How Do I Research AI Search Queries?

Understanding AI Search Query Research

Researching AI search queries is fundamentally different from traditional keyword research because AI search engines operate on different principles than conventional search engines. Instead of ranking pages on a results page, AI platforms like ChatGPT, Perplexity, Claude, and Gemini synthesize information from multiple sources to generate direct answers to user questions. This shift means you need to understand not just what people search for, but how they phrase questions when interacting with AI chatbots and how your content appears in AI-generated responses. The goal is to ensure your brand and content are cited when AI systems answer questions relevant to your industry or expertise.

Identifying Core Prompts Your Audience Uses

The foundation of AI search query research begins with identifying the natural language questions your target audience asks when using AI tools. Unlike traditional keyword research that focuses on short, fragmented search terms, prompt research emphasizes complete questions and conversational phrases. Start by analyzing customer support conversations, frequently asked questions on your website, and common inquiries in your industry forums and social media channels. These sources reveal the exact language people use when seeking information, which directly translates to how they’ll phrase questions in AI chatbots.

Create a comprehensive list of these prompts organized by customer journey stage—awareness, consideration, and decision. For example, if you’re in the software industry, your prompts might include questions like “What is the best project management tool for remote teams?” or “How do I integrate project management software with my existing workflow?” These conversational questions are far more representative of how people interact with AI search engines than traditional keywords. Document at least 20-30 core prompts that represent the main topics and questions your audience cares about, as this becomes your baseline for monitoring AI search visibility.

Monitoring Across Multiple AI Platforms

AI search is fragmented across multiple platforms, each with different user bases, data sources, and retrieval methods. Monitoring only one platform gives you an incomplete picture of your AI search visibility. The major platforms you should track include ChatGPT with over 800 million weekly users, Google AI Overviews appearing on billions of searches, Claude which is growing rapidly with Safari integration, Perplexity which specializes in research-oriented queries, and Gemini as Google’s standalone AI assistant. Each platform pulls from different data sources and uses distinct algorithms to determine which sources to cite in their responses.

To effectively monitor across these platforms, you need a systematic approach. Start by running your core prompts through each AI platform and documenting which sources appear in the responses. Note whether your brand is mentioned, whether your website is cited, and how prominently your content appears in the answer. This manual process gives you baseline data, but for ongoing monitoring, consider using dedicated GEO (Generative Engine Optimization) tracking tools that automate this process and provide statistical sampling across multiple queries and platforms.

Tracking Citation Frequency and Brand Mentions

Citation frequency is the most critical metric in AI search research because it directly impacts whether users discover your brand. Unlike traditional search where appearing on page one matters, in AI search, being cited in the answer is what counts. When an AI system generates a response, it typically cites only a handful of sources—often just three to five. If your content isn’t among those cited sources, users won’t see your brand or have a pathway to your website. Track how often your website appears as a source in AI-generated answers for your target prompts, and monitor whether mentions of your brand appear in the answer text itself.

Beyond simple citation counting, pay attention to the context in which your brand is mentioned. Is it presented as a primary source, a supporting reference, or mentioned in passing? AI systems often weight sources differently based on authority, relevance, and content quality. Additionally, track sentiment and positioning—how does the AI characterize your brand or product? If an AI system describes your competitor as “industry-leading” while describing you as “an alternative option,” that positioning gap represents an optimization opportunity. Use spreadsheets or dedicated GEO tools to maintain historical records of citation frequency over time, as this helps you identify trends and measure the impact of your content optimization efforts.

Measuring Share of Voice in AI Answers

Share of voice in AI search represents your brand’s mention rate compared to competitors in AI-generated answers for your target queries. If a competitor appears in 60% of relevant AI responses while you appear in only 15%, that gap represents lost visibility and potential customers. To calculate share of voice, run your core prompts through each AI platform multiple times (since AI responses can vary), document which brands are mentioned, and calculate the percentage of responses where each brand appears. This competitive intelligence reveals where you’re losing ground and identifies queries where small content improvements could earn you citations.

Share of voice analysis also helps you prioritize your optimization efforts. Focus first on queries where you’re close to visibility—perhaps you appear in 20% of responses while competitors appear in 40%. These represent quick wins where targeted content improvements could significantly boost your visibility. Conversely, queries where you have zero visibility but competitors dominate may require more substantial content creation efforts. Track share of voice monthly or quarterly to identify trends, and use this data to inform your content strategy and resource allocation decisions.

