Here’s something you need to know about AI search: clear, organized content has a major advantage. 

Why is that?

It has to do with how AI systems pull and rank online content. 

Instead of indexing pages in their entirety, AI models break them up into small, self-contained chunks. 

Whenever an AI pulls online content to cite and generate answers from, it retrieves relevant chunks instead of entire documents. 

Here’s why this matters for search marketing. 

If your content isn’t clearly structured with self-contained subheadings, your site’s chunks may confuse AI systems, causing them to conclude your content is irrelevant (when it actually is). 

Topical drift and content decay are two other major risks. 

In this guide, we’ll teach you why organized content is an absolute necessity in AI search. Keep reading to find out how you can create AI-friendly content. 

How Do AI Systems Read Content?

AI models are completely changing the way online content gets surfaced and evaluated. 

To understand why, let’s take a look at how classic Google Search works. 

Googlebot crawls the open web continuously. When it does, it parses every single word on the page. Thus, meaning and authority are evaluated on a page-wide basis (i.e., the entire piece is evaluated). 

As mentioned in the intro, AI search does not do this. 

They ingest content into their indexes in small, fixed-size segments called chunks. They normally consist of 300 - 500 tokens, which are fragments of text that are typically words or parts of words. 

Here’s a real-world example of a chunk from a blog that clearly explains what a high-yield savings account is:

While it’s part of a greater piece, an AI system would ingest this chunk as a standalone snippet. 

Whenever a user enters a query asking about high-yield savings accounts, this particular snippet could be one that the system considers to cite. 

Furthermore, when deciding which chunk to cite and build answers from, it will compare this snippet to other snippets explaining high-yield savings accounts. 

In doing so, it does NOT evaluate each entire page, only the self-contained chunks. 

What about the rest of the blog?

It’ll get split into other chunks that relate to other subtopics, assuming the blog doesn’t lose focus or drift into something unrelated. 

As long as each section of the piece remains focused, each chunk has a shot at retrieval for its specific intent. A few examples would be a savings account pros/cons table or a subheading breaking down how high-yield savings accounts actually work. 

Why do AI models use chunking instead of page-wide indexing?

It may seem strange why AI models choose to split content up into chunks instead of considering the context behind the entire page. 

After all, page-wide indexing is a more effective way of preserving the meaning and context behind an entire piece of content, not just individual snippets. 

It turns out there are several reasons why AI models use the chunking method:

1. Resource efficiency – Crawling each page in its entirety would be A) resource-intensive and B) slow the retrieval process down considerably. Splitting content into self-contained snippets is far more efficient from a speed and resource perspective. 
2. More precise retrieval – With chunking, AI systems are able to retrieve the exact part of the document that pertains to a user’s prompt. For instance, the average informational blog contains sections like ‘what it is,’ ‘how it works,’ and ‘pros and cons.’ If a user’s prompt specifically asks about pros and cons, the AI system can retrieve that specific section instead of processing the entire page. 
3. It’s a better way to handle context windows – LLMs and vector indexes have context windows and practical size windows. That means they only have a limited amount of space to store sources for generating an answer. Full-page indexing would make it difficult to fit multiple sources into a single answer, especially if they’re longer documents. With chunking, the AI system can easily mix and match multiple sources without exceeding size limits or context windows. 
4. Splitting each piece at topic boundaries for improved answer quality – Headings and structured data (such as schema markup and semantic HTML) help AI models identify natural topic boundaries within a page. These boundaries enable precise chunking so that AIs can have multiple sources (like more than one pros and cons list) to synthesize answers, leading to an improved answer quality. It’s the equivalent of citing several sources on a term paper instead of relying on a single reference.

Put simply, AI systems use chunking because it’s resource-efficient and enables stronger, more precise answers. 

Why Messy Content Fails in AI Search 

Freeform content that follows a loose structure (if any) risks not performing very well on AI search platforms like ChatGPT, Google’s AI Overviews, and Perplexity. 

By ‘freeform,’ we mean content that doesn’t follow a clear, scannable structure.

If your blogs are a giant wall of text that jumps from one topic to another with no subheadings to separate them, AI systems will have a hard time understanding your content. 

