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What Are Grounding Queries? The Hidden AI Searches | GEOly | GEO/AEO Platform for DTC Brands
What Are Grounding Queries? The Hidden Searches That Decide Whether AI Cites You
Summary
A grounding query is the machine-generated search an AI runs for itself before answering — the hidden retrieval layer that decides which pages get cited in AI Overviews, ChatGPT, Gemini, and Perplexity. Here is how they work and how to win them.
2026/07/05
16 min read
Updated 2026/07/23
Quick answer: A grounding query is a search that an AI system generates and runs for itself — behind the scenes, before it writes an answer — to fetch fresh, verifiable facts from the live web instead of relying on frozen training data. You never see these searches, but they decide which pages get read, trusted, and cited in AI Overviews, Google AI Mode, Gemini, ChatGPT, Copilot, and Perplexity. In the AI era, ranking for the query a human types matters far less than surfacing for the query the machine generates.
The one-sentence definition
When you ask a large language model a question, one of the first things it does is decide: can I answer this from memory, or do I need to check the web? If it needs to check, it silently rewrites your question into one or more precise, fact-shaped searches, runs them against a live index (Google for Gemini and AI Mode, Bing for Copilot), reads the results, and grounds its answer in what it found. Those machine-generated searches are grounding queries.
They are the difference between an AI that confidently hallucinates and one that says "according to [your page]…". Grounding is the retrieval layer that sits underneath every AI-visibility metric you care about — mention rate, citation rate, share of voice. Content that never surfaces for a grounding query has a hard ceiling of zero on all of them.
The three-layer query stack: typed query → fan-out → grounding
The single biggest source of confusion is collapsing three distinct things into one "search." They are separate layers, and you optimize for each differently.
Figure 1 — How one prompt becomes many machine searches: fan-out for breadth, grounding for depth.
Layer 1 — The query you typed. What the human enters: "Which engagement ring has the best diamond quality for the price?" Conversational, messy, full of intent.
Layer 2 — Fan-out queries. Semrush defines query fan-out as "an AI search system process that splits a user query into multiple sub-queries, collects information for each sub-query, then merges relevant information into a single response." The AI decomposes your one prompt into a breadth of parallel sub-searches to build a rich picture: "best value engagement ring brands,""lab-grown vs natural diamond quality,""engagement ring buying guide 2026,""Tiffany vs Blue Nile price." Google's Head of Search frames AI Mode as "bringing a whole new level of intelligence to search" — in one documented example, to answer a single vacation-planning prompt. Fan-out is about : casting a wide net across many source types (official pages, reviews, forums, expert posts).
Layer 3 — Grounding queries. Where fan-out is wide, grounding is deep and narrow. A grounding query isn't looking for the ten best articles on a topic; it's hunting for the single most precise, most current, most citable data point to verify one specific claim before the model commits to it in writing.
The practical takeaway: fan-out brings the audience; grounding makes the AI trust the answer. You need a two-track strategy, not one.
How grounding actually works (under the hood)
Grounding is an application of Retrieval-Augmented Generation (RAG): the model's fluent-but-frozen language ability is tethered to an external, current source of truth. Google's own Gemini API documents the pipeline explicitly, and it generalizes to every grounded AI system.
Figure 2 — The grounding decision (stable vs. volatile) and the retrieval pipeline.
The model reads your prompt and decides whether a search would improve the answer. This is a routing decision, not a reflex — stable facts skip it, volatile facts trigger it (see the next section).
If grounding is needed, the model generates one or more search queries automatically — reformulating your natural-language question into terse, retrieval-optimized strings.
It executes those searches, reads the returned passages, and synthesizes them into a single answer.
It returns the answer plus grounding metadata that names its work.
In the Gemini API, that metadata is inspectable, and it is the clearest window into how the black box actually behaves:
Field
What it exposes
webSearchQueries
The exact grounding queries the model ran — the reformulated searches, not your prompt
groundingChunks
The web sources (URIs + titles) it retrieved and pulled from
groundingSupports
Which sentences in the answer map to which sources — passage-level attribution
searchEntryPoint
The rendered "Search Suggestions" chips shown to users
Two things follow from this that most SEOs miss:
The unit of retrieval is a passage, not a page.groundingSupports ties individual claims to individual chunks. A page can be grounded for one sentence and ignored for the rest. You optimize passages, not documents.
