When conversational AIs like ChatGPT, Perplexity, or Google AI Mode generate snippets or answer summaries, they’re not writing from scratch, they’re picking, compressing, and reassembling what webpages offer. If your content isn’t SEO-friendly and indexable, it won’t make it into generative search at all. Search, as we know it, is now a function of artificial intelligence.
But what if your page doesn’t “offer” itself in a machine-readable form? That’s where structured data comes in, not just as an SEO gig, but as a scaffold for AI to reliably pick the “right facts.” There has been some confusion in our community, and in this article, I will:
- walk through controlled experiments on 97 webpages showing how structured data improves snippet consistency and contextual relevance,
- map those results into our semantic framework.
Many have asked me in recent months if LLMs use structured data, and I’ve been repeating over and over that an LLM doesn’t use structured data as it has no direct access to the world wide web. An LLM uses tools to search the web and fetch webpages. Its tools – in most cases – greatly benefit from indexing structured data.
Image by author, October 2025In our early results, structured data increases snippet consistency and improves contextual relevance in GPT-5. It also hints at extending the effective wordlim envelope – this is a hidden GPT-5 directive that decides how many words your content gets in a response. Imagine it as a quota on your AI visibility that gets expanded when content is richer and better-typed. You can read more about this concept, which I first outlined on LinkedIn.
Why This Matters Now
- Wordlim constraints: AI stacks operate with strict token/character budgets. Ambiguity wastes budget; typed facts conserve it.
- Disambiguation & grounding: Schema.org reduces the model’s search space (“this is a Recipe/Product/Article”), making selection safer.
- Knowledge graphs (KG): Schema often feeds KGs that AI systems consult when sourcing facts. This is the bridge from web pages to agent reasoning.
My personal thesis is that we want to treat structured data as the instruction layer for AI. It doesn’t “rank for you,” it stabilizes what AI can say about you.
Experiment Design (97 URLs)
While the sample size was small, I wanted to see how ChatGPT’s retrieval layer actually works when used from its own interface, not through the API. To do this, I asked GPT-5 to search and open a batch of URLs from different types of websites and return the raw responses.
You can prompt GPT-5 (or any AI system) to show the verbatim output of its internal tools using a simple meta-prompt. After collecting both the search and fetch responses for each URL, I ran an Agent WordLift workflow [disclaimer, our AI SEO Agent] to analyze every page, checking whether it included structured data and, if so, identifying the specific schema types detected.
These two steps produced a dataset of 97 URLs,…
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