How I Structure My Website for Large Language Models

As large language models (LLMs) increasingly mediate access to digital content, I’ve learned that my website design must adapt accordingly. While optimizing for human users remains my primary goal, it’s no longer my only goal. I now structure my websites for machine readability, specifically so search and chatbot interfaces like ChatGPT, Perplexity, or Google’s AIO can extract, interpret, and sometimes cite my content without direct human interaction.

In this article, I’ll outline the essential structural elements I use to improve the likelihood of my website being processed, referenced, and accurately summarized by an LLM.

1. I Make Semantic HTML My Foundation

I’ve found that machine parsing depends on consistent and interpretable structure. I achieve this best through semantic HTML.

Here’s what I do:

• I use one <h1> element per page to signal the main topic
• I organize subtopics under hierarchical <h2> and <h3> elements
• I avoid using heading tags for stylistic purposes unrelated to structure
• I ensure my text isn’t hidden inside non-textual elements (e.g., rendered only through JavaScript or embedded in images)

When I fail to apply semantic structure, I increase parsing complexity and reduce extractability.

2. I Start With a Precise Summary

I’ve learned that LLMs prioritize clarity and explicitness. I begin each page or article with a short paragraph that defines the topic, explains its scope, and clarifies the intended outcome for my readers.

For example:

“This article explains how I structure my website to increase the likelihood of its content being interpreted and surfaced by large language models.”

This introduction creates context and increases the probability of my page being used as a source for model-generated summaries.

When I create content for large language models, I break down information into concise, focused sections that each address a single aspect of the main topic, and I use titles that pose questions or provide detailed context, such as “How Does Breaking Content Into Smaller Sections Improve Understanding?”, instead of relying solely on short, keyword-centric headings. I avoid long, SEO-driven content elements that lose focus and go too broad outside of my core topic.

3. I Use Structured Patterns for Extractability

I’ve discovered that LLMs are trained to identify and prioritize structured content. This includes:

• Bullet points to express key points, advantages, or steps
• Numbered lists for chronological or logical sequences
• Tables for comparison or grouping
• FAQs with clearly defined question-and-answer pairs
• Pictures, video and audio content with clear titles, alt-tags and descriptions

These formats reduce ambiguity and facilitate more accurate representation in model responses.

4. I Avoid Ambiguity and Redundancy

I’ve noticed that LLMs avoid pages with repetition, unclear referents, and metaphorical language. I make my content factual, specific, and free of rhetorical devices that lack informational value.

Examples of content I avoid for LLM interpretation:

• “My solutions are revolutionary and unlike anything else on the market.”
• “Think of it like planting a seed that grows into your brand’s future.”

Instead, I favor direct, evidence-based statements:

“My product uses [X] process to reduce [Y] by [Z]% over [timeframe], based on [data source].”

5. I Reference Authoritative Sources

I use citations, either internal (linking to my in-depth site pages) or external (linking to original data), as signals of credibility. LLMs trained on web data may weigh the presence of citations as a measure of source quality.

I use in-text links and name reputable sources explicitly. For example:

“According to the Pew Research Center (2024), over 60% of U.S. adults now use generative AI tools weekly.”

This increases trust in my content and raises its relevance during retrieval.

6. I Maintain High Signal-to-Noise Ratio

My pages must deliver value without padding. I’ve learned that LLMs deprioritize pages that exhibit the following patterns:

• Keyword stuffing
• Excessive internal linking with no contextual value
• Repetitive paragraphs phrased differently
• Generic phrasing that could apply to any topic (e.g., “In today’s fast-paced world…”)

I ensure each paragraph contributes a discrete informational unit. Irrelevant or duplicated content fragments reduce retrieval efficiency.

7. I Provide Metadata and Structured Markup

I use structured data (schema.org) to annotate:

• Article types
• FAQ sections
• Author credentials
• Published and modified dates

This metadata enables more precise extraction and contextual understanding. It also improves inclusion in AI summaries and knowledge graph references.

8. I Prioritize Load Speed and Accessibility

I’ve learned that models are increasingly trained on mobile-rendered versions of websites. Pages that are inaccessible due to:

• JavaScript gating
• Cookie walls obscuring content
• Long load times
• Non-mobile responsive design

…may not be fully indexed or parsed. I ensure that all my core content is available in the HTML source and degrades gracefully if scripts fail.

9. I Design Clear Information Architecture for Topical Relevance

Beyond formatting and metadata, my site’s structure plays a critical role in how LLMs interpret and reuse my content.

I’ve found that LLMs benefit from a well-organized information environment, where every page has a clear role and relationship within a broader topical system. This is where information architecture becomes essential.

a) I Build Topic Clusters

Rather than publishing standalone articles, I group related content into topic clusters. A topic cluster typically consists of:

• A pillar page that I use to offer an in-depth overview of a broad subject
• Cluster pages that I create to dive into specific subtopics, questions, or use cases
• Consistent internal linking between my pillar and cluster pages

For example, if I focused on “Remote Work,” I might structure my content like this:

• Pillar page: “My Complete Guide to Remote Work”
• Cluster pages: “How I Manage Remote Teams”, “My Remote Work Security Best Practices”, “Time Zone Coordination Tools I Use”, etc.

This helps LLMs associate my content thematically, recognize my expertise, and improve surfaceability in results.

b) I Use Hierarchical Navigation

I structure my navigation menus to mirror the whole topic architecture of my site, not just page titles. This helps both users and language models understand content depth and relationships.

My recommended structure:

/my-remote-work-guide/
 └── /my-remote-team-management/
 └── /my-security-best-practices/
 └── /coordination-tools-i-use/

I make sure my URLs, breadcrumbs, and headings all reflect this hierarchy. Pages buried in generic slugs (e.g., /blog/post123) lose contextual clarity.

c) I Ensure Smooth Internal Linking

I use internal links as signals of importance, relevance, and topical cohesion.

My effective internal linking includes:

• Linking my cluster pages back to the pillar page with consistent anchor text
• Linking horizontally between subtopics to show coverage depth
• Avoiding overuse of exact-match anchor text; I use variations to reflect context

LLMs use these links to infer which of my pages are foundational, which are supplementary, and how thoroughly I cover a subject. Pages with no internal links, or links only added for SEO, are often treated as orphaned or low-relevance content.

d) I Diversify Content Types, But Keep Structure Consistent

I’ve learned that LLMs are not biased toward one format, but structured clarity helps regardless of type.

I include:

• How-to guides with step-based formatting
• FAQs with clearly labeled questions and answers
• Comparisons using tables or bullet points
• Use cases or examples as standalone pages or subsections
• Data-backed insights with sources and citations

Each content type I create still follows my cluster model and reinforces topical relevance.

Conclusion

The interface between human-readable content and machine-readable structure is now a central concern for my digital communication strategy. I’ve learned that websites that are semantically structured, textually precise, and accessible to automated systems will be more likely to appear in AI-powered tools and be cited in LLM-generated outputs.

This is no longer a speculative concern for me. LLMs now shape how my users discover, evaluate, and interact with information across platforms. Structuring for machine interpretation isn’t optional—it’s part of my modern content strategy.