What is query rewriting in AI search?

Query rewriting is the process an AI search system uses to turn what a person typed into the search queries it sends to an index. You'll also see it called query reformulation.

The user writes a prompt. The engine decides whether to search at all. If it does, it writes its own queries, retrieves pages, and generates an answer from them. The rewritten queries, not the original prompt, decide which pages enter the model's context.

In the four-stage view of AI visibility (parametric, access, retrieval, generation), query rewriting sits at the start of retrieval. It's controlled by the engine, not the publisher.

As of October 2026, the vendors describe it this way:

• ChatGPT search. OpenAI's help centre says ChatGPT search "typically rewrites your query into one or more targeted queries" that it sends to search providers. It also says that if memory is enabled, ChatGPT may use saved memories when rewriting a search query. Our walkthrough of OpenAI's guide to searching the web with ChatGPT covers the rest of that page.
• Google AI Overviews and AI Mode. Google says it may use a "query fan-out" technique, issuing multiple related searches across subtopics to build one response.
• Gemini API. Google's grounding documentation says the model automatically generates one or more search queries and executes them.

Retrieval itself is discretionary. ChatGPT decides per prompt whether to search, and AI Overviews often don't trigger. So one prompt can produce several queries, a different set the next time, or none at all.

Most AI visibility tools, including prompt trackers, send a fixed list of prompts and record the answers. That's useful. It tells you what an engine said in response to those words. It doesn't tell you which queries the engine ran, so it can't tell you which searches your pages actually won or lost. The short answer to whether AI searches for the exact question a user types is no.

Three practical consequences follow:

1. Treat prompt results as a sample. The free checker on this site runs up to three buyer questions as a snapshot. Read it as answers to those questions, not a map of every query run.
2. Measure logged out, with memory off. Memory can change the rewrite, so logged-in tests aren't reproducible.
3. Look at real grounding queries where they exist. Bing Webmaster Tools' AI Performance report shows the phrases the AI used when retrieving content it cited, for Copilot and Bing AI summaries. It has no API as of August 2026. Our playbook on comparing grounding queries with your prompt set shows how to use it, and this page covers whether Bing Webmaster Tools is worth setting up.

• "Optimize for the prompts your customers type." The engine searches for its own rewritten queries. Matching your copy to a guessed prompt targets an input the engine may never use.
• "Cover every long-tail variant." Google's AI optimization guide says you don't have to worry about missing long-tail keywords or every variation of how someone might search. Studies reviewed in the evidence base point to topical relevance as a reproducible factor, not lexical matching.
• "Phrasing doesn't matter at all." It does, to the user's result. Different prompts can produce different rewrites and different answers. See how question phrasing affects brand mentions.
• "Rewriting can be influenced from a page." No vendor documents any page-level signal that changes how it rewrites a query. What you control is whether your pages can be fetched and indexed when a query does match them.

• Prompt: the text a user types, before rewriting. See prompt.
• Query fan-out: Google's term for issuing several related searches across subtopics for one request.
• Grounding query: a query an AI system actually ran to retrieve content it cited. Bing exposes these.
• Retrieval: the stage where pages enter the model's context, driven by the rewritten queries.
• Long-tail keywords: narrow search phrases. See long-tail keywords.