Using GEO Tracking Tools for Systematic Monitoring

Manual monitoring of AI search queries becomes impractical at scale, which is why dedicated GEO (Generative Engine Optimization) tracking tools have become essential infrastructure for modern marketing. These tools automate the process of running your prompts through multiple AI platforms, tracking citations, measuring brand visibility, and comparing your performance against competitors. A comprehensive GEO tracker should measure citation frequency (how often your site appears as a source), brand visibility score (a composite metric showing how prominently your brand appears), AI share of voice (your mention rate versus competitors), and geographic performance (how visibility varies by location).

When selecting a GEO tracking tool, look for platforms that monitor all major AI search engines, provide historical trend data, offer competitive benchmarking, and support location-based analysis. The tool should also allow you to track custom prompts specific to your industry and business, rather than limiting you to predefined keywords. Many GEO tools offer free tier options that let you run initial visibility checks before committing to paid plans. Start with a baseline assessment using a free tool to understand your current AI search visibility, then consider upgrading to a comprehensive dashboard if you need ongoing monitoring and deeper competitive intelligence.

Analyzing AI Response Patterns and Content Gaps

Effective AI search query research requires analyzing not just whether you’re cited, but why certain sources are chosen over others. When you run your prompts through AI platforms, examine the sources that appear in responses and identify common characteristics. Are cited sources longer-form content or concise summaries? Do they include statistics, expert quotes, and data visualizations? Are they structured with clear headers and TL;DR sections? Understanding these patterns reveals what makes content citation-worthy in the eyes of AI systems.

Additionally, identify content gaps where competitors appear but you don’t. Analyze what topics they cover, how they structure their content, and what makes their material attractive to AI systems. This competitive analysis often reveals opportunities to create content that fills gaps in your current offerings. For instance, if competitors consistently appear in responses about “best practices” but you don’t, you might need to create comprehensive best practices guides. If competitors dominate responses about specific use cases or industry verticals, consider creating targeted content for those segments. Use this gap analysis to inform your content strategy and ensure you’re creating material that addresses the questions your audience asks in AI search engines.

Optimizing Content for AI Search Visibility

Once you understand how AI search queries work and what makes content citation-worthy, you can optimize your existing content and create new material specifically for AI search visibility. Increase fact density by adding statistics, research findings, and verifiable data points throughout your content. AI systems prefer content packed with concrete information rather than general statements. Include citations and quotes from authoritative sources, as research suggests this boosts AI visibility by over 40%. Structure your content with clear headers, TL;DR summaries, and FAQ sections, as AI engines often pull structured content directly into their answers.

Build entity authority by ensuring your brand information is consistent across trusted sources that AI platforms rely on. This includes maintaining accurate business information on platforms like Google Business Profile, industry directories, and authoritative review sites. Create comprehensive content that covers your core topics thoroughly, including adjacent concepts and related questions your audience might ask. Rather than creating thin content on many topics, focus on creating authoritative, in-depth content on fewer topics that clearly establishes your expertise. Update your content regularly, as AI systems continually retrain on new information, and stale content loses visibility over time.

Establishing Ongoing Monitoring and Reporting

Sustainable AI search visibility requires ongoing monitoring, not just one-time analysis. Establish a regular monitoring cadence—monthly or quarterly—to track changes in your citation frequency, brand visibility, and share of voice. Create dashboards or reports that visualize trends over time, making it easy to identify whether your visibility is improving, declining, or remaining stable. Include competitive benchmarking in your reports so you can see not just your absolute metrics, but how you’re performing relative to competitors.

Use your monitoring data to inform iterative optimization. If you notice declining visibility for a particular query, investigate whether competitors have published new content, whether your content has become outdated, or whether you need to adjust your content strategy. Conversely, when you see visibility improvements, analyze what drove those gains so you can replicate success in other areas. Integrate GEO monitoring into your broader marketing analytics and reporting infrastructure, treating AI search visibility as a core metric alongside traditional SEO rankings and organic traffic. This ensures AI search optimization remains a priority and receives appropriate resources and attention from your marketing team.