Guides
LLM visibility is the ability to be retrieved, represented accurately and cited when an AI system answers a relevant question. It is not a stable ranking position, and it cannot be measured responsibly through a single prompt or an invented universal score.
This library covers how major answer systems use web sources, how to structure definition blocks and quotable passages, and how original data, entity clarity and source quality affect citation eligibility. It distinguishes observable behavior from speculation about proprietary models.
Use the audit and writing guides to build repeatable tests, document citations over time and improve pages that are difficult to extract or verify. The objective is a reviewable visibility process rather than a promise that any model will cite a particular source.
16 published guides in this collection.
