AI Search Query Generator

What is the AI Search Query Generator?

The AI Search Query Generator is a free tool that writes 10 short search queries with commercial intent for a topic: the strings people type while comparing, buying or choosing. Each is labelled with the buying intent behind it. Paste them into a rank tracker or keyword tool to plan the pages that should answer them.

How to use the AI Search Query Generator

1. Enter the subject people search for in Topic or keyword, such as "programmatic SEO". Use the category, not your brand name.
2. Press Generate queries.
3. Read the 10 Generated Queries and the intent label under each. The first time, sign up free to reveal them.
4. Copy one query, or press Copy All to paste the list into a rank tracker or keyword tool.

What is a commercial intent query?

A search typed by someone choosing between options rather than learning about them. Someone typing "what is technical SEO" is not choosing a vendor. Someone typing "best technical SEO tool for a small agency" is most of the way to choosing one.

These are the queries where being absent costs most. A comparison query answered without you in it is a decision made without you in it.

How is a search query different from an AI prompt?

Length and shape. A search query is a short, keyword-shaped string. It goes into a rank tracker, may have measurable search volume, and maps onto one page you can point at it. A conversational prompt does none of those things.

For the full sentences buyers type into ChatGPT or Perplexity, use the AI Prompt Generator instead. Same topic, two surfaces, two ways of measuring.

What should you do with the generated queries?

Three passes. Check each against a keyword tool for volume. Search each one and note whether an AI Overview or answer panel appears and what it cites. Then map each surviving query to the one page that should answer it.

A query with no page gets a page. A query whose page does not rank, which the keyword rank checker shows, gets that page rewritten so the answer is in the first paragraph rather than the fourth.

Is ChatGPT an AI search engine?

ChatGPT is an AI assistant that can also search the web. When it searches, it reads pages, writes an answer and shows the sources it used, which is how an AI search engine behaves. When it does not, it answers from what the model learned in training, with no live sources behind the answer.

ChatGPT decides for itself when a question needs a search, and you can also ask it to search. For anyone tracking visibility, that split matters: a buying question that triggers a search can be influenced by the pages that rank and get cited, while an answer from training reflects what was written about you before the model was trained.

That is also why the same need gives different results in different places. A buyer using ChatGPT with search, a buyer using it without, and a buyer reading a Google AI Overview can each see a different shortlist.

For your own checks, note whether an answer showed sources. An answer with sources is one you can work on through the pages it cites; one without tells you about the model's training, which changes only when the model is updated.

Which AI is best for searching?

There is no single best AI for searching; it depends on what you need back. Perplexity searches the web for every answer and cites each source, so it suits research where you want to check claims. Google's AI Overviews and AI Mode sit on Google's own index. ChatGPT, Gemini and Microsoft Copilot combine a general assistant with web search when the question calls for it.

The practical test is the same as for a prompt: ask the same question in two or three of them and compare which sources each cites. For a business, the engine that matters is the one your buyers use, and the queries this tool writes are a reasonable set to run in each.

For tracking, consistency matters more than strength. Pick the engines once, run the same queries in each on a schedule and keep the list stable, so changes in the results reflect the engines and not your method. If you only have time for one, start where your buyers start, and ask a few customers which tool they used while choosing you.

Which engine cites you is worth knowing too. The same query can produce a citation to your page in one engine and to a competitor's in another, because each searches and ranks differently.

Is this the same as an Azure AI Search query?

No. Azure AI Search is Microsoft's search service for indexing and querying your own data inside an application. Its queries are written in its own syntax, with filters in OData expressions and full text in simple or Lucene query syntax, and they run against an index you built.

This tool writes the opposite kind of query: what people type into Google or an AI assistant while shopping. It does not produce Azure query syntax, filters or index definitions. If you arrived here looking for those, Microsoft's Azure AI Search documentation covers query construction in detail.

Azure AI Search does offer semantic ranking and vector search over your own content, which is where the "AI" in its name comes from, but it is still searching your index, not the web.

The two meet in one place. If you run site search on Azure AI Search or a similar service, the queries visitors type into it are a good source of real buyer language, worth comparing with the list this tool produces. Where the two lists overlap, you have a query that is both typed on your own site and likely to be searched elsewhere, which makes it a strong candidate for a dedicated page.

How do AI search engines turn a question into queries?

Many AI search engines split a question into several shorter searches, run them, and write one answer from the results. Google calls this query fan-out in its descriptions of AI Mode. A question like "what CRM should a five-person agency use" can become searches for agency CRMs, CRM pricing for small teams and CRM comparisons.

Those sub-searches look a lot like the short commercial queries this tool writes. Ranking for them is one way into the answer, because the pages they return are the pages the engine reads. To see how one question might break apart, try the AI query fan-out tool.

This is also why a page that answers one narrow query well can appear in answers to broader questions it never mentions: the engine found it through a sub-search, not through the question the buyer typed. Short queries are also easier to measure, since each can be checked in a rank tracker and a long conversational question cannot.

In practice, group the generated queries by the broader question they would serve, check which of them you already rank for, and fill the gaps with pages that answer each query in the first paragraph.