“Okay, but where’s the good part?”

This is a common thought we all have when faced with a giant wall of text, especially for online articles where you want to quickly learn something specific. 

Even if we don’t consciously realize it, we scan headlines, jump to bullet points, and skim for the section that actually contains the information we want to know. 

While this might seem like laziness, it isn’t. 

It’s actually a very useful cognitive shortcut or heuristic that helps us quickly process text without wasting too much energy. 

The kicker?

The large language models (LLMs) that power AI systems like ChatGPT do the same thing. 

It turns out that AI models use all sorts of ‘mental shortcuts’ for processing and understanding text that mirror our own, which is what we’re going to explore today. 

Just like us, LLMs prefer ‘best-known’ answers, trust recognized institutions, and skim text to find the most relevant information. 

What’s even better is that you can optimize your content to capitalize on these mental shortcuts. 

Keep reading to learn how to help make your brand the easiest, safest, and most popular choice for AI models to cite. 

Human Mental Shortcuts and Their LLM Equivalents 

First, let’s compare some of the most common mental shortcuts people use and how AI systems mirror the same processes. 

To be clear, LLMs don’t ‘think’ like humans, but they do rely on very similar shortcuts for processing large amounts of information quickly and efficiently. 

We use shortcuts to preserve mental energy, and AI systems are designed to use them to reduce computation, which ultimately saves physical resources like electricity and cooling. 

Consensus gravity (the availability heuristic) 

This shortcut involves defaulting to the best-known answer, which is the answer that comes to your mind the quickest and easiest.  

It’s called the availability heuristic, and it’s based on the assumption that if we hear something often, it must be important or true. 

As an example, imagine someone asks, “What’s the best way to improve heart health?” 

Most people would immediately answer, “Eat healthy and exercise.”

Why?

It’s because we’ve all heard it countless times, it’s effortless to recall, and it feels like a universally accepted truth. While the person answering the question doesn’t study medical journals or have healthcare expertise, it doesn’t matter. The most familiar advice wins.  

This is the availability heuristic in action. 

Surprisingly, LLMs mirror this shortcut almost to the letter. For instance, if an LLM was posed with the same question about heart health, it would surface diet and exercise recommendations. 

It would also use the same phrasing as common public health sources, and it would actively avoid niche or emerging facts. Once again, the best-known answer earns the citation, not the most exhaustive one. 

Mental prototyping (the representativeness heuristic) 

“What does this remind me of?”

This is the central question behind the representativeness heuristic, which entails judging events by how closely they match a mental prototype. 

For instance, imagine you’re comparing two cleaning products online. 

One has minimalist packaging, super clean typography, and muted colors. It’s the Apple iPhone equivalent of a cleaning product. 

The other has abrasive colors, loud typography, and a cluttered layout. 

Most people would assume the minimalist product is higher quality. 

Why?

It’s because it fits the mental prototype of a premium product. It’s what most people would imagine when they envision ‘high-quality products.’ In this scenario, design stands in for performance. 

Now, let’s examine the LLM equivalent. 

If ChatGPT or a similar platform were asked to find ‘the best all-purpose household cleaner,’ they would favor sources with:

• Polished product descriptions 
• Familiar review formats 
• Pros and cons sections with structured data 

Even if a cheaper product works better, the LLM would lean toward content that looks like a high-quality recommendation. In other words, it finds content that fits its mental prototype of what constitutes a strong product.  

Skimming text (chunking and memory limits) 

Both LLMs and humans skim text, and they do it in almost the same exact way. 

Most readers will skim an online article by breaking it into digestible units. Most commonly, this takes the form of:

1. Reading subheadings and bullet points first
2. Taking note of bolded and highlighted text 

If the article is interesting enough, a person may choose to read the entire thing, but only after skimming it first. 

Reading takes a lot of focus and mental energy, so it makes sense that people would use shortcuts to snap to the most important parts. 

LLMs do the very same thing by using a process called chunking. 

Instead of parsing every word on a page, AI systems ingest content in fixed chunks of 300 – 500 tokens (which are most commonly words or parts of words). 

In doing so, they reduce articles to brief self-contained snippets. Any ideas that exist outside of token chunks are effectively invisible to AI tools. 

At the same time, subheadings and bullet points are easily extractable by AI systems, so they often get parsed first. 

Anchoring bias (early-signal dominance) 

With the anchoring bias, the first piece of information we receive sets the stage, and all new information we learn gets interpreted relative to it. 

Another way to think about it is that the first thing we learn acts as an anchor, and everything else gets considered alongside it. 

For instance, the anchoring bias appears all the time during shopping experiences. 

Let’s say you’re shopping for a pair of headphones at an electronics store, and the very first pair you see is $399. It’s way over your budget, so you keep looking. On the next shelf is a pair of headphones that are $199, and you decide to purchase them. 

