{"id":1211,"date":"2025-04-25T12:47:56","date_gmt":"2025-04-25T12:47:56","guid":{"rendered":"https:\/\/storyblok-hub.itcurated.com\/?p=1211"},"modified":"2026-05-06T11:37:18","modified_gmt":"2026-05-06T11:37:18","slug":"your-content-has-a-new-reader-it-reasons-differently","status":"publish","type":"post","link":"https:\/\/itcurated.com\/storyblok-hub\/your-content-has-a-new-reader-it-reasons-differently\/","title":{"rendered":"Your Content Has a New Reader. It Reasons Differently."},"content":{"rendered":"\n

Introduction<\/h2>\n\n\n\n

If you\u2019ve only just wrapped your head around the concept of \u201clarge language models\u201d (LLMs), you\u2019re not alone. Generative engines may have been around for 2-3 years now, but how AI works under-the-hood and how it impacts search are just starting to become, maybe not common knowledge yet, but certainly more researched and talked about.<\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n\n
WHY THIS MATTERS FOR BRANDS:<\/strong><\/h6>\n\n\n\n

Control:<\/strong> How your content is interpreted, understood, and surfaced is increasingly determined by AI engines, not just search engines or social platforms.<\/p>\n\n\n\n

Visibility:<\/strong> AI-driven content discovery may favor certain formats, sources, or even phrases, changing who audiences see first when they type in a prompt.<\/p>\n\n\n\n

Trust:<\/strong> Users have started to rely on AI recommendations and summaries to help them make faster decisions; if your content isn\u2019t up-to-date and regularly refreshed with new insights then it risks losing credibility<\/p>\n\n\n\n

WHY THIS MATTERS FOR MARKETERS:<\/strong><\/h6>\n\n\n\n

If you\u2019re a marketer today, you need to understand how generative engines work, how they find their information, and how to structure your content so that it gets \u201ccited\u201d in an answer. You might read a blog here, or send a prompt there, or maybe you\u2019re frequently skimming think-pieces on social media from your favourite industry influencer. But you\u2019re still a bit confused on what to do, and it\u2019s all feeling more technical than you\u2019re used to. That\u2019s okay, we\u2019re here to help.<\/p>\n\n\n\n

In this handy PDF we\u2019ve condensed a really big topic into three sections. And added a bunch of examples to really help you understand. We\u2019re giving away three big nuggets:<\/p>\n\n\n\n\n

\n
    \n
  • What LLMs \u201csee\u201d when they \u201cread\u201d your content<\/li>\n
  • How to structure your content for LLMs as a result<\/li>\n
  • A checklist of technical optimizations to discuss with your developer team<\/li>\n<\/ul>\n <\/div>\n\n\n\n\n\n\n
    \n

    PART 1:<\/span><\/strong>\n<\/p>\n <\/div>\n\n\n\n

    What do LLMs \u201csee\u201d when they \u201cread\u201d your content?<\/h2>\n\n\n\n

    In a nutshell: LLMs don\u2019t read text like humans do. They don\u2019t scan sentences for context and meaning like our brains do as we take in the words. Instead, they break text into small pieces called \u201ctokens\u201d, then predict what comes next based on patterns learned from vast amounts of data. It\u2019s all mathematical.<\/p>\n\n\n\n\n

    \n
    \n

    For marketers, it\u2019s important to understand how that relates to any piece of content you write for the web. Let\u2019s take a standard blog post and a really simple example of copy.<\/strong><\/em><\/p>\n

    Here\u2019s an example of an LLM breaking down your blog content into tokens (\u201ctokenization\u201d):<\/strong><\/em><\/p>\n

    So, you\u2019ve written a blog post. In your CMS you\u2019ve written\u2014<\/strong><\/em><\/p>\n

    \u201cI love apples!\u201d<\/strong><\/em><\/p>\n

    If you\u2019re familiar with HTML, the code in the frontend for this might typically look like\u2014<\/strong><\/em><\/p>\n

    <p class=\u201dintro\u201d>I love apples!<\/p><\/strong><\/em><\/p>\n

    This copy would then be separated into its components, this might look like (simplified!)\u2014<\/strong><\/em><\/p>\n

    [I, love, apples, !]<\/strong><\/em><\/p>\n

    The long version with HTML included\u2014<\/strong><\/em><\/p>\n

    [\u201c<\u201d, \u201cp\u201d, \u201c class=\u201d, \u2018\u201d\u2019, \u201cintro\u201d, \u2018\u201d\u2019, \u201c>\u201d, \u201cI\u201d, \u201clove\u201d, \u201c,\u201d, \u201c apples\u201d, \u201c!\u201d, \u201c<\/\u201d, \u201cp\u201d, \u201c>\u201d]<\/strong><\/em><\/p>\n

    Do you see how the words and the code become a sequence of symbols? LLMs don\u2019t inherently understand that <p> means paragraph, it\u2019s just another symbol. This sequence of symbols looks different depending on the LLM, as they break down the components differently.<\/strong><\/em><\/p>\n

    Finally, the symbols are converted into numbers, with each symbol given a unique numerical ID (token IDs.)<\/strong><\/em><\/p>\n

    LLMs will begin to understand meaning by parsing vast amounts of these numbers and recognizing any patterns. Taking the earlier example, they will understand that a pattern emerges where words included within <p> are separated into paragraphs, therefore <p> means paragraph.<\/strong><\/em><\/p>\n\n

    \n \"\"\n <\/div>\n <\/div>\n <\/div>\n\n\n