"Google has long advised using such markup for traditional organic search, stating: Google Search works hard to understand the content of a page. You can help us by providing explicit clues about the meaning of a page to Google by including structured data on the page. The search giant generates rich snippets from select structured data and gathers info on a business from additional markup types, such as Schema.org's Organization, FAQPage, and Author."
"Unlike Google, LLMs have no native indexes. They generate answers based on their training data (which doesn't store URLs or code) and from external search engines such as Google, Bing, Reddit, and YouTube. To access a page, LLMs can (i) query traditional search engines, indirectly relying on structured data markup such as Schema.org, and (ii) crawl a page directly to fetch answers."
Google and Bing publish guidelines and tools for traditional search engine optimization, while generative engine providers provide no comparable optimization instructions. No large language model has issued guidance on structured data markup such as Schema.org. Google recommends adding structured data to help Search understand page content and generate rich snippets from types like Organization, FAQPage, and Author, but the impact of structured data on AI agents or crawlers is unknown. LLMs have no native indexes and produce answers from training data and external sources; they can query traditional search engines (which may rely on structured data) or crawl pages directly. Many businesses misunderstand Schema.org; no reputable case studies show that structured data increases AI mentions or citations.
Read at Practical Ecommerce
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