Research Methodology
Cited AI uses documented prompts, repeatable test conditions and clearly defined measurement categories so AI visibility findings can be evaluated in context.
Why this methodology exists
AI answer engines are dynamic systems. Results can change by model version, location, prompt wording, user context, retrieval settings and time. Cited AI uses a documented methodology so findings can be interpreted as structured observations rather than universal or permanent facts.
What a credible study requires
A finding is only as good as the discipline behind it. In practice, that means the question and scope are settled before testing starts, not adjusted afterward to fit the result. It means the prompts reflect how real customers actually decide rather than just category searches — a poorly built prompt set will misrepresent a brand's visibility either way, and getting this right for a specific business and location is most of the actual work. It means every observation carries enough context that a single lucky or unlucky answer doesn't get mistaken for a trend. It means different forms of visibility — a passing mention, an active recommendation, a citation, an accurate description — are kept separate rather than folded into one score, because conflating them is how vanity metrics happen. It means external claims are checked against primary sources before they're relied on. And it means every study says plainly what it does and doesn't show, rather than presenting a small sample as a market-wide truth.
Updates, corrections and repeatability
Time-sensitive research includes a publication or review date. Material corrections are made when identified. Where a study is repeated, Cited AI aims to preserve the original prompt set and measurement definitions so changes can be compared more meaningfully over time.
What this methodology doesn't do on its own
Knowing the steps isn't the same as running them well. Building a prompt set that actually reflects how your customers ask, running it consistently across engines month after month, telling a genuine classification change from normal answer variability, and turning a finding into an entity or content fix that moves the needle — that's the part that takes ongoing, specialist attention.
Editorial and Evidence Policy · Australian AI Search Prompt Library · How to Measure AI Visibility
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