TL;DR version

Choosing which questions to track in AI search starts with defining a clear goal, establishing a baseline through experimentation, sourcing questions from real customer and sales signals, and iterating toward influence, not volume. The objective isn’t to track everything, but to track what matters.

Customers keep asking me, “What questions should we track?”

Doing my best CPA impression, I respond, “It depends.”

Tracking the wrong questions in AI search creates noise, not insight. The goal isn’t to track everything; it’s to track the questions that meaningfully affect visibility, trust, and revenue.

Step 1: Define the goal (and limit the scope)

Before entering a single question, answer this:

What are we trying to influence or understand?

Common goals

Some common goals our existing users have had include:

• Ensuring existing content is surfaced in AI answers.
• Confirming the AI accurately describes your brand.
• Building trust with your prospects.
• Comparing favorably against competitors.
• Establishing thought leadership in a category.

Each goal leads to a different question set. We don’t want to stop there, though.

If you don't define constraints, the scope becomes a behemoth of an undertaking—killing the project before you even get to the optimization part.

If you don’t define constraints, the scope becomes a behemoth of an undertaking. Thus killing the project before you even get to the optimization part!

Good constraints

Good constraints look like:

• A specific product, feature, or rebrand
• A funnel stage (top-of-funnel versus sales objections)
• A category or comparison set
• A campaign timeframe

Once you’ve defined your goal and constraints, it’s time to start experimenting!

Step 2: Establish a baseline through experimentation

Have you and a colleague tried asking ChatGPT the same question but got different results? Or did you get a different answer from Google’s AI Overviews even though you asked the same question just yesterday?

This is because models change, training data evolves, and your context/history can impact the data returned.

I know what you’re thinking, “Why even bother tracking anything at all?”

Related: AI search results do fluctuate, but not as randomly as you might think. Learn why in our guide → AI Search Ranking Fluctuations

Variability is surface-level

The data shows that the variability is mostly surface-level, not structural.

• The phrasing of answers may change.
• The order of brands may shift.
• The citations may rotate slightly.

But the underlying source set AIs pulls from is surprisingly stable.

The proof: 1,000 questions, 74 brands

Seer Interactive ran an experiment with 1,000 semantically similar versions of the same question.

Each question was fed into an LLM, and guess what?

• Only 74 unique brands showed up across the answers to these 1,000 questions.
• On average, six brands appeared per answer.

If AI answers were truly random, we would expect chaos at scale.

With 1,000 semantically equivalent questions and an average of 6 brands per answer, we could have seen up to 6,000 unique brands - but there were only 74.

That’s not randomness, that’s constant.

Best practices for baselining

The best practices here are to:

• Track frequently (often daily) at first.
• Treat early data as directional, not definitive.
• Look for patterns, not single-answer wins.

Step 3: Source questions from real signals (not guesses)

The best questions already exist. You just need to collect them.

The best questions already exist. You just need to collect them.

High-quality sources include:

1. Existing content

Audit your site and ask:

What question is this page actually answering? What intent does it serve?

This helps you begin to move from keyword-thinking to intent-thinking.

2. Sales calls & demos

Sales conversations are gold. Every time I’m on a demo call and get a new objection, clarifying question, comparison insight, or pricing concern, I track those in Knowaota.

If prospects are bringing these up during a call, AI users will bring them up in their conversations.

If prospects are bringing these up during a call, AI users will bring them up in their conversations.

3. Funnel stage coverage

Track questions across the journey:

• Exploratory: “What is geocoding?”
• Evaluative: “What are the best geocoding API providers?”
• Comparative: “How trustworthy is Geocodio?” (See our Competitive Ranking guide for more on comparison questions)
• Transactional: “What is Geocodio’s pricing model?”

Remember, your question set should reflect your goals and constraints.

Step 4: Iterate, observe patterns, then design campaigns

In the first couple of weeks, you should add questions and observe patterns in the answers.

Are several questions returning similar data? They have the same intent: keep one and toss out the others.

Are there opportunities to improve the answers? If so, those are the golden ones to keep.

Over time, you’ll be able to reduce how often you check the answers.

From measurement to strategy

This is where AI tracking shifts from measurement to strategy.

This is where AI tracking shifts from measurement → strategy. And where you can start coming up with content, PR, or authority-building campaigns around the answers you want to influence.

Next step: Ready to optimize? Check out our B.I.S.C.U.I.T. Framework for a complete AI search optimization strategy.

Sources:

• Seer Interactive - “What questions should I track in LLMs for AI brand monitoring?” https://www.seerinteractive.com/insights/what-questions-should-i-track-in-llms-for-ai-brand-monitoring