Building Websites That Are Visible to LLMs

For two decades, we’ve optimized websites for search engines. We learned the rules: keywords in headers, descriptive alt text, fast load times, mobile responsiveness. But the way people discover information is shifting, and designers need to understand what it means to build for a new kind of reader: large language models.

When someone asks ChatGPT for a product recommendation or Claude for service providers in their area, the response draws from training data and, increasingly, real-time web content. Microsoft confirmed at SMX Munich in March 2025 that Bing uses schema markup to help its LLMs understand web content. If your clients’ websites aren’t structured in ways that AI systems can parse, they’re becoming invisible in a growing category of discovery.

How LLMs Read Differently

Search engines crawl and index. They look for keyword relevance, authority signals, and technical performance. They’re matching queries to documents based on probability of relevance.

LLMs do something different. They attempt to understand content semantically: parsing meaning, relationships, and context rather than just matching strings. They synthesize information across sources rather than returning ranked lists. And they’re often asked questions that require reasoning about content rather than simply locating it.

A study cited by DigidDop found that GPT-4’s accuracy jumped from 16% to 54% when processing content with structured data versus unstructured text. That’s a 38-point improvement from the same content, presented differently.

Michael King, an industry analyst, describes how AI systems handle retrieval: “Because routing decisions are modality-aware, having your information available in text, tables, images, videos, transcripts, and structured data gives you more entry points into the retrieval process. If the system decides a sub-query should be answered with a table and you only have prose, you’re invisible to that branch.”

Structured Data as Foundation

Schema.org markup has existed for years as a way to help search engines understand content. For LLMs, this structured data becomes even more critical.

When you mark up a product page with proper schema (price, availability, reviews, specifications) you’re providing information in a format that LLMs can reliably extract and synthesize. When you use FAQ schema for common questions, you’re creating clear question-answer pairs that models can incorporate directly into responses.

AISO ran a test on two identical websites, one with schema markup and one without. They asked ChatGPT the same questions about both websites and analyzed the responses. The website with schema markup provided more detailed and accurate information about the company in every category tested.

The designers and developers who take structured data seriously are building sites that translate cleanly into the knowledge representations LLMs use internally. It’s not just about rich snippets in Google anymore. It’s about whether your content can be accurately understood by AI systems that increasingly mediate how people find information.

Semantic HTML Still Matters

The old principle of using proper heading hierarchies and semantic elements isn’t just about accessibility. It helps LLMs understand content structure. When you use <article> to wrap discrete pieces of content, <nav> for navigation, <aside> for supplementary information, you’re providing signals about what’s important and how pieces relate.

Many modern websites, particularly those built with component-heavy frameworks, generate markup that’s technically functional but semantically flat. Everything is a <div>. Relationships between elements are expressed through visual design and CSS rather than document structure. Humans browsing the rendered page understand these relationships. LLMs reading the underlying markup may not.

This is an area where good design practices align with LLM visibility. The same semantic rigor that improves accessibility and maintainability also makes content more parseable by AI systems.

Clear, Extractable Answers

LLMs are often asked direct questions: What does this company do? How much does this service cost? What are the hours of operation? When your website buries these answers in marketing prose, they become harder to extract reliably.

Consider how your key information is presented. Is your core value proposition stated clearly in a parseable format, or does it require inferring from scattered copy? Are critical details like pricing, location, and contact information formatted consistently? Are common questions answered directly, or does finding an answer require navigating through multiple pages?

This doesn’t mean dumbing down your copy or eliminating brand voice. It means ensuring that essential information exists in clear, extractable forms somewhere on the site, even if it’s also expressed more artfully elsewhere.

Higher Visibility’s research found that LLM queries average 23 words compared to 4 words in traditional Google searches. Users are asking specific, conversational questions. Sites that provide specific, direct answers to those questions have an advantage.

Content Freshness and Authority

LLMs increasingly access real-time web content rather than relying solely on training data. This means current, well-maintained content has advantages over static pages that haven’t been updated in years.

Regular publishing signals that a site is active and authoritative. A company blog with recent posts about industry trends demonstrates ongoing expertise. Updated case studies show current capabilities. Fresh content gives LLMs more recent information to draw from when answering queries.

Authority signals matter too, though they function differently than traditional backlinks. When your content is cited, quoted, or referenced by other reputable sources, it builds the kind of trust signals that both search engines and LLMs use to evaluate reliability. Original research, unique perspectives, and genuine expertise create content that’s more likely to surface in AI-synthesized responses.

The Human Audience Remains Primary

Optimizing for LLM visibility shouldn’t come at the cost of human experience. The good news is that most principles align: clear information architecture, semantic markup, well-structured content, and accessible design serve both audiences.

Where tensions exist, human visitors should win. If a design choice improves LLM parseability but harms usability, prioritize the humans actually using the site.

According to HubSpot, 19% of marketers plan to add SEO for LLM best practices to their strategy in 2025. Traditional SEO still captures 80% of search traffic. The smartest approach is designing primarily for human experience while ensuring that technical foundations support machine readability. Proper semantic structure, comprehensive structured data, clear information architecture: these elements enhance human experience while simultaneously improving AI visibility.