A query library is a structured set of test queries a B2B company runs regularly across AI platforms to measure Share of LLM, identify citation gaps, and track competitor positioning. Without a query library, AEO measurement becomes anecdotal. You check a few queries when you think about it, draw no conclusions, and make no decisions. With a query library, AEO becomes a repeatable practice: same queries, same platforms, tracked over time, producing trends rather than snapshots.
The query library is the foundation of all five AEO metrics. Share of LLM, LLM Position, Citation Accuracy, Query Coverage, and Citation Source Mix all depend on having a defined query set. Without consistent queries, you have data points but no trends.
What Are the Five Query Categories Every B2B Library Needs?
Every query library must cover the complete buyer journey through AI research. Five categories capture how B2B buyers actually search and where citation gaps typically appear.
Pain-first queries use no vendor names or category language. These capture early-stage research where buyers frame problems before looking for solutions. Example: “how do B2B companies reduce manual invoice processing” rather than “best AP automation software.” Buyers ask these questions before they know your category exists.
Category discovery queries help buyers build initial shortlists. Format: “best [category] platforms for [ICP descriptor].” Example: “best marketing automation platforms for B2B SaaS companies.” These queries reveal whether AI includes you in the consideration set and how it describes your positioning relative to competitors.
Comparison queries cover feature-level evaluation. Include both “[your company] vs [top competitor]” and “[competitor A] vs [competitor B]” formats. These reveal how AI positions you relative to competitors and what language it uses to describe strengths and weaknesses.
Technical validation queries address integration, compliance, and implementation questions. Format: “does [your company] [specific capability].” Example: “does Salesforce integrate with NetSuite” or “is HubSpot SOC2 compliant.” Companies are often most invisible in these queries because answers require ungated technical documentation.
Branded queries show what AI says about you directly. Include both “[your company name]” and “[your company name] [category]” variants. These reveal Citation Accuracy problems: wrong market segment, outdated feature descriptions, or competitor confusion.
How Do I Size My Query Library?
Start with a minimum viable library of 20-30 queries covering all five categories. This provides representative coverage without operational overhead. Budget 5-6 queries per category and 2-3 hours per month to run the full set.
A growth library scales to 50-75 queries by adding more use cases, buyer segments, and competitor pairs. This depth reveals patterns across different buyer personas and competitive scenarios but requires 4-5 hours monthly to execute.
Comprehensive libraries exceed 100 queries with full buyer journey coverage. Most companies never need this depth. The operational cost outweighs the insights unless you have dedicated AEO resources.
The practical rule: start small and add 2-3 test queries for every new content page you publish. If you create a case study about manufacturing customers, add queries like “best [category] for manufacturing companies.” If you publish technical integration guides, add corresponding technical validation queries.
How Do I Build the Initial Library?
Start with your top 5 pain points from customer interviews or win/loss analysis. Turn each into a pain-first query that captures how prospects frame problems before they know solutions exist.
Next, write the 3-5 most common category queries your ICP uses. Ask your sales team what buyers say they were researching when they found you. These queries form your category discovery set.
Add your top 3 competitor comparison pairs. Include at least one query where you are not in the comparison to see how AI frames your category without your input. Example: if you compete with HubSpot and Salesforce, include “HubSpot vs Salesforce” alongside “[your company] vs HubSpot.”
Build your technical validation set from your most common integration questions and compliance requirements. Start with your primary integrations and certifications. If prospects consistently ask about Slack integration or SOC2 compliance, include those queries.
Finish with 3-5 branded queries. Include your company name alone, your company name plus category, and your company name plus your most common use case. Example: “Gong,” “Gong sales intelligence,” and “Gong conversation analytics.”
How Do I Run the Library and Record Results?
Run every query across ChatGPT, Claude, and Perplexity at minimum. These three platforms cover the majority of B2B buyer AI research. Add Gemini if your buyers are heavy Google Workspace users, but three platforms provide sufficient coverage for most companies.
