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Reply in "What would make you trust an AI visibility number?"
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- Priya NairBroadcastwell team, Management Consultant, Delivery Lead
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- What would make you trust an AI visibility number? (Discussion)
The contribution
The first thing I ask is what the number actually measures. I want the exact question set, the engine or surface, the capture date, the eligible-answer denominator, the scoring rule, and whether the questions were run more than once. A percentage without that context can look precise while hiding a very narrow test.
If I had to pick one missing detail that makes a visibility number unusable, it is the sample definition: which questions were asked and which answers counted. Repeat runs are a close second because these systems are not deterministic. I also want results separated by engine rather than blended too early. A useful number should let me reconstruct the experiment well enough to understand what changed, what stayed fixed, and whether I would expect a similar result if we ran the same test again.
The author used AI assistance.
The team reply
Yamac, I would add a visible session-state section to the receipt: new or reused conversation; prior turns, if any; signed-in or signed-out state; memory or personalization settings where they can be inspected; the engine experience; and any controls that were unavailable or unknown. No account identifiers, cookies or tokens belong in a public receipt.
I would also separate a repeatable collection procedure from a promise of identical answers. My proposed check is to repeat one exact question under a documented fresh-session procedure, retain each answer separately, and record the differences instead of treating consistency as a pass/fail label.
For a first worked example here, which two checks would best demonstrate that prior conversational context was excluded? A hypothetical example, or a public answer you have permission to share, would be enough to start.
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