How Do You Use Prompts and Queries to Effectively Monitor AI Search Performance?

Key Takeaways

Increasing AI visibility requires a whole new approach from the traditional SEO playbooks. That’s why we created the State of AI Search 2026 Report: to share trends we’ve observed from millions of data points spanning 8+ LLMs, including ChatGPT, Claude, and Perplexity. However, we also wanted to go deeper and contextualize our findings, turning to extensive conversations with marketers grappling with a whole new acquisition channel that forms its own opinions on which brands or products to recommend. Today, we’re exploring one of the most pressing questions around performance monitoring using prompts and queries. 

Methodology

Between December 2025 and March 2026, we collected and analyzed millions of AI-generated responses across B2B and B2C. We also layered the product data with a supplementary analysis of over 500 conversations in Q2 (April-June 2026), including enterprise demos, strategy sessions, and onboarding calls. We identified the most common questions and unresolved priorities for marketing teams building out GEO programs. These companies include both enterprise and mid-market teams, with a median average revenue of $933 million and median average size of 755 employees.

Each question in this blog series is answered with benchmark metrics compiled from our product data, along with a series of recommended action items to improve your GEO efforts.

You can view the full reporthere.

How Do You Use Prompts and Queries to Effectively Monitor AI Search Performance?

Athena’s discovery-prompt methodology simulates the real questions consumers actually ask AI engines,  rather than relying on keyword lists carried over from traditional SEO. 

Applied at scale, this approach surfaces a clear content-intent hierarchy: Informational prompts drive 36.2% of citations, Comparative or Selection prompts drive 23.2%, Acquisition or Obtaining prompts drive 15.1%, and Learning or Education prompts drive 13.4%. The remaining content categories, Navigation, Consumption, Updates, Investigation, and Optimization, each account for less than 4% . 

Distribution also shifts significantly by AI model: Learning and Education content over-indexes in Gemini (representing 19.4% of its citations) relative to Grok (9%), while Comparative content is far more prominent in AI Overview (24.2%) than in AI Mode (20.4%).

This means your prompt set needs to mirror the same distribution, weighted toward informational and comparative framing, rather than a list of branded and unbranded keywords. 

The quarter-over-quarter deltas are just as important as the snapshot. Comparative content share fell 1.9 points and Acquisition content fell 2.2 points between Q1 and Q2, even as Informational content gained 3.8 points. This suggests the models are  leaning slightly more toward definitional and explanatory answers. 

Action Items

For more insights, check out the full State of AI Search 2026 report. 

FAQs

How do you use prompts and queries to effectively monitor AI search performance?
Effective monitoring relies on a discovery-prompt methodology that simulates the actual questions consumers ask AI engines, rather than a traditional SEO-style keyword list. This approach reveals a content-intent hierarchy showing which types of prompts most frequently drive citations, allowing marketing teams to build a monitored prompt set that reflects real buyer behavior instead of assumptions carried over from search.

What is the content-intent hierarchy in AI search citations?
The content-intent hierarchy ranks prompt types by how often they drive citations: Informational prompts account for 36.2%, Comparative or Selection prompts account for 23.2%, Acquisition or Obtaining prompts account for 15.1%, and Learning or Education prompts account for 13.4%. The remaining categories, including Navigation, Consumption, Updates, Investigation, and Optimization, each account for less than 4%.

Why does Informational content drive the most citations?
Informational prompts represent the largest share of citations because AI engines are frequently asked to define, explain, or provide background context. Additionally, this category has been trending upward, gaining 3.8 percentage points between Q1 and Q2 2026, which suggests AI models are increasingly leaning toward definitional and explanatory answers over other content types.

Does prompt intent distribution vary by AI model?
Yes, significantly. Learning and Education content represents 19.4% of citations in Gemini but only 9% in Grok. Similarly, Comparative content is more prominent in AI Overview, at 24.2% of citations, than in AI Mode, at 20.4%. This means a prompt set optimized for one AI model may not reflect intent weighting in another, making cross-model testing important.

How should marketing teams structure their monitored prompt set?
Prompt sets should mirror the real intent distribution rather than weighting toward branded terms: roughly a third informational, a quarter comparative, and the remainder split across acquisition, education, and long-tail categories. Teams should also run these prompts across every AI model their buyers actually use, since intent weighting shifts meaningfully by model.

Why is quarter-over-quarter tracking important for GEO monitoring?
Quarter-over-quarter deltas reveal shifts in what AI models prioritize before those shifts show up as a visibility drop. Between Q1 and Q2 2026, Comparative content fell 1.9 points and Acquisition content fell 2.2 points, even as Informational content gained 3.8 points, showing that intent shares are not static and require ongoing tracking rather than a one-time snapshot.

How should content production be prioritized based on this data?
Teams should prioritize new content toward whichever intent category is both high-share and currently trending upward in their specific vertical. That combination signals where near-term citation opportunity is expanding, making it a more effective prioritization signal than focusing solely on the largest category in isolation.

What data supports these prompt-monitoring findings?
The findings are based on millions of AI-generated responses collected between December 2025 and March 2026 across B2B and B2C, supplemented by an analysis of over 500 conversations in Q2 2026 (April–June), including enterprise demos, strategy sessions, and onboarding calls with enterprise and mid-market teams averaging $933 million in revenue and 755 employees.