Discover how LLMs process web content from HTML to insights using a four-stage pipeline of parsing, tokenization, semantic analysis, and memory management
LLMs don't see content as humans do in browsers. Instead, they process everything as tokenized plain text, stripping away visual elements to focus on semantic meaning. Understanding this process helps optimize content for AI consumption and explains why certain formatting choices improve LLM comprehension.
The transformation from colorful webpages to AI understanding involves systematic text extraction, mathematical tokenization, and pattern recognition that determines how effectively your content reaches AI-powered search results.
When the search tool opens a link, the HTML is fetched.
Boilerplate (menus, ads, unrelated footers) is stripped out where possible.
It’s left with the main textual content — headings, paragraphs, lists, tables.
Non-text content (images, videos) is ignored unless it has descriptive text (like alt text or captions).
Before It can “reason” on it, the text is broken down into tokens — these are small chunks of words, word parts, or symbols.
For English, a token is often ~4 characters on average, so "Webflow" might be 1 token, "optimization" could be 2–3 tokens.
Example: The phrase "How to optimize Webflow websites" might tokenize as: ["How", " to", " optim", "ize", " Web", "flow", " websites"] — 7 tokens total.
The tokenization preserves order, so It know the sequence of the content.
Here is tokenizer from openai: https://platform.openai.com/tokenizer
Once tokenized, It:
Run semantic similarity checks to see if this content matches your query.
Extract key sections (e.g., “Step-by-step guide”, “Best practices”).
Look for structured patterns — headings, bullet points, Q&A sections — which help in re-assembling a coherent, context-aware answer.
It does’t store the full webpage — just the relevant parts I’ve processed as tokens during conversation.
Once It has extracted what’s needed, It discard the rest.
Those tokens are then “decoded” back into natural language when It answers.
It’s like LLM is handed a stripped-down transcript of the page, It break it into Lego bricks (tokens), and then It decide which bricks to keep and how to snap them together into a useful structure.
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