Record which platforms cited you, where you appeared in the response, how you were described, which competitors appeared, and whether the description was accurate. Track both presence and positioning quality.
Use a simple spreadsheet with one row per query, columns for each platform, a notes column for description quality, and a date column. Structure tracks trends without creating maintenance overhead. Complex tracking systems get abandoned.
Run the library monthly. AEO changes slowly. Weekly tracking captures noise, not signals. Quarterly tracking misses emerging patterns. Monthly frequency provides meaningful trend data without excessive time investment.
Budget 2-3 hours per month for a 25-30 query library across three platforms. Scale accordingly as your library grows. The key is consistency over depth.
How Do I Turn Query Library Results into Content Decisions?
Queries where you never appear signal content gaps. Either the page does not exist, it is gated, or it is poorly structured. Each missing query represents a specific content investment opportunity.
When competitors appear and you do not, analyze the competitor page being cited. What does it have that you do not? Usually: more specific detail, better structure, or ungated access. Clone the approach, not the content.
Inaccurate descriptions indicate Citation Accuracy problems. AI is pulling from outdated or inconsistent sources. Audit which sources are being cited and update them. Check your about pages, feature descriptions, and press releases for consistency.
Appearing in position 4 or 5 indicates an LLM Position problem. You are being cited but not prominently. Make your content more specific and front-load key information for that topic.
Track content investments back to query results. When you publish a new integration page, add the corresponding integration query to the library. Monitor whether you start appearing within 4-6 weeks. This connects content effort to citation presence.
What Are Common Query Library Mistakes?
Only tracking branded queries tells you nothing about how buyers find you before they know your name. The majority of B2B buyer research happens in the pain-first and category discovery phases.
Only tracking queries you already rank for creates confirmation bias. The library should surface gaps, not confirm existing wins. Include queries where competitors dominate to understand what content you are missing.
Running too many queries leads to burnout. A 100-query library that never gets executed is worthless. Start with 25 queries and run them consistently rather than creating an ambitious library that gets abandoned after two months.
Not tracking descriptions and positioning quality misses half the value. Appearing in AI responses with inaccurate positioning creates wrong impressions that are harder to correct than absence. Track what AI says about you, not just whether it mentions you.
A single month of data provides a snapshot. Six months reveals trends. Twelve months tells you whether your AEO investments are working and which content drives actual citation presence. The compounding value of consistent query library execution becomes clear over time, but only if you maintain the discipline of regular measurement.
A query library is a structured set of test queries run regularly across AI platforms to measure Share of LLM, identify citation gaps, and track competitor positioning. Without one, AEO measurement becomes anecdotal—checking a few queries occasionally without drawing conclusions or making decisions. With a query library, AEO becomes repeatable: the same queries tracked across the same platforms over time produce trends rather than isolated snapshots.
Every query library should cover: pain-first queries (problem-focused, no vendor names), category discovery queries (best platforms for specific ICPs), comparison queries (feature-level evaluation between companies), technical validation queries (integration and compliance questions), and branded queries (direct company name searches). Together, these categories capture the complete B2B buyer journey through AI research.
Pain-first queries use no vendor names or category language and capture early-stage research where buyers frame problems before seeking solutions. An example is ‘how do B2B companies reduce manual invoice processing’ rather than ‘best AP automation software.’ These queries represent how buyers search before they even know your category exists.
Start with a minimum viable library of 20-30 queries covering all five categories. This provides representative coverage without excessive operational overhead. The recommended approach is to budget 5-6 queries per category, ensuring balanced representation across all buyer journey stages.
The query library is the foundation of all five AEO metrics: Share of LLM, LLM Position, Citation Accuracy, Query Coverage, and Citation Source Mix all depend on having a defined query set. It serves as the operational bridge between AEO strategy and weekly execution, enabling consistent measurement over time.