TL;DR Summary of Structured Data’s Role in AI and LLMs: What Recent Tests Reveal
Optimixed’s Overview: Understanding the Real Impact of Structured Data on AI Language Models
Background and Experimentation
Mark Williams-Cook conducted a test with a fictitious company named DUCKYEA t-shirts to explore if AI language models use structured data differently than regular webpage text. By embedding the company’s address only within a fabricated JSON-LD schema markup (not visible on the page itself), he tested how ChatGPT and Perplexity would respond when prompted for the address.
Key Findings
- Both ChatGPT and Perplexity extracted the address from the fake schema, despite it being invalid.
- This suggests that these AI models treat schema markup as part of the page text, without validating or interpreting it as structured data.
- Therefore, the AI does not leverage schema in the explicit, intended manner designed for search engines or data feeds.
Industry Perspectives
While OpenAI has acknowledged using structured data feeds for shopping results, and Google’s John Mueller states the usefulness of schema depends on context, Microsoft has highlighted schema’s role in assisting Copilot functionality. Despite this, the consensus from tests like Williams-Cook’s is that schema markup is primarily valuable for traditional SEO rather than providing a distinct advantage in AI language model comprehension.
Conclusion
Structured data remains a good practice for SEO and content organization. However, claims that schema offers a magical improvement in how AI engines understand or utilize web content lack strong evidence. As Mark Williams-Cook summarized: schema is beneficial, but repackaging basic SEO principles as revolutionary AI tools is misleading.