When a large language model reads your website, it does not experience it the way a human does. It does not see your design, admire your brand colours, or follow your carefully planned user journey. It processes text, structure, and signals, breaks content into pieces, and converts them into mathematical representations. Understanding this process helps you build websites that AI systems interpret accurately.
This guide explains, in plain language, how LLMs and the systems around them understand websites, and what that means for your content and technical setup.
The Big Picture
LLMs interact with websites in two main ways:
- Training: during model training, large amounts of web text are processed so the model learns language patterns and knowledge.
- Retrieval: when answering a question, an AI system may fetch web pages, extract relevant passages, and give them to the model as context.
In both cases, the model relies on the text it can access and how clearly that text communicates meaning.
Step 1: Crawling and Fetching
Before anything else, a crawler must access your page. AI companies and search engines use crawlers such as GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Googlebot, and Bingbot. Some fetch pages for training, others for search indexes, and others on demand when a user asks a question.
If robots.txt, firewalls, or login walls block access, the model cannot use your content. An AI SEO audit will reveal which of these crawlers can currently reach your site.
Step 2: Extracting Text From HTML
Once fetched, the page’s HTML is processed to extract meaningful content. Systems typically:
- Remove navigation, footers, ads, and scripts where possible
- Identify the main content area
- Preserve headings, lists, and tables as structural cues
- Capture metadata such as titles and descriptions
Many AI crawlers do not execute JavaScript as fully as a modern browser. If your content only appears after scripts run, it may be missed. Server-rendered HTML is the safest approach.
Step 3: Understanding Structure
Headings, lists, and tables help systems understand how content is organised. An H2 that says “How Much Does a Kitchen Renovation Cost?” followed by a clear answer tells the system exactly what that section covers. Vague headings and long, unbroken paragraphs make interpretation harder.
Step 4: Chunking Content
For retrieval, pages are often split into smaller segments called chunks. A chunk might be a section under a heading, a few paragraphs, or a fixed number of words. Each chunk is evaluated separately for relevance to a question.
Why this matters: If a key fact depends on context from a different section, the chunk containing it may not make sense on its own. Self-contained sections improve how accurately your content is used.
Step 5: Creating Embeddings
Chunks are converted into embeddings: numerical representations of meaning. Embeddings allow systems to compare the meaning of a user’s question with the meaning of content, even if they use different words. This is semantic search.
Why this matters: Clear, focused writing produces chunks with clear meaning. Mixed topics in one section create muddier representations that match fewer questions well.
Step 6: Recognising Entities
Systems identify entities in your content: people, organisations, products, places, and concepts. They connect these to what they already know. Structured data, consistent naming, and links to official profiles help systems recognise and connect entities correctly.
Why this matters: If your brand name is ambiguous or inconsistent, systems may confuse you with another entity or fail to connect mentions together.
Step 7: Evaluating Trust and Relevance
When selecting content for an answer, systems consider relevance to the question and signals of trust, such as the source’s reputation, authorship, consistency with other sources, and freshness. The model then generates an answer using the selected chunks.
What Site Owners Should Do
Make Content Accessible
- Allow the AI crawlers you want visibility in
- Avoid blocking bots unintentionally through security tools
- Render key content in HTML
Structure Clearly
- Use a logical heading hierarchy
- Lead sections with direct answers
- Use lists and tables for structured information
Write Self-Contained Sections
- Restate key terms rather than relying on “this” or “it”
- Avoid splitting critical facts across distant sections
Clarify Entities
- Use consistent brand and product names
- Implement Organization, Product, and Person schema where accurate
- Link to official profiles
Maintain Accuracy
- Keep facts current across your site
- Remove or update outdated pages
For a practical list of these actions, see our AI SEO checklist.
Common Problems That Confuse LLMs
| Problem | Effect |
|---|---|
| Content loaded only by JavaScript | Content may be missed entirely |
| Vague headings | Harder to match sections to questions |
| Long mixed-topic sections | Weaker, less precise chunks |
| Inconsistent brand names | Entity confusion |
| Outdated pages left live | Old facts repeated in answers |
| Key facts in images only | Information not extracted as text |
Final Thoughts
LLMs understand websites through text, structure, chunks, embeddings, and entities. The clearer and more accessible your content, the more accurately AI systems can interpret and use it. Designing for machine understanding does not mean sacrificing human experience; it means removing ambiguity. For a deeper look at retrieval, read how AI search works and our introduction to what AI search is.
Frequently Asked Questions
How do LLMs read websites?
AI systems fetch pages with crawlers, extract text from HTML, use headings and structure as cues, split content into chunks, convert them into embeddings, and identify entities to understand meaning.
Can LLMs read JavaScript content?
Many AI crawlers do not execute JavaScript as fully as modern browsers. Content that only appears after scripts run may be missed, so server-rendered HTML is the safest approach.
What is chunking in AI search?
Chunking is splitting a page into smaller segments, such as sections under headings, so each can be evaluated separately for relevance. Self-contained sections produce clearer, more useful chunks.
What are embeddings?
Embeddings are numerical representations of meaning. They let AI systems compare the meaning of a question with the meaning of content, even when different words are used, enabling semantic search.
Why does entity consistency matter for LLMs?
Consistent names and details help AI systems recognise your brand, connect mentions across the web, and avoid confusing you with similarly named entities, leading to more accurate descriptions.
What is the biggest mistake that confuses LLMs?
Hiding important content behind JavaScript, images, or logins is a major mistake. If AI systems cannot access and extract your text, they cannot understand or use it in answers.
