What is a grounding query, and why does it matter?
Updated 2026-07-22
What a grounding query is
A grounding query is the search-style question an AI engine runs behind the scenes to gather sources before it writes an answer. When you ask a chatbot something, the assistant does not answer purely from what it memorized during training. It first turns your prompt into one or more grounding queries, sends those queries to a search index, retrieves passages from the results, and only then composes a response using those retrieved passages as grounding material. The term borrows directly from retrieval-augmented generation, where grounding means anchoring a generated answer to external evidence rather than letting the model invent facts unsupported by any source. Every grounded answer you see in ChatGPT, Perplexity, Copilot, or Gemini exists because a grounding query ran first and returned something citable. If nothing was retrieved for a given query, the engine either declines to answer with specifics or falls back to unsupported general knowledge, which is precisely why understanding grounding queries matters for anyone trying to influence what these engines say about a brand or topic.
How engines turn a prompt into grounding queries
An assistant rarely searches for your exact words. Instead it rewrites your prompt into a set of narrower, more search-friendly queries designed to surface different angles of the answer, a process commonly called query fan-out. Ask what the best CRM for a small agency is, and the engine might quietly generate separate grounding queries for CRM comparisons, agency-specific reviews, pricing pages, and recent roundup articles, running each one against its search index. This mirrors how an experienced researcher would break a broad question into several targeted searches rather than typing the question verbatim into a search box. The fan-out step happens inside the model's reasoning before any text reaches you, so the queries themselves are invisible unless you have access to the engine's own diagnostic tooling. What matters for content strategy is the outcome: the sources retrieved across all of those fanned-out queries become the pool the engine draws from when it drafts its answer, and a source that only matches your original prompt but none of the fan-out variants may never get retrieved at all.
Why one prompt spawns several queries
A single user question is usually underspecified relative to what a good answer needs. Fan-out exists because one query rarely retrieves everything required to write a complete, well-supported response. A prompt asking about a product category might need queries covering definitions, comparisons, pricing, recent news, and user opinions, and no single search string reliably returns strong sources for all of those at once. Splitting the prompt into several grounding queries lets the engine cast a wider net, then merge and re-rank whatever comes back before deciding which passages actually make it into the answer. This also gives the engine redundancy: if one grounding query returns weak or thin results, the answer can still lean on stronger results from a different query in the same fan-out set. For a brand, this means visibility is not about winning one head-term search but about being retrievable across the several related queries an engine is likely to generate for any given prompt in your category.
How to find the grounding queries behind your category
Grounding queries are generated inside the engine, so you cannot request the exact list directly, but Bing Webmaster Tools' AI Performance report exposes a close proxy. Because Copilot and other Microsoft-integrated assistants route much of their retrieval through Bing, the AI Performance report surfaces the queries that led an AI surface to fetch a page from your site, which is effectively a sampled view into real grounding queries firing against your content. Reviewing that report regularly shows which phrasings and question forms are pulling your pages into AI answers versus which related queries are retrieving competitors instead. Pair that signal with direct prompting: run your category's core questions through ChatGPT, Perplexity, and Copilot yourself, and note the phrasing and sub-questions each engine seems to be answering along the way. Between the Bing report and manual prompting, a clear pattern of recurring grounding queries usually emerges within a few sessions of research, giving you a concrete list to work from instead of guessing.
What to do with your grounding query list
Once you have a list of recurring grounding queries for your category, treat each one as an individual page target rather than folding them all into a single broad article. A page built to directly and self-sufficiently answer one grounding query, in a form the retriever can lift as a clean passage, has a far better chance of being pulled into an answer than a page that only touches the topic in passing. Structure each page so the first sentence of the relevant section answers the query plainly, without requiring the reader to scroll through unrelated context first. Revisit the Bing AI Performance report and your manual prompting checks periodically, since the fan-out queries an engine generates for a given prompt can shift as the engine's models and retrieval systems change. The practical discipline is the same one search marketers have used for keyword targeting for years, just redirected at the queries an AI system runs on your behalf instead of the queries a human types into a search box.
Frequently asked questions
Can I see the exact grounding queries an AI engine runs for my prompt?
Not directly from most consumer assistants, since fan-out happens inside the model before you see a response. Bing Webmaster Tools' AI Performance report is the closest public proxy, showing which queries led an AI surface to retrieve pages from your site.
Is a grounding query the same as a keyword?
Not exactly. A keyword describes what a person types into a search box, while a grounding query is generated by the AI engine itself to gather source material. They often overlap in wording, but a grounding query is machine-generated and can differ from anything a human would naturally search.