Query Classifiers
The 8 Query Spaces: How what you ask determines what you get
Source: Be Datable. The three intersecting dimensions that determine AI citation sources.
Why This Matters for Your Business
The structure of your question determines which sources AI will cite. This isn't random. AI systems classify queries along three dimensions and select sources accordingly. If you understand this, you control the quality and type of information you receive.
For businesses, this framework reveals where your content needs to appear. If customers ask objective, branded questions ("What time does Wendy's open?"), you need structured data on official channels. If they ask subjective, unbranded questions ("Best burger near me?"), you need presence on review platforms.
Most companies optimize for one query type while ignoring the others. That's leaving traffic and conversions on the table.
The Three Dimensions
1. Objective vs Subjective
Does the query have a verifiable answer, or is it a matter of opinion?
"What is the capital of France?" "Store hours?" "Tesla Model 3 range?"
"Best pizza nearby?" "Is it worth the price?" "Should I invest?"
2. Branded vs Unbranded
Does the query reference a specific company, product, or entity?
"Nike running shoes" "Tesla FSD capabilities" "Wendy's menu"
"Best running shoes" "Self-driving car technology" "Fast food options"
3. Simple vs Complex
Does the query need a direct answer or multi-faceted analysis?
"Store hours" "Phone number" "Current price"
"How does ML work?" "Should I buy a house now?" "Investment strategy"
How to Use This Framework
When Using AI for Research
- For factual answers (objective), phrase your query to get official/academic sources. Add specificity.
- For opinions (subjective), expect review sites and social content. Verify the sample size.
- For complex questions, AI will often ask clarifying questions first. Provide context upfront to skip this step.
- When you need authoritative sources, use objective + unbranded + complex framing ("How does X work?" pulls academic papers).
For Your Content Strategy
- Map your customers' likely queries across all 8 spaces. Which do you own? Which are you missing?
- Objective + Branded queries need structured data (hours, prices, specs) on your official site.
- Subjective + Branded queries need review management and customer testimonials.
- Unbranded queries (where customers don't know you yet) need SEO and content marketing presence.
For Product Teams
- Build intent classification into your own AI products. Route queries to appropriate handlers.
- Simple + Objective queries can be automated confidently.
- Complex + Subjective queries need guardrails or human review.
- Track which query types your users ask most. Optimize for those first.
Quick Comparison
| Query Type | Typical Sources |
|---|---|
| Objective + Complex + Unbranded | Government agencies, academic papers, professional associations, .edu/.gov sites |
| Objective + Simple + Branded | Official company website, structured data, Google Business Profile |
| Subjective + Simple + Unbranded | Yelp, Google Reviews, Reddit, local blogs, social media |
| Subjective + Complex + Branded | Expert reviews, comparison sites, industry analysts, financial reports |
Self-Assessment
What are the top 5 queries customers use to find businesses like yours?
Classify each by the three dimensions. Which spaces do they fall into?
For each space, where would AI pull citations? Do you have presence there?
Which query space represents your biggest gap? Start there.
Query structure determines citation sources. And citations matter.
Understanding this gives you control. Control over the information you receive from AI. Control over where your business appears when customers ask questions.
Sources and Further Reading
- Original Be Datable post: "Trust & AI: Showing Your Work"
- Vectara: AI Model Hallucination Rates
- MIT: ContextCite for trustworthy AI content
Related Frameworks
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