Topic guide
Practical guidance for helping AI systems find, understand, extract, and confidently cite public content.
AI Discoverability is the ability of public systems to find, interpret, extract, and confidently reference your website. It begins with technical SEO and extends into entity clarity and citation readiness.
llms.txt is a proposed Markdown index for LLM-friendly website context. It can be a useful map, but it is not access control, a guaranteed crawler directive, or a shortcut around good site architecture.
SEO, AEO, and GEO emphasize different search experiences, but the durable work is shared: crawlable pages, original information, clear answers, strong entities, and trustworthy evidence.
Structured data translates visible facts into explicit entities and relationships. Its best use is clarification: who published the page, what it describes, where it belongs, and when it changed.
AI Discoverability starts before language analysis. AWS delivery must let automated clients fetch stable, complete, well-identified public evidence.
Citable content gives a reader or retrieval system a reason to choose your page and enough context to quote it accurately. The goal is not short answer fragments; it is verifiable information.
RAG retrieves from sources you deliberately ingest. Public AI Discoverability helps external systems find and cite approved web content. They need different controls.
AI crawler policy is not one yes-or-no switch. Search discovery, user-requested browsing, and model training can use different agents and deserve separate business decisions.
An AI visibility audit should test the website before testing prompts. First prove that valuable pages are accessible, understandable, extractable, and trustworthy; then measure how systems represent them.
Run an outside-in audit and turn public signals into a focused improvement backlog.