Most advice on getting cited by AI models is frustratingly vague: "write quality content," "be authoritative," "cover topics comprehensively." This article does the opposite. It breaks down the structural and formatting signals that large language models actually use when deciding which source to extract and surface in a response, and gives you a practical framework for reverse-engineering those signals from the content that's already winning.

TL;DR

• LLMs favour content with clear, question-based heading structures that map directly to how users query AI models.

• Word count is not about length for its own sake; it's about answer completeness within a defined section.

• Internal link density matters less than link purpose; links that reinforce topical authority outperform links added for SEO volume.

• Google AI Overview optimization and LLM extraction share overlapping signals, but they are not identical strategies.

• You can reverse-engineer winning content structures by auditing what AI models are already citing in your niche.

About the Author: Simaia is an agentic marketing team specialising in AI search visibility for B2B companies across APAC, with direct experience running AI search audits across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview to identify exactly which content structures earn citations.

Why Do LLMs Extract Some Content and Ignore Others?

LLMs extract content that is easiest to interpret as a self-contained, trustworthy answer. This is the foundational principle that everything else in this article builds on. When a model generates a response, it is not ranking pages the way Google does; it is identifying passages that can be lifted, paraphrased, or synthesised into a coherent reply with minimal ambiguity.

The practical implication is significant: content optimised purely for keyword density or backlink signals will often be ignored by LLMs, even if it ranks well organically. What the model needs is structural clarity, direct definitions, and answers that stand alone without requiring surrounding context to make sense.

What Word Count Actually Signals to an LLM?

Word count signals completeness, not effort. A section that answers a specific question in 120 well-constructed words will outperform a 600-word section that buries the answer in preamble and qualifications.

The useful frame here is "section-level completeness," not document-level length:

• Opening sentence: Define or directly answer the section's question immediately.

• Body: Provide the supporting evidence, context, or steps in concise bullet points or short paragraphs.

• Close: Either draw a conclusion or transition clearly to the next question.

Each section should be extractable as a standalone unit. If an LLM lifted only that section and nothing else from your page, would the answer still make sense? If not, the section is not structured for extraction.

For reference, sections that earn consistent LLM citations tend to be between 80 and 200 words per H2 block, with the direct answer in the first one to two sentences. Longer is not wrong, but every sentence beyond the core answer needs to add verifiable detail, not restate it.

How Should You Structure Headings to Get Cited by AI?

Headings are not just navigational labels; they are the primary signal LLMs use to understand what a content block is about [kb.ndsu.edu]. An effective heading structure maps directly to the way users phrase queries to AI models, because those models are pattern-matching your headings against incoming questions.

Concrete principles for heading structure [pressbooks.bccampus.ca]:

• Use question-based H2s. Phrase your primary subheadings as the actual questions users ask. "What is X?" outperforms "Overview of X" for LLM extraction because it mirrors query syntax.

• Keep headings descriptive and specific [developers.google.com]. Vague headings like "More Information" or "Key Considerations" tell the model nothing about what the section answers.

• Limit heading depth. H2s and H3s are sufficient for most content. Nesting beyond H3 creates structural ambiguity that makes it harder for models to assign the right section to the right query [searchenginezine.com].

• Use sentence case [developers.google.com]. This is a minor but consistent signal in content that major style guides recommend and that AI-friendly documentation follows.

One effective reverse-engineering exercise: run your target query in ChatGPT, Claude, or Perplexity, then look at the structure of the source it cites. Note the exact heading phrasing, the depth of the heading hierarchy, and where the cited passage sits within the document. Then build your own content to match that structural pattern while differentiating your actual answer.

What Internal Link Density Actually Matters for LLM Visibility?

Building on the heading structure above, the harder question is whether internal links help or hurt your chances of being cited. The short answer: links help when they reinforce topical depth; they hurt when they appear to pad content or redirect the reader away from the answer.

LLMs process internal links as signals of content ecosystem. A page that links to three closely related, specific articles on the same topic signals that the domain has depth on that subject. A page with 15 internal links pointing to loosely related commercial pages signals the opposite.

Practical guidelines:

• Aim for two to four contextually relevant internal links per 1,000 words. This is a range, not a rule, and relevance matters more than count.

• Link to pages that extend the answer, not pages that replace it. If a reader clicks your internal link and finds a deeper treatment of the subtopic, that's a good link. If they find a product page with no additional information, it's not.

• Anchor text should describe the destination specifically. "See our guide on AI search audits" is more useful to a model than "click here."

How Do You Reverse-Engineer What's Already Winning?

The most reliable method is to run the queries your buyers are using directly inside the AI models you want to appear in, then systematically audit the sources being cited.

A step-by-step approach:

1. List 10 to 20 queries your buyers use when researching your category. Phrase them conversationally, the way they would type them into ChatGPT or Perplexity.

2. Run each query across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview. Note every source cited.

3. For each cited source, record: the word count of the cited section, the heading structure (H1, H2, H3 depth), the position of the cited passage within the page, and the number of internal links on the page.

4. Look for patterns. Which heading styles recur? Which section lengths recur? Which platforms are cited most often by each model? (ChatGPT leans toward LinkedIn; Google AI Overview cites Reddit and publisher content.)

5. Build content that matches the structural patterns of the sources already being cited, then differentiate on the quality and specificity of your actual answer.

This is the same audit methodology Simaia runs for clients, and in one case it helped a healthcare SaaS company grow from zero AI search visibility to owning 45% of its niche's AI search visibility across major LLMs within 2.5 months.

Frequently Asked Questions

Does total page word count affect whether an LLM cites me?
Section-level clarity matters far more than total page length. A 400-word page with one perfectly structured answer can outperform a 3,000-word page where the answer is buried.

Is Google AI Overview optimization different from general LLM optimization?
Yes. Google AI Overview optimization tends to favour content that also performs well organically on Google, including structured data and publisher authority. Other LLMs place more weight on conversational phrasing and community platform signals like Reddit and LinkedIn.

How often should I update content to maintain LLM citations?
When the factual accuracy of a cited section changes, update it. LLMs that index live web content (like Perplexity) will reflect changes faster than models with static training windows.

Do headings need to be exact keyword matches?
No. Natural question phrasing that mirrors how users actually query the topic performs better than forcing exact keyword matches into headings [searchenginezine.com].

Can I over-optimise for LLM extraction and hurt my Google rankings?
The structural signals that help LLM extraction (clear headings, direct answers, descriptive anchor text) overlap significantly with what Google rewards. The risk is minimal if content quality is genuine.

About Simaia

Simaia is an agentic marketing team that replaces the in-house marketing function for B2B companies across APAC, handling both strategy (AI search audits, competitor gap analysis, trusted-source mapping) and execution (content writing, media placement, lead identification). Rather than handing clients a dashboard to operate, Simaia runs the entire AI visibility playbook end-to-end, from identifying which LLMs cite which platforms in a client's category, to publishing content structured specifically for extraction, to surfacing the identities of inbound visitors who arrive via AI referrals. For companies losing pipeline to competitors that appear in AI answers, Simaia closes that gap without requiring internal teams to hire, learn, or operate anything themselves.

If you want to see exactly where your brand appears (and where your competitors appear) across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview, visit https://www.simaia.co/ to learn how Simaia can run that audit for you.