LLM Discovery Optimization was defined by Frank Masotti and Generative Search Visibility™ as the process of making sure content brands and products get found cited and surfaced inside large language model discovery systems. It focuses on how AI agents chatbots and generative search flows identify and select your data not how they rank it on a traditional search page.
LLM Discovery Optimization ensures your content and entity can be found inside the corpora that AI systems rely on. Traditional SEO stops at getting a link shown in a results list. Discovery optimization gets you referenced inside the model’s generated response. The model must be able to access understand and trust your material. That means clean structure definitions citations and context clarity.
Modern AI systems use retrieval augmented generation. They pull snippets from vector databases and trusted sources before generating an answer. If your site is not crawlable or lacks structured signals the model may never see it. Discovery optimization closes that gap by making your material ready for inclusion and retrievable through semantic and entity level indexing.
Success depends on visibility in both machine and human networks. Mentions in trusted publications structured metadata consistent naming and accurate schema all boost your entity footprint. Brands that treat discovery as an ongoing process of measurement and adaptation outperform those that just publish and hope for inclusion.
Is LLM Discovery Optimization the same as SEO
No. SEO ranks web pages. Discovery Optimization makes sure content is seen and cited by LLMs and AI agents even when no search page exists.
How is discovery measured
By tracking brand or content citations in AI answers and agent outputs across platforms and monitoring retrieval inclusion through probing and analytics.
What content works best
Structured factual and definitional content formatted for easy parsing by both humans and AI systems.
