"AI search builds on the same signals that support traditional SEO, but adds additional layers, especially in satisfying intent. Many LLMs rely on data grounded in the Bing index or other search indexes, and they evaluate not only how content is indexed but how clearly each page satisfies the intent behind a query. When several pages repeat the same information, those intent signals become harder for AI systems to interpret, reducing the likelihood that the correct version will be selected or summarized."
"When multiple pages cover the same topic with similar wording, structure, and metadata, AI systems cannot easily determine which version aligns best with the user's intent. This reduces the chances that your preferred page will be chosen as a grounding source. LLMs group near-duplicate URLs into a single cluster and then choose one page to represent the set. If the differences between pages are minimal, the model may select a version that is outdated or not the one you intended to highlight."
AI search builds on traditional SEO signals and adds layers that evaluate how clearly pages satisfy user intent. Multiple pages repeating the same information make intent signals harder for AI systems to interpret. LLMs group near-duplicate URLs into clusters and select a single representative page for grounding. Minimal differences between pages increase the risk that an outdated or unintended version will be chosen. Campaign, audience, and localized variations only satisfy distinct intents when differences are meaningful. Reused content reduces signals needed to match pages to unique user needs. Duplicate pages also slow propagation of fresh content in AI systems.
Read at Search Engine Roundtable
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