What is an AI search query set?

An AI search query set is a stable list of buyer questions used to measure how AI answer engines mention, cite, and compare a brand.

How many queries should I start with?

Start with 20 to 40 queries across category, use case, comparison, alternative, pricing, proof, and technical intents.

How often should I change the query set?

Keep the core set stable for comparability. Add or archive queries intentionally when markets, competitors, or product positioning change.

How to Build an AI Search Query Set

Your AI visibility report is only as useful as the questions behind it.

If the query set is too narrow, you will overestimate visibility. If it is too broad, the team will not know what to fix. If it changes every week, the trend line becomes noise.

This guide shows how to build a query set that can power repeatable AEO monitoring.

Start With Buyer Jobs

Do not start with keywords. Start with the jobs buyers are trying to complete.

For a B2B SaaS product, those jobs often look like:

Understand a category.
Find tools for a use case.
Compare vendors.
Replace a competitor.
Check pricing or packaging.
Validate security or compliance.
Confirm integrations.
Find proof that the tool works.

Each job should become a small group of prompts.

Use Seven Query Groups

Use these groups for the first baseline:

Group	What it measures	Example
Category	Whether your brand appears in category discovery	"What is answer engine optimization software?"
Use case	Whether your product appears for a workflow	"How do I monitor ChatGPT brand mentions?"
Alternative	Whether you appear as a substitute	"Best alternatives to [competitor]."
Comparison	Whether you appear in shortlist evaluation	"[Brand] vs [Competitor]."
Pricing	Whether AI answers understand cost expectations	"How much does AI visibility monitoring cost?"
Proof	Whether answer engines can cite evidence	"Which sources explain AI citation tracking?"
Technical	Whether implementation questions find your docs	"How should a SaaS site handle AI crawlers?"

You do not need ten prompts in every group. You need enough coverage to see patterns.

Write Prompts Like Buyers, Not Marketers

Avoid prompts that only your team would use. Buyers rarely ask for your internal positioning language.

Instead of:

"What is the leading answer intelligence platform for AI-native brand visibility?"

Use:

"How do I track whether ChatGPT mentions my brand?"

Instead of:

"What is the best AEO observability system?"

Use:

"Best tools for monitoring AI search visibility."

Plain language prompts are better because they match how buyers actually research.

Include Competitor And Substitute Prompts

Competitor prompts are uncomfortable, but they are essential.

Add:

"[Competitor] alternatives."
"[Brand] vs [Competitor]."
"Best tools like [Competitor]."
"Which is better for [use case], [Brand] or [Competitor]?"
"What are the top [category] tools for [audience]?"

Also include substitutes. A buyer may not compare you only against direct competitors. They may compare you against spreadsheets, agencies, SEO tools, analytics platforms, or doing nothing.

That is why AEO Table includes a manual AI visibility tracking comparison.

Keep The Core Set Stable

A query set should evolve, but not constantly.

Use three layers:

Core queries: 20 to 40 prompts that stay stable for trend reporting.
Campaign queries: temporary prompts tied to a launch, market, or content push.
Exploration queries: experimental prompts that may become core later.

Only core queries should drive the main benchmark. Campaign and exploration queries are useful, but they should not rewrite the trend line every week.

Map Each Query To A Page

Every important query should have a target source page.

If the query is "How do I track brand mentions in ChatGPT?", the target page might be a guide, use-case page, or product page.

If the query is "[Brand] vs manual tracking", the target page should be a comparison page.

If the query is "How do AI citations work?", the target page may be an explainer or research page.

When no target page exists, the query set has found a content gap.

Review Query Quality After The First Run

After the first Run, remove weak prompts:

Prompts that are too vague to create a useful answer.
Prompts that no buyer would ask.
Prompts that always produce general education with no vendor relevance.
Prompts that duplicate another query too closely.

Add prompts when:

A competitor appears in a new way.
Sales hears a recurring buyer question.
Search Console shows a rising query.
Product positioning changes.
A new category term becomes common.

The Bottom Line

An AI search query set is the foundation of AEO monitoring.

Build it from buyer jobs. Cover category, use case, alternative, comparison, pricing, proof, and technical intents. Keep the core stable. Map each important question to a page. Then run the same Task over time.

Create a free AEO Table account to turn your query set into a repeatable AI visibility baseline.