Schema markup can increase content citation by LLMs like ChatGPT and Perplexity by more than 3x, jumping from 16% to 54% extraction rates.

LLMs use RAG (Retrieval-Augmented Generation) to find and score relevant pages, with scoring criteria detailed in Princeton's GEO research paper

Key ranking signals include answer directness, cited statistics, structured data (JSON-LD), crawl accessibility, and content freshness

Schema markup implementation alone dramatically improves citation potential, shifting from near-invisible content to 54% extraction accuracy