AI Visibility Audit Methods: Checklists, Prompt Sets and Scoring

An AI visibility audit answers one question with evidence: when a buyer asks an AI search system about the decision your client serves, does the client appear, how is it described, and what sources produce that description. This cluster holds the working methods: how to scope the audit, how to build the prompt set from real buyer decisions rather than keyword lists, how to record observations so they can be compared later, and how to score the whole protocol before you sell it.

The method articles here are instruments, not opinion. The audit checklist scores an audit against 47 weighted checks covering scope, facts, technical eligibility, prompts, evidence and handoff, and it ships as a CSV you can keep. The prompt set guide builds a complete 30-prompt panel from one worked buyer decision, with freezing rules so month three can be compared with month one.

If you are pricing or proposing the engagement itself, service delivery is the neighbouring cluster. The AI Search Visibility Audit System is the packaged version of this method, with the observation protocol and evidence workbook included.