Grounding has a price, so the machine is selective. Google bills Grounding with Google Search at roughly $35 per 1,000 grounded queries (and, on Gemini 3, per each search the model chooses to run). Because grounding costs money and latency, models only ground when the payoff is real — which is exactly why which queries get grounded is predictable and strategic.
Grounding vs. fan-out, side by side
Figure 3 — Two retrieval layers, two different ways to win.
Dimension
Grounding query
Fan-out query
Purpose
Verify one specific claim
Map the whole information landscape
Breadth
Narrow — 1–2 authoritative sources
Wide — many parallel sub-queries
Best-fit content
Primary research, statistics, specs, prices
Comparisons, thought leadership, FAQs
Preferred sources
Structured, institutional, "of record"
Diverse — expert, social, review, official
Citation behavior
Often uncited but shapes answer confidence
More often visibly cited
How to win
Be the citable source of a fact
Be present everywhere the topic is discussed
Which queries actually get grounded? (The most useful insight in the field)
Not every query triggers grounding — and knowing which ones do is where the strategy lives. The sharpest framing here comes from Mark Williams-Cook: AI systems ground queries whose answers are volatile, and skip queries whose answers are stable.
"What do red blood cells do?" → No grounding. The answer is settled knowledge; the model's token predictions are highly consistent. To change how AI answers this, someone would have to retrain the model.
"What happened in the news today?" / "best AI visibility tool 2026" / "is X in stock" → Grounding. Answers shift over time, the model's internal confidence is low/variable, so it reaches for the live web.
This variability is even measurable. ChatGPT exposes an internal search_prob signal; a score around 0.65+ indicates the model is likely to run a web search. Google's Gemini API lets you inspect grounding directly. Community ML models estimate grounding likelihood too.
Why this matters so much:
Grounded queries can be influenced in days. Publish or update the right fact-shaped content, get it crawled, and you can move the AI's answer almost immediately — no model retraining required.
Non-grounded ("solved knowledge") queries are near-immovable in the short term. Chasing them with content is wasted effort.
This lets you triage a keyword list. Of, say, 500 related keywords, 200+ may be stable-knowledge queries not worth near-term investment. Concentrate your GEO budget on the grounded, convertible subset.
Why grounding queries are the foundation of GEO
Traditional SEO optimizes for what humans type. Generative Engine Optimization (GEO) optimizes for what machines search. That is the whole shift in one line.
Figure 4 — Retrieval is the master gate: without it, every AI-visibility metric is zero.
Because the user never sees the grounding query, the entire competition happens inside a zero-click black box — observable only from the outputs unless a platform hands you the data. And the stakes are counterintuitive:
Retrieval is the gatekeeper. Every downstream metric — mention rate, citation rate, product-card inclusion, AI share of voice — sits below retrieval. If you don't surface for the grounding query, none of them can be non-zero.
AI citations don't follow classic rankings. Roughly 67% of AI citations do not rank in the top 10 for the corresponding query. Pages have accumulated 1,000+ AI grounding citations while remaining near-invisible in blue-link search. The old scoreboard doesn't measure the new game.
The source mix is different, too. Community sources punch above their weight — Reddit alone accounts for roughly one in five off-site AI citations, and its share has been climbing. Grounding rewards being genuinely present where a topic is discussed, not just owning a polished landing page.
Case study: grounding queries in the wild (Tiffany in the engagement-ring category)
Everything above is abstract until you watch a real AI engine do it. Below is a live GEOly report for a demo workspace tracking Tiffany & Co. across the jewelry / engagement-ring category — the exact grounding and fan-out queries the AI ran, pulled straight out of the black box. (Screenshots show GEOly's real product interface, in the original English.)
1. The scale of the hidden search layer
Figure 5 — GEOly's AI Search Keywords view: the whole grounding layer for one brand's category.
Over the tracked window, AI issued 7,412 searches across 287 topics, which GEOly aggregates into 470 keyword roots. Tiffany — one of the most famous jewelry brands on earth — surfaces in only 66 of those 470 roots. There are 74 early-signal gaps worth attention, and 11% of all AI searches target Reddit. None of this appears in Google Search Console; it exists only in the grounding layer. The keyword map shows who the AI names on each root — for "engagement ring" (728×) it's Brilliant Earth, Blue Nile, James Allen, and Brilliance; Tiffany is nowhere on it.
2. The grounding queries themselves
Figure 6 — Drill into one keyword root and the black box opens: "AI's actual search phrases" are the grounding queries.
