Article
Aug 19, 2026
How to Build an AI Search Query Tracking List
This post covers why conversational prompts matter more than keyword-style ones, where to find real buyer language instead of guessing it, and how to turn that language into a tracking list worth reporting on.
User behaviour has completely shifted in AI search
You learned to search in fragments because that's what Google could work with: "crm software small business," "best running shoes marathon." and how user behaviour functioned in a traditional search engine.
Nobody talks to an LLM like that. People ask ChatGPT, Perplexity, or Gemini the way they'd ask a colleague who actually knows the space: full sentences, real context, a budget, a team size, a tool they already use and want the new one to play nicely with.
If the prompts in your tracking list don't sound like that, they're not measuring anything close to how your buyers actually use in AI search. You end up with a dashboard that looks thorough and tells you almost nothing you can act on.
The real question isn't "what prompts should I track." It's "where do I find the sentences my buyers are already typing, in their own words, before they ever land on my site."
How to find real buyer language for LLM query tracking
On a recent client project, I connected the client's stack via MCP and pulled real language straight out of the tools that already had it sitting in them:
Website form fills. The client had an open text field on their contact form asking "what service do you need?" That's about as close to a raw, unprompted buyer sentence as you'll get. No dropdown, no multiple choice, just someone typing what's actually in their head.
Sales call transcripts (Gong). Sales calls are full of prospects describing their problem before they know your product's name for it. I pulled the transcripts into Claude and had it surface the recurring phrases, objections, and constraints prospects kept bringing up unprompted.
Customer reviews (G2). Existing customers describing your product, and your competitors', in their own words, usually including the exact use case, team size, or comparison that got them to convert. Reviews are close to pre-written prompts.
Long-tail Search Console queries. The messier, more specific searches sitting below your top-performing keywords. These are already close to conversational and show you the questions people couldn't fully answer with a normal search.
Feed language from all four into Claude, ask it to cluster the recurring phrases and themes, and you get a prompt list built from what people actually say - not what you assume they'd say.
Turning raw language into an actual tracking list
That raw language is the input but it isn't the tracking list yet. A few things to do before it is.
Sort it by business priority first. Don't dump every phrase you pulled into a tracker. Decide which products, audiences, or markets you actually need visibility into right now, then filter the language against that.
Make sure it covers the full decision, not just discovery. Most raw language skews toward "best X" phrasing, because that's what shows up most in form fills and Search Console. Go back to the sales transcripts and reviews specifically for comparison and validation language too: "[you] vs [competitor]," "is [brand] worth it for [use case]," "alternatives to [competitor] that also do [thing]." That's usually where deals are actually won or lost, and where most tracking lists have a blind spot.
Keep some branded language and some non-branded, on purpose. Non-branded phrases, pulled mostly from Search Console and form fills, show whether you're found at all. Branded phrases, pulled mostly from reviews and sales calls, show whether AI systems describe you accurately once someone's already heard of you. Track both.
Test each platform separately. ChatGPT, Perplexity, Gemini, and Google AI Overviews can answer the exact same real-world sentence completely differently. Don't average the results into one visibility score.
Start with 30 to 50 prompts. A smaller list built from real language beats a huge list of guessed keywords every time. Expand once the first round shows you where the actual gaps are.
A free prompt to build your own
I turned this whole process into a copy-paste prompt you can run yourself in Claude or ChatGPT: fill in the software you use for each source, paste in the raw exports, and it builds the tagged tracking list for you.