Share of Model is how often your brand appears in AI answers compared to competitors. Measure it by running a controlled prompt set across ChatGPT, Perplexity, and Google AI, logging mentions/citations, and tracking changes weekly. Tie gains to specific content and schema updates to prove what actually increases AI visibility.
Share of Voice (Search) vs Share of Model (Generative)
Share of Voice measures how often your brand ranks in traditional search results. Share of Model measures how often AI systems mention, cite, or recommend your brand when answering relevant questions.
A user searching Google sees ten blue links. A user asking ChatGPT receives a synthesized answer mentioning one brand, several, or none. Your visibility depends on whether AI recognizes your brand as authoritative for that topic.
| | |
| | AI assistants (ChatGPT, Perplexity, Gemini) |
| Ranking position, impressions | Mentions, citations, recommendations |
| | |
| | Manual prompts, custom logging |
Why It Matters in 2026
Traditional rank tracking tools don't capture LLM mentions. You can rank #1 for "best domain registrar" while AI systems consistently recommend competitors. Without measuring AI visibility directly, you're optimizing for a shrinking portion of how users discover products.
Decision Framework: Selecting Golden Prompts
Golden Prompts are controlled queries you'll track consistently across three categories:
Branded Queries: "What is [YourBrand]?" confirms AI recognizes your entity.
Solution Queries: "What's the best way to [solve problem]?" reveals whether AI recommends you when users describe needs without naming brands.
Comparison Queries: "[YourBrand] vs [Competitor]" shows competitive positioning.
Example 1 - Domain Registrar Golden Prompts:
- "What's the cheapest domain registrar?"
- "How do I transfer a domain?"
Start with 10-15 prompts. Run weekly across ChatGPT, Perplexity, and Google AI Overview.
Implementation Steps: Building a Tracking Sheet
Create a spreadsheet with these columns:
Calculate Share of Model: (Prompts where you appear ÷ Total prompts) × 100. Track weekly and correlate changes with content updates.
Example 2 - Tracking Insight:
After adding FAQPage schema to your pricing page, Share of Model for "cheapest domain registrar" increases from 40% to 70%. This proves the schema update improved AI extraction.
Common Mistakes: Tracking Only Branded Queries
Branded queries only confirm AI recognizes your entity. They don't reveal whether AI recommends you when users haven't decided to research your brand yet.
Solution queries matter more for growth. "How do I register a domain?" captures users earlier in their journey. If AI answers without mentioning you, you're invisible during consideration regardless of branded recognition.
Balance your prompts: 20% branded, 60% solution, 20% comparison.
What This Means for You
Share of Model connects directly to content and technical work you're already doing. When you publish chunk-optimized documentation, implement author schema, or create an llms.txt file, this tracking shows whether efforts translate to AI visibility.
On the NameSilo blog, we track how our documentation performs across AI platforms. When competitors appear for queries we should own, we audit content for missing context or weak signals. Share of Model turns AI optimization from guesswork into measurable practice.
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