Drill into the "engagement ring" root and you see the machine's real behavior. AI ran 728 searches here, triggered by 262 distinct user prompts, and Tiffany appears in just 6% of them. GEOly surfaces "AI's actual search phrases" — literally the grounding queries the model generated:
"most secure emerald cut engagement ring settings"
"solitaire vs halo engagement ring value appearance"
"best durable oval engagement ring settings long term reviews"
"blue sapphire vs white sapphire engagement ring durability appearance over time"
"vintage style lab grown engagement ring settings"
Notice how unlike a keyword these are: long, comparative, attribute-driven, ultra-specific. No keyword tool would ever hand you "blue sapphire vs white sapphire … durability appearance over time." And notice who wins the citations — third-party pages, not the brand: usajewels.com (×37), elle.com (×31), rings.com (×28), naturaldiamonds.com (×27).
3. Fan-out in action — and the volatility signal made visible
Figure 7 — One prompt, five grounding queries: GEOly's AI query fan-out, every query tagged "Time-sensitive."
Take one real user prompt — "Which engagement rings for women have the best diamond quality for the price" — and GEOly shows the AI query fan-out: the model issued 5 distinct grounding queries to answer it, including:
"best value engagement ring diamond quality price brands"
"best value engagement rings diamond quality ideal cut lab grown vs natural 2026"
"…for the price lab grown vs natural engagement rings retailers 2026"
"…2026 lab grown vs natural engagement ring buying guide"
Every single one is tagged Comparison · Pricing · Time-sensitive — the exact volatility profile that forces grounding (recall: volatile answers get grounded, stable ones don't). And every one names rivals — Blue Nile, Brilliant Earth, Rare Carat, Bliss Diamond, Grown Brilliance — while Tiffany is absent.
What the case proves: the grounding layer is real, measurable, and unforgiving. A category-defining brand can be invisible in 94% of the searches that matter simply because its content doesn't answer the fact-shaped, comparison-driven, time-sensitive queries the AI actually runs. Every screenshot above is a live GEOly report — and the gap it exposes is exactly the opportunity you can go fix.
How to actually *see* grounding queries
You can't optimize what you can't observe. Four windows exist today, in rough order of directness:
Bing Webmaster Tools → AI Performance report. Rolled out in public preview February 2026 and expanded in June 2026 with Intents, Topics, Citation Share, and a Compare view. This is the first mainstream console to expose grounding-query-level exposure and cited URLs for Copilot/Bing-powered answers.
The Gemini API groundingMetadata. If you build against Gemini, webSearchQueries literally returns the searches the model ran — the ground truth for how your prompts get grounded.
Prompt-level monitoring across engines. Because each engine reformulates differently, the durable method is to probe your target questions at scale and inspect what each engine cites. GEOly, for example, tracks prompt-level outcomes across seven engines — ChatGPT, Gemini, Perplexity, Copilot, Grok, Google AI Mode, and AI Overviews — and reverse-engineers the grounding and fan-out queries behind the answers.
search_prob / grounding-likelihood signals to classify which of your keywords are grounded vs. solved before you spend a dollar of content budget.
How to win grounding queries: an eight-point playbook
Grounding rewards content that is precise, atomic, fresh, and structured for extraction. Do these:
Fill fact-shaped gaps. Grounding queries hunt data points: prices, specs, comparisons, dates, percentages, "how many," "as of when." If the citable fact about your category doesn't exist on your site in clean form, someone else's does.
Publish primary research. Original surveys, proprietary statistics, and regularly-updated benchmarks are the most undervalued GEO asset. They make you the terminal source of a fact — the thing a grounding query is built to find.
Write in atomic passages ("chunks"). Because the retrieval unit is the passage (groundingSupports), lead each section with a direct, self-contained answer, then support it. Every heading should be able to stand alone as an answer to a sub-question — Semrush calls this "writing for NLP."
Use tables and lists. Structured data is trivially machine-extractable and disproportionately grounded. Comparison tables, spec sheets, and numbered steps are grounding magnets.
Build topic clusters for the fan-out layer. A pillar page for the broad topic, linked to granular cluster pages for each sub-query, builds the topical authority AI systems prioritize — and covers the breadth of sub-queries a single prompt fans out into.
Implement structured markup (JSON-LD). Schema (Product, Offer, FAQ, etc.) makes claims unambiguous to machines and meaningfully lifts extraction and snippet inclusion.
Keep it fresh and dated. Volatile-answer queries are the only ones that get grounded — so freshness is the signal. Put visible "last updated" dates on data. Stale statistics silently lose the citation.
