AIGR: Visibility Within AI-Generated Responses

Lesson Overview

Many teams working on GEO have only a concept of “citation rate” but no concept of “visibility within the answer.” In reality, an AI answer is not just about “cited or not”—it also includes: did you appear, in which section did you appear, were you the primary recommended position, were you used to provide the definition, and were you placed in a comparison table.

This lesson introduces the AIGR metric model to help learners measure exactly how much “cognitive real estate” a brand occupies within an AI-generated answer.

Core Concepts

1. The Course Definition of AIGR

AIGR = AI-Generated Response Visibility, used to measure a brand’s “exposure strength” and “position quality” within AI-generated answers.

One important clarification: AIGR is not an official term from Google or Schema.org. It is an internal methodological metric used in this course, better suited as an internal GEO monitoring model for an organization. Official and industry sources more commonly use terms such as visibility, share of voice, cited sources, and response structures (Source: Search Engine Land).

2. The AIGR Scoring Model (100-Point Scale)

Scoring Dimension	Points
Whether you appear	20
Whether you appear in the first half of the answer	15
Whether you are used as the direct answer / recommended option	20
Whether a citation link is included	15
Whether the brand’s own site / product page appears rather than a third-party site	10
Whether you appear repeatedly across multiple platforms	10
Whether the sentiment is positive / neutral-leaning-positive	10

3. Why This Metric Is Valuable

The core of GEO is not just “whether you were retrieved,” but “how much cognitive real estate you occupy in the answer the AI ultimately generates for the user.” Brands should focus on visibility, share of voice, sentiment, and response structures—not just traditional traffic (Source: Search Engine Land).

4. Distinguishing the 4 Levels of AI Visibility

One of the teaching priorities is to guide learners in distinguishing AI visibility from weak to strong: from “completely absent,” to “merely mentioned,” to “cited but only as an alternative / comparison item,” and finally to “used as the direct answer or primary recommended option.” Even though all of these count as “appearing in the answer,” the difference in value across these levels is enormous—which is exactly why AIGR scores “position,” “role,” and “recommendation status” separately.

In-Class Exercise

Manually annotate the AI answers for 10 prompts:

• Position of appearance
• Role within the answer
• Whether recommended
• Whether a citation is present
• Sentiment polarity
• Whether it is the brand’s own-site link

Learning Outcomes

• An “AIGR Visibility Scorecard”
• An “AI Answer Annotation Guideline”
• A “Cross-Platform Visibility Comparison” table
• The ability to separate “being seen” from “being recommended,” build the AIGR scoring logic, and complete cross-platform AI answer annotation