What it is
In AI Visibility, search terms are the prompts you send to AI engines, and topics are the way you group those prompts for analysis and reporting.
Search term = The exact query sent to an AI engine
Topic = A grouping layer that organizes related search terms
Together, they form the foundation of how AI visibility is measured.
Unlike traditional SEO keyword tracking, AI search terms are often written as natural language prompts, similar to how a user would ask a question in ChatGPT or Gemini.
Why it matters
AI search is prompt-driven, not keyword-driven.
That means:
Small wording changes can influence responses
AI engines interpret semantic meaning
Context affects which brands are surfaced
If search terms are poorly structured:
Visibility data becomes noisy
Competitive comparisons lose clarity
Trends become difficult to interpret
Credit usage may be inefficient
Clear topic structure allows you to:
Segment visibility by strategic themes
Compare performance across product areas
Identify which areas drive AI presence
Detect gaps in authority or coverage
Search terms determine what is measured.
Topics determine how insight is extracted.
What is a search term?
A search term in AI Visibility is:
The exact prompt submitted to a selected AI engine
Executed on a defined update schedule
Evaluated for brand mentions, citations, sentiment, and position
Examples:
“What is the best CRM software for small businesses in 2025?”
“Top project management tools for remote teams”
“Is [brand] better than [competitor]?”
Each search term is tracked separately per AI engine.
This means:
The same prompt across multiple engines produces separate data streams
Visibility may differ per engine
What is a topic?
A topic is a grouping layer used to organize related search terms.
For example:
Topic: CRM software
“Best CRM for startups”
“Affordable CRM tools for small business”
“HubSpot vs Salesforce comparison”
Topics allow you to:
Aggregate visibility across related prompts
Filter dashboard performance
Identify which themes drive brand presence
Compare competitor strength by category
Without topics, search terms exist in isolation.
With topics, you gain structured, focused insights.
How search terms and topics work together
When you:
Add a search term
Assign it to a topic
Select AI engines
Set update frequency
The system:
Executes the prompt
Collects brand and citation data
Aggregates results by topic
Displays performance across engines
This creates multiple analysis layers:
Term-level visibility
Topic-level visibility
Engine-level visibility
Brand-level trends
How AI prompts differ from SEO keywords
Traditional SEO:
Tracks static keywords
Measures ranking position in SERPs
AI Visibility:
Tracks full prompts
Measures brand presence inside generated answers
Evaluates context, citations, and sentiment
AI engines interpret meaning, not just exact phrasing.
That means:
“Best CRM software” and
“What CRM tool is best for startups?”
may produce similar but not identical results.
Choosing meaningful prompt structure is critical for accurate visibility tracking.
What to expect
Slight variation across runs is normal
Some prompts generate citation-heavy answers
Others generate summarized brand lists
Competitive prompts may shift visibility patterns
Search term structure directly impacts:
Detection rate
Average position
Citation frequency
Competitive comparisons
Over time, well-structured topics provide clearer strategic direction.
Best practices
Group search terms into logical, business-aligned topics
Avoid creating too many overlapping prompts
Use full natural language questions when possible
Track multiple engines for important topics
Review term-level results before scaling