What this workflow should accomplish
Evidence Review gives analysts and reviewers validating AI visibility findings a controlled input for AI visibility measurement instead of relying on ad hoc prompts or screenshots.
The result is a bounded measurement for a defined question set and collection period. It does not establish a universal ranking, guarantee future inclusion, or prove why a model produced an answer.
Why a generic visibility score is insufficient
automated extraction can misread entity mentions, recommendations, citations, and ambiguous answers.
Keep the full evidence trail so a reviewer can distinguish absence, mention, recommendation, citation, and description accuracy.
analysts and reviewers validating AI visibility findings
Use this playbook when the result will change a content, positioning, measurement, reporting, or go-to-market decision. Assign an owner before collection begins and agree on what evidence would justify action.
Start with a decision-shaped question
“Does the retained answer support the extracted result and the claim made in the report?”
Evidence to retain
raw answer, extracted entities, citation links, confidence, exception reason, and reviewer decision.
Interpretation boundary
a polished report can amplify a small extraction error into a false conclusion.
Move from scope to a reviewable retest
- 01
Define the decision and audience
Define the decision and audience: analysts and reviewers validating AI visibility findings.
- 02
Build a controlled question set. Start with
Build a controlled question set. Start with: “Does the retained answer support the extracted result and the claim made in the report?”
- 03
Retain raw answer, extracted entities, citation links, confidence, exception reason, and reviewer decision.
Retain raw answer, extracted entities, citation links, confidence, exception reason, and reviewer decision.
- 04
Review the main failure mode
Review the main failure mode: a polished report can amplify a small extraction error into a false conclusion.
- 05
Turn the finding into a test
Turn the finding into a test: route low-confidence and commercially material findings through explicit review states.
publishable observations passing the quality gate
Publish the numerator, denominator, eligible question set, providers, collection dates, and exclusions beside the result. A score without its measurement contract is difficult to compare or audit.
Turn the observation into a test
route low-confidence and commercially material findings through explicit review states.
Record the observation, hypothesis, planned change, owner, expected mechanism, and retest condition separately. This keeps the report honest when evidence is incomplete.
Questions to resolve before acting
What should Evidence Review measurement include?
At minimum, keep raw answer, extracted entities, citation links, confidence, exception reason, and reviewer decision. The result should remain traceable to the exact question and collection conditions.
What is the main interpretation risk?
a polished report can amplify a small extraction error into a false conclusion. Treat observed answers as bounded evidence, not proof of a universal ranking or a hidden model cause.
Which metric should the team review first?
Start with publishable observations passing the quality gate. Keep its numerator, denominator, eligible question set, and collection period visible beside the result.