Schema.org for AI Engines
How to structure your data with JSON-LD so that ChatGPT, Perplexity and Gemini understand and correctly cite your content.
Technical guides and key concepts to master Generative Engine Optimization and position your content as a source of truth for AI.
Everything you need to know about how generative AI engines select, process and cite web content. From Schema.org to RAG simulation.
How to structure your data with JSON-LD so that ChatGPT, Perplexity and Gemini understand and correctly cite your content.
Understand the chunking, retrieval and quality scoring process we use to predict whether your content will be selected by a RAG pipeline.
The difference between what your website says visually and what your structured data says. Why LLMs penalize inconsistencies.
Reverse engineering how Perplexity and ChatGPT attribute sources. Which technical signals increase the likelihood of being cited.
How to set up recurring audits and alerts to detect regressions in your GEO score before they affect your visibility.
The responsibility of being discovered by generative models. Transparency, accuracy and the implications of optimizing for AI.
Put what you've learned into practice. Run your first GEO audit and discover how AI models perceive your content.
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