This can cause problems with determining relevance, and your blogs may not get cited by AI tools as a result. 

Also, outdated content is another factor that can cause serious AI visibility issues. 

AI models prefer fresh content, especially for prompts that have temporal intent. Imagine that a user asks ChatGPT, “What’s the latest news in the digital marketing world?” 

To find out, ChatGPT will retrieve fresh snippets from the most frequently updated digital marketing websites. 

Older content will get phased out during the freshness layer of the retrieval process. To learn more about the importance of fresh content, check out our guide on the seven trust signals AI systems use. 

Here are the formatting choices you should avoid at all costs if you want better AI search visibility:

1. Text that ventures off topic or doesn’t relate to its subheading at all. If you present a question in a subheading, like ‘what is (blank)?’, then you should immediately start answering it in the first sentence. At the same time, the entire paragraph should remain on topic. New ideas, like ‘how does (blank) work?’ should have their own subheadings. 
2. Walls of text that contain no subheadings or bulleted lists. It’s equally as cumbersome for machines to parse through giant blocks of unbroken text as it is for humans. Long paragraphs that jump from topic to topic don’t mesh well with how AI systems retrieve content.
3. Outdated content that has old timestamps and aging terminology. Frequently updating your content is an absolute necessity in AI search optimization. Primarily, you should update timestamps and structured data, as well as provide any new relevant insights. Your evergreen pieces are not immune to becoming stale to AI tools, so ensure they stay updated with fresh timestamps, the latest facts, and current terminology. 

What Does Clearly Formatted Content Look Like?

Now that you’re familiar with what not to do, let’s analyze the proper way to format content for improved AI visibility. 

Here are the basics: 

1. One general header (the H1) 
2. Subheadings that serve as ‘subtopic boundaries’ (H2s, H3s, H4s, etc.)
3. Only one topic per subheading 
4. Short sentences and paragraphs 
5. Bulleted lists 
6. Charts, images, and other visualizations 
7. Add internal links to related pieces to create strong topical clusters 
8. Keep paragraphs under subheadings fewer than 300 words (to not exceed the chunk limit) 

Following this formula, your content will be effortless for LLMs to parse, heightening the chances that you’ll earn citations. 

Important note: While a concise content structure is integral for proper parsing and retrieval, it alone does not guarantee that AI models will choose to cite your brand. 

Besides structure, your content also needs to pass the rest of the seven trust layers that AIs use to cite content. That means you’ll also need things like authoritative brand mentions, backlinks, and a positive brand sentiment. 

How a clear content structure benefits both SEO and AI search

Discovering that online content must follow such a rigid structure may seem disappointing to some creators, but there is a silver lining. 

A clear, hyper-focused content structure benefits:

1. Traditional SEO (i.e., better organic rankings) 
2. AI search (increases your chance of earning citations) 
3. Your user experience (your content will be effortless to read) 

While using a uniform structure may stifle some creativity, the sheer number of benefits it provides outshines that fact. 

Search engine crawlers, AI systems, and users will all appreciate that your content is easy to consume, making it easier to generate leads and sales online. 

How the HOTH Supports Content Clarity 

We’ve been unpacking the way AI search systems work for years at this point, and it’s served as a blueprint for our current product offerings. 

As a result, we’re well equipped to help brands improve content clarity and earn more AI citations through:

• Content creation services that produce chunk-friendly articles. 
• Technical SEO audits to clean up pages, add structured data, and improve site speed (which is still a crucial factor in AI search). 
• Link-building and Digital PR services that signal authority to search engines and AI platforms. 
• Local SEO services that strengthen your business’s entity. 
• AI Discover to improve AI search visibility specifically. 

Whether you want to rank better in local search results or improve your AI search visibility, we’ve got the perfect hands-off solution for you. 

Concluding Takeaways: Creating AI-Friendly Content 

Changing the way you format your content doesn’t mean that you have to lose your original voice or insights. 

It just means that you’ll have an easier time reaching your audience through multiple channels, and that’s always a good thing. 

Do you need expert guidance in developing an AI-friendly SEO strategy?

Book a free call with our team to uncover the perfect strategy for your specific needs.