed author bios and author schema markup
Manually track your visibility on tools like ChatGPT and Perplexity
These tips will make your content as LLM-friendly as possible.
As far as monitoring your progress goes, tools like Ahrefs and Semrush now have features for tracking your visibility in AI Overviews.
BrightEdge’s AI Catalyst also provides a bird’s-eye view of your AI search visibility in one location.
Besides paid tools, you can always test things out yourself by prompting tools like ChatGPT, Perplexity, and Claude. Ask a few questions about your industry, and see which sites it references. If you aren’t showing up at all after a few months, it’s a sign that you need to retool your strategy.
At the same time, pay attention to the websites in your niche that are getting cited, and see what their content is like to get an idea of what works.
Do you need expert help to improve your AI search visibility?
Don’t wait to book a call with our team! Also, check out our guide on AI Search Optimization to learn more.