How content depth and readability affect AI chatbot citations?

Studies and simple correlation checks suggest two things matter most when AI chatbots cite a page: content depth and readability. This applies to tools like ChatGPT, Perplexity, and Google’s AI Overviews.

AI citations may seem different from classic SEO, which is why there’s hype around AEO—and why you’ll see promises like “we’ll get your website cited.”

But LLMs, like search engines, still rely mostly on what the page says and how easy it is to read. In short: content quality.

Just like search engine optimization, to get cited by LLMs, here's what you should focus on:

Content depth

Content depth usually means word count and sentence count. Across several platforms, deeper pages tend to get more citations.

• Perplexity and Google AI Overviews often cite pages that have more words and more complete sentences.

• The “surface area” effect: longer content is not better just because it’s long. It helps because it covers more angles. That raises the chance your page includes the exact detail an AI needs for a specific question.

The takeaway: if your content is thin, an AI may not find enough useful lines to quote or cite. Thin, here, doesn’t mean fewer words, it means a lack of depth. Content depth refers to how rich the content is, how well it covers a topic, how different angles are presented, and how many unique perspectives it has.

PS: word count matters only if the content doesn't have filler sentences, or ideas that are not related to the query intent or the original aim of the topic.

Readability

Readability is often measured by the Flesch Reading Ease (FRE) score, which estimates how easy text is to understand.

• ChatGPT seems to care more about readability than other tools. When two pages cover similar points, the clearer one is more likely to be cited.

• Clarity over complexity: the best pages are both detailed and easy to follow. They explain ideas in plain language, even when the topic is technical.

If your writing is hard to scan, full of jargon, or includes long sentences, an AI may skip it.

What readability score improves chances of AI citation?

A Flesch Reading Ease score of 60+ means the text is easier to read and understand, which is generally a good target for most web content. However, it doesn’t guarantee that an AI will cite your page, it’s just a helpful signal. The Flesch Reading Ease scores around 60–70 are often recommended for broad audiences because they’re easy for most adults to read.

There are other readability scores like Flesch-Kincaid Grade Level (FKGL) too. Simply put, it’s a target readability score based on your audience. For maximum accessibility, a Flesch-Kincaid Grade Level (FKGL) of around 7–8 (roughly a middle-school reading level) is commonly recommended because most people can understand it easily.

Query match

Word count and readability help, but there is another big factor: semantic overlap. That means how closely your page matches the user’s wording and intent.

• Query matching: if someone asks for the “best and cheapest” option, a page that uses those exact words may get cited more often than a page that is more accurate but never says them.

• Preference manipulation exists: experiments with “preference manipulation attacks” show that hiding text designed push the AI to recommend a product can increase recommendations. One test found a product could become about 2.5× more likely to be recommended and cited.

In short: AI recommendations can be influenced, not just earned.

However! We never recommend using shady SEO tactics: use the words your customers use, and answer the question directly.

PS: We don’t mean you should stuff exact-match keywords. This isn’t the 2000s. We’re saying that if your product really is the cheapest, say that clearly. You don’t need to copy the exact phrase from keyword research tools.

Can classic SEO signals predict citations?

Classic SEO metrics often have a weak link to AI citations. Things like backlinks, domain rating, and organic traffic do not always line up with what LLMs cite.

In some datasets, the most-cited pages can even have less traffic and rank for fewer keywords than pages that rarely get cited. That implies LLMs may value useful, clear, detailed writing more than general popularity.

A simple way to think about it

Picture a detailed medical textbook versus a popular health magazine.

The magazine might have more readers and more links. But when someone needs a precise answer, the textbook wins. It has more coverage, so the right detail is likely inside. And if it is well structured, the answer is easy to pull out.

AI chatbots work in a similar way. They tend to cite pages that are deep enough to contain the answer and clear enough to extract it fast.