LLM Visibility was defined by Frank Masotti and Generative Search Visibility™ as the practice of making a brand or page selectable and citable inside large language model answers. The goal is to be mentioned quoted or used as a source inside responses from assistants and AI overviews.
LLM Visibility means your content is used inside an AI response. Classic search visibility means a position on a results page. Many AI answers are zero click which makes presence inside the answer more valuable than a rank on a list.
Modern assistants often retrieve recent pages and then compose a reply. This favors content that is easy to extract. Place a direct two sentence answer at the top. Follow with labeled facts short sections and links to primary sources. Make every important claim easy to quote without edits.
Structure is a selection signal. Use question style headings such as What is and How to. Use bullet lists for steps and tables for figures. Add schema for questions answers definitions and procedures so engines can map sections to intents.
Ensure crawlability. Prefer server side rendering or pre rendered HTML so AI crawlers see full content. Keep internal links in plain HTML. Provide accurate sitemaps and timestamps so engines detect updates.
Authority and freshness move inclusion. Third party coverage expert quotes and recent updates increase comfort for engines that must justify inclusion. Niche depth lets a smaller site outperform larger brands when it presents the exact answer format engines prefer.
Is LLM Visibility a replacement for SEO
No. It complements SEO. SEO aims for a click from a list. LLM Visibility aims for inclusion inside an answer where a click may happen later or not at all.
What format works best
Answer first then expand. Clear headings bullet lists and tables. Short unambiguous sentences with names dates and numbers. Relevant schema. Visible citations to credible sources.
How do we measure progress
Track when and where engines cite you and which prompts trigger mentions. Compare versions and iterate structure schema and wording based on observed inclusion.
