Cited Is Not Recommended: An AI Visibility Measurement Protocol
A preregistered method for measuring whether an entity is cited as evidence, explicitly recommended, merely mentioned, or absent in AI answers.
Protocol status: This page preregisters a method. SearchEngineAnswer has not completed the declared sample and publishes no citation rate, recommendation rate, platform ranking, or causal optimization result.
A cited source and a recommended entity are not interchangeable. A system may cite a publisher for a statistic while recommending a competitor, mention a brand without linking it, link a source without endorsing it, or provide no relevant entity at all. Combining those outcomes into “AI visibility” hides the decision the reader actually cares about.
Define the outcomes before testing
| Outcome | Operational definition | Evidence to save |
|---|---|---|
| Cited | Linked or attributed as support for a claim | Claim, source, passage, placement |
| Recommended | Explicitly proposed for the user’s stated task | Recommendation wording and conditions |
| Mentioned | Named without support or endorsement | Sentence and surrounding context |
| Absent | Not present in the answer or sources | Complete saved response |
Allow multiple labels in one response only when they apply to distinct passages. Do not upgrade a neutral list entry into a recommendation or treat an unlinked mention as a citation.
Build the query and entity sample
Select real reader jobs: choose a tool, find a local provider, learn a method, compare products, verify a current fact, or diagnose a problem. Define geography, language, audience, price boundary, and other conditions in the prompt. Include branded, category, comparison, and problem-led variants without rewriting a failed prompt to favor the target.
Freeze the entity set and aliases. Record the canonical name, website, product names, founder or organization names when relevant, and common ambiguous terms. Create an adjudication rule for entities with similar names.
Use a minimum repeated-run schedule declared in advance. Save every run, including refusals, errors, empty sources, and unchanged answers.
Freeze the run environment
- Record platform, product surface, model label, date, time, account state, memory or personalization, locale, and device.
- Save the exact prompt and conversation context.
- Capture the complete answer, sources, citation placement, and destination URLs.
- Open each citation and classify support, freshness, ownership, and accessibility.
- Record recommendation conditions, caveats, ordering, and alternatives.
- Keep the raw artifact before annotation.
A change between runs can come from retrieval, model, product UI, web freshness, location, personalization, or stochastic generation. The protocol measures observed responses; it does not automatically identify the cause.
Report without a composite score
Report cited prevalence, recommendation prevalence, mention prevalence, and absence separately within the sample. Add citation-support rate and task-fit review as separate quality fields. Show denominators and confidence intervals when the sample supports them. Do not call a temporary response position a ranking.
Predeclare the stopping rule and publish deviations. An honest pilot can end with a method failure, ambiguous labels, insufficient sample, or platform instability. Those outcomes are more useful than a polished but unreproducible visibility score.
Practitioner discussions in r/SEO_LLM and r/SEO motivated the distinction. They are problem signals, not primary evidence for how a platform works. Use the AI visibility measurement crosswalk for channel definitions.
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