Why?

Because compared to $399, $199 actually seems like a good deal. 

That means the $399 price anchored your sense of value. As a result, you thought $199 didn’t seem too bad. Had you evaluated the $199 in isolation, it may have seemed too expensive. 

LLMs can do the same thing, too. 

If an LLM is tasked with finding the best pair of noise-canceling headphones, the first article it surfaces may cite premium brands first, using terms like ‘best’ and ‘high-end.’ 

Because of this, it may frame mid-range options as ‘good value,’ and budget options as compromises. 

Here again, the first framing defines the scale. 

How to Optimize for LLM Heuristics: Making Your Brand the Obvious Choice 

Optimizing for heuristics isn’t about ‘tricking’ AI models into citing your content. It’s about ensuring your content’s strongest ideas are picked up by heuristic-driven AI models. 

In other words, you want to make your brand the ‘no-brainer’ choice for AIs to cite. 

Whenever a platform like ChatGPT needs to cite a source, it’s not looking for the most brilliant, original content it can find. Instead, it’s looking for:

• Sources that appear the most reliable (at a glance) 
• Snippets that fit the expected answer pattern 
• Content that reduces uncertainty 

These characteristics exemplify a ‘no-brainer’ source for AI tools. 

Thus, if you can get your content to reflect these characteristics, you stand a better chance of earning citations. 

Clear formatting and structured data (skimming and chunking) 

Because AI models process content in small, self-contained chunks, your content should follow suit. 

By that, we mean:

1. Using subheadings to mark conceptual boundaries, like going from how a topic works to describing its benefits. 
2. Keep each section self-contained, and try not to exceed 400 words. If the subheading mentions pros and cons, then remain on topic for the entire paragraph. If you find yourself venturing into something else, separate that thought with its own subheading (remember, you can use H2s, H3s, and H4s). 
3. Use short, concise sentences that are all substance and no fluff. Also, stick to roughly 2 sentences per paragraph. 
4. Make frequent use of bulleted lists and comparison tables to make skimming even easier for readers and AI systems. 

Also, structured data like semantic HTML and schema markup make your content easier for AI systems to understand, so all your web pages should contain them. 

Check out our post on why structured data is the new SEO cheat code to learn more. 

Brand citations and editorial backlinks (availability heuristic and mental prototyping) 

AI models are more likely to surface and cite brands that pop up frequently across reputable sources. It mirrors how we trust the names and facts that we’ve heard countless times. 

The way to leverage this in your favor is to build brand mentions and backlinks on reputable outlets in your field. 

If trusted blogs, news sites, and media outlets consistently mention your brand, link to your content, and treat you as an authoritative source, you’re more likely to earn AI citations. 

Digital PR methods reign supreme here, which include:

• Networking with online journalists through platforms like HARO and Qwoted 
• Publishing original research, expert interviews, and thought leadership pieces 
• Newsjacking trending stories by injecting your brand into them 
• Writing guest posts and making appearances on podcasts 

Our ultimate digital PR guide is a great resource for taking a deeper dive into the practice. 

Leading with answers (anchoring) 

After each heading and subheading, lead with the most relevant answer or chunk of information at the very start of the paragraph. 

Moreover, start your articles with the most important takeaways and information that readers and LLMs need to know. 

This aligns with the anchoring bias. When your content establishes the first clear, authoritative framing on a topic, it increases the chances that AI systems will treat it as a reference point when synthesizing answers. 

Mirroring already successful content structures (representativeness heuristic) 

LLMs learn what ‘good’ content should look like by observing patterns in the most trusted, authoritative content online (and in their sets of training data). 

Where we match situations to mental prototypes, LLMs match queries to answer shapes. 

Therefore, by mirroring familiar high-quality content structures (using the same phrasing, layout, and approach), your content will ‘feel’ representative of authoritative material. 

When the structure, language, and framing align with known answer shapes, AIs can confidently cite that content without expending additional effort to deeply analyze credibility. 

Our suggestion?

Check out the most respected websites in your field, and take a close look at their content. Make notes about:

• Who authors each page, including their individual biographies and social links 
• The formatting of each post
• Popular topics and the level of depth explored 
• Their internal linking strategy 
• Use of original images and videos 

While you don’t have to mirror their style completely, it never hurts to learn from the best. 

Final Thoughts: Optimizing for LLM Mental Shortcuts 

To wrap up, many internal processes in AI systems mirror the mental shortcuts we make when making decisions throughout our day. 

They default to best-known answers, skim text, and prefer familiar answer shapes. 

Understanding these shortcuts is the first step, and designing content that aligns with them is the next. 

Do you want AI tools to view your brand as a ‘no-brainer’ to cite?

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