AI query intent types: discovery vs best-of vs alternatives vs pricing
A citation on a pricing question is worth more than one on a definitional question. Here's the intent taxonomy that tells you which rows in your report to work first.
AI buyer queries fall into a small number of intent types, and the type determines how much a citation is worth. Discovery questions ask what exists in a category; being named here builds awareness but the buyer is early. Best-of questions ask for the top options; a citation is a direct recommendation slot. Alternatives questions ask what to use instead of a named tool; appearing here is high-intent and often competitive. Pricing questions ask what things cost; the buyer is close to a decision, so a citation is the most valuable of all. On a reverse AI search report you should sort rows by intent before anything else — an alternatives or pricing citation cited by ChatGPT, Gemini or Grok is worth defending hard, while a discovery citation is a lower-stakes asset to keep warm.
Why classify AI queries by intent at all?
A reverse AI search report can run to dozens or hundreds of rows. Without intent, every row looks equally important and you end up working the easy ones instead of the valuable ones. Intent is the lens that turns a flat list into a priority order: it tells you how close the buyer is to a decision when they ask, which is the single best proxy for how much a citation is worth.
This is the same logic SEO has always used to separate informational from transactional keywords — we’re just applying it to the questions buyers ask AI assistants instead of search engines.
What are the four intent types?
Most buyer queries to ChatGPT, Gemini and Grok map to one of four intents. The examples below are illustrative phrasings, not measured queries from any index.
| Intent type | Example phrasing | What a citation here means | Priority |
|---|---|---|---|
| Discovery | queries like “what tools exist for X” or “how do teams handle Y” | You are on the long list — early awareness, buyer not yet choosing. | Low–medium: keep warm, don't over-invest. |
| Best-of | queries like “best X for small teams” or “top X in 2026” | You are in the recommended set — a direct shortlist slot. | High: defend hard, these convert. |
| Alternatives | queries like “alternatives to Z” or “what to use instead of Z” | A model treats you as a credible substitute for a named rival. | High: competitive and late-funnel. |
| Pricing | queries like “how much does X cost” or “X pricing vs Y” | The buyer is near a decision — closest-to-purchase citation. | Highest: protect or win first. |
How does intent change what I do with a row?
For best-of and pricing rows you already win, the job is defence: keep the underlying pages accurate and well-structured so you hold the slot. For alternatives rows, check who you appear beside — that is your competitor query overlap — and confirm the model is recommending you, not cautioning against you. For discovery rows, do enough to stay present without diverting effort from the high-intent questions.
How do I use intent to attack gaps?
Run a competitor’s domain through the same reverse check. Where they are cited on a pricing or alternatives query and you are not, you’ve found a high-value AI keyword gap. Sort those gaps by intent and work the closest-to-purchase ones first — the full workflow is in from AI keyword gap to content brief.
One caution: intent is not the same as sentiment
A high-intent citation is only good if the model speaks well of you. Being named on an alternatives query as the option to avoid is the opposite of a win. Always pair intent with sentiment before you celebrate a row.
See your own queries by intent
Run the free Domain Check to pull your queries across ChatGPT, Gemini and Grok with intent labelled, then sort the list and start from the top. For a deeper definition of the underlying terms, see the glossary.