Designate one canonical URL per fact, and work the third-party layer. Don't fragment your best statistic across five posts — concentrate authority on a single, linkable, updatable source of record. Then reinforce presence off-domain: reviews, analyst mentions, and authentic community participation (Reddit, niche forums) win the fan-out layer.
And the table stakes: stay crawlable. If your robots.txt blocks the AI crawlers, or your facts are locked behind JavaScript the retriever can't render, you've lost the grounding query before it started.
Case study — Stripe. Semrush highlights Stripe as a model of two-track optimization: solution pages tailored to different business stages and use cases (fan-out breadth), detailed subsections answering specific sub-topics with direct explanations (grounding depth), content distributed across blog, guides, and support center, all cleanly structured. The result: Stripe out-visibilities competitors across Google AI Mode, ChatGPT/SearchGPT, Perplexity, and Gemini.
A practical five-step diagnostic
Map intent to retrieval type. For each target query, classify: grounded or solved? Grounding-heavy or fan-out-heavy?
Audit your grounding candidacy. Do you own citable, precise, current facts for your category — or only prose?
Audit your fan-out coverage. Are you present across the breadth of source types the AI samples, or just your own site?
Find the gaps. Where does the AI answer your category question without you? Those are your retrieval holes.
Build the two-track content strategy. Deep, canonical, fact-of-record pages for grounding; distributed, granular, independently-useful content for fan-out.
Common mistakes
Optimizing the typed keyword, not the grounding query. You're writing for a search the machine will never run in that form — treating grounding queries like 2019 head-term keywords.
Chasing solved-knowledge queries. No amount of content moves an answer the model already "knows." Triage first.
Watching only one engine. Each engine reformulates differently; Bing's report is a proxy, not the whole picture.
One giant pillar page. Grounding wants atomic, extractable passages, not a 5,000-word wall. Fan-out wants breadth, not a single monolith.
Expecting on-site content to win queries that resolve to third-party sources. If Reddit and review sites define your category to the AI and you're absent there, you lose the fan-out layer entirely.
Assuming top-10 rankings = AI visibility. Two-thirds of AI citations come from outside the top 10. Different game, different scoreboard.
FAQ
Are grounding queries the same as the keywords I track in Search Console?
No. Your tracked keywords are what humans type. Grounding queries are the reformulated, fact-shaped searches the AI generates after reading your prompt. They frequently target an intent your keyword research never surfaced.
What's the difference between a grounding query and query fan-out?
Fan-out is the expansion step — one prompt splitting into many parallel sub-searches for breadth. Grounding queries are the searches themselves, especially the deep, narrow ones that verify a specific claim. Fan-out is wide; grounding is deep. You need both — see our full guide to query fan-out.
How can I see ChatGPT's grounding queries?
There's no direct feed. Use Bing Webmaster Tools' AI Performance report as a proxy for Copilot/Bing-grounded answers, and track prompt-level outcomes (what gets cited) across engines to reverse-engineer the queries.
Can I control which grounding queries an AI generates?
Not directly — the model generates them. But you can control whether you're the best answer when it runs one (via entity naming, consistent terminology, and category coverage), and you can predict which of your queries get grounded (volatile vs. stable) to focus effort.
How fast can I influence a grounded answer?
For genuinely grounded (volatile) queries, days — publish/update the right fact, get it crawled, and the AI can pick it up on its next retrieval. Solved-knowledge queries are effectively immovable without model retraining.
Where GEOly fits
Grounding queries are invisible by design — which is exactly the problem GEOly is built to solve. The Tiffany case above isn't a mock-up; it's a live GEOly report. GEOly probes AI engines at scale across seven engines (ChatGPT, Gemini, Perplexity, Copilot, Grok, Google AI Mode, AI Overviews), reverse-engineers the grounding and fan-out queries behind your category's answers — down to the exact search phrases and who gets cited — shows you which queries are grounded vs. solved, and tracks whether your content is actually being retrieved and cited. Instead of guessing at the black box, you get the query-level map (the 7,412 searches, the 470 roots, the 6%-presence gaps), the query fan-out analysis behind each prompt, a full GEO audit of your gaps, and the citation sources AI pulls from. Already trusted by 12,000+ brands monitoring their presence in AI search.
Compiled July 2026 from an analysis of the top-ranking results for "grounding queries," Semrush's query fan-out guide, and Google's official Gemini grounding documentation.
