What is a grounding query?

You enter a single question into a search engine or AI tool, but the system does not necessarily have to stop at what you typed. It may generate additional queries, search for up-to-date information, compare data from several sources, and only then prepare an answer. This is where the concepts of grounding, grounding queries, and query fan-out come into play. With the development of AI Mode, AI Overviews, and search-enabled models, these concepts are becoming increasingly important from a business perspective as well.

However, the terminology should be clarified from the outset. “Grounding query” is not the name of a new type of keyword or an official ranking factor in Google Search documentation. Google does, however, describe grounding—the process of basing a model’s response on retrieved sources—as well as query fan-out, which involves generating multiple related queries to obtain the information needed to answer a question. From an SEO perspective, the mechanism itself is therefore more important than the terminology: your website may be discovered not only for the question entered by the user, but also through additional searches performed by the AI system.

What is a grounding query?

Put simply, a grounding query can be understood as a search query performed by an AI system in order to find information that the model can use as the basis for its response. It does not have to be identical to the user’s original question. The model may analyze the user’s intent, determine that additional information is required, generate one or more searches, and then use the retrieved materials as the foundation for its answer.

This is how mechanisms such as Grounding with Google Search, used within the Gemini ecosystem, work. The model analyzes the user’s question, may use search, and then incorporates the retrieved information when generating its response. Depending on the product and technical interface, information about the queries performed by the system may also be available.

It is important to remain precise, however: a grounding query is not the equivalent of a traditional keyword, nor is it a separate SEO metric made available to website owners. It is primarily a useful way of describing one element of the process in which AI searches for the information required to generate an answer.

Grounding vs. query fan-out — how do these mechanisms differ?

Grounding and query fan-out are related, but they do not mean the same thing. Grounding refers to basing an answer on retrieved information, while query fan-out describes the process of finding information by generating additional related queries.

In practice, a single user question may therefore lead to several stages of searching. The system analyzes the problem, identifies the information it needs, performs searches covering different parts of the topic, and then uses the retrieved materials to prepare its response.

The key takeaway is simple: one question from a user may trigger more than one search by the system. This mechanism is precisely why analyzing visibility in AI requires a broader perspective than the traditional approach of mapping one keyword to one landing page.

How does a grounding query work in practice?

Suppose a user asks an AI system: “Which heating system should I choose for a 150 m² house if low running costs are my priority?” A traditional SEO approach might focus primarily on a keyword that closely matches this question. A generative system, however, may require substantially more information before it can prepare a useful answer.

Depending on the context, it might search for information relating to:

• types of heating systems for detached houses;
• operating costs of different heating systems;
• how to size a heat pump according to building parameters;
• the energy demand of the house;
• underfloor heating and other heating installations;
• the impact of building insulation on energy demand;
• differences between an installation in a new-build property and the modernization of an existing building.

This does not mean that Google will always generate these exact queries for such a question. This is an example illustrating the mechanism, not a list of actual grounding queries disclosed by Google.

What matters is the principle itself: the system may break a complex problem into smaller parts, collect information from different sources, and then combine that information into a single answer. Your website therefore does not always have to be the best source for the entire topic. It may provide an excellent answer to one specific element of a broader problem.

Why does grounding change the way we think about SEO?

Grounding does not make traditional SEO irrelevant. In Google’s case, the opposite is true. Generative Search features still rely on search infrastructure and systems that help discover and evaluate pages available in the index.

What changes is the way content planning should be approached. Instead of limiting your analysis to the question:

“Which keyword do I want to rank for?”

you should increasingly also ask:

“What information will the user and the search system need in order to solve this entire problem?”

This does not mean abandoning keyword research. Keywords, search intent, website architecture, indexing, and internal linking all remain important. Grounding and query fan-out simply demonstrate why fully understanding the user’s problem may be more useful than mechanically optimizing content for a single combination of words.

How can grounding affect your company’s visibility?

One of the most important business implications is that your brand may appear during a search process even if the user did not begin by entering your brand name—or even an exact phrase corresponding to your page.

If AI breaks a complex problem down into several areas, a source may provide particularly useful information relating to just one part of the answer. In practice, this means that the value of an expert article does not have to be limited to ranking for its primary keyword.

Imagine an online store selling specialist outdoor equipment. A prospective customer does not have to search for a specific product name. They might ask:

“What should I pack for a five-day trek in the Alps in September?”

To prepare an answer, the system may need information about clothing, pack weight, backpacks, footwear, rain protection, or choosing equipment for specific conditions. If your website contains a particularly valuable guide covering one of these topics, there is a potential opportunity for it to be used as a source or for a link to be presented to the user.

There is no guarantee, however, that your page will be cited or displayed. Just as proper SEO optimization does not guarantee a specific ranking position, creating content that is useful to generative systems does not guarantee that it will be used in a specific AI response.

Grounding queries vs. keywords — what is actually changing?

Keywords remain useful, but they describe only part of the search process. In the traditional model, you analyze a keyword, user intent, competition, and the pages appearing in the search results. In an environment that uses query fan-out, an additional layer may appear between the user’s question and the selection of sources: the system itself generates searches required to gather information.

This is therefore not a matter of replacing SEO with “AI SEO.” A more accurate approach is to view these new mechanisms as an extension of the traditional way of analyzing search intent and information architecture.

Does grounding mean you need to write longer articles?

No. Content length in itself does not solve the problem and should not be the objective of optimization. What matters far more is whether the content addresses a genuine user need and provides the information required to make a decision.

If the subject requires an explanation of five different relationships, it makes sense to cover them. If a complete answer can be provided in a few paragraphs, artificially expanding the text adds no value.

For more complex topics, a useful structure may be:

problem → criteria → options → comparison → limitations → decision → next step.

This allows the content to naturally cover several subproblems without keyword stuffing or building the article exclusively around hypothetical AI-generated queries.

How should you prepare content for grounding and query fan-out?

The best starting point is not to look for another “AI hack,” but to understand the user’s problem more thoroughly. In practice, a strong visibility strategy for generative Search still relies on content quality, technical accessibility, logical information architecture, and genuine usefulness.

1. Identify the broader problem, not just the keyword

Consider what decision the user wants to make after reading your content. Simply entering a phrase into a keyword research tool will not always reveal the entire process. For purchase-related topics, the user may simultaneously need comparisons, pricing information, limitations, selection criteria, and risk-related information.

2. Map out natural subproblems

If someone is looking for a way to choose a particular solution, they will probably also need the information required to evaluate it. Create sections that answer genuine questions, but do not present your own topic map as a list of actual fan-out queries generated by Google unless you have such data.

3. Add information that cannot easily be copied

The greatest competitive advantage may come from elements based on your company’s real-world experience: original tests, examples, working methodologies, expert commentary, project data, photographs, product materials, or analysis of specific cases.

Another article that merely summarizes five other articles may be linguistically correct, but it is difficult to regard it as an exceptionally valuable source. The more original knowledge you contribute to the topic, the more useful the content becomes both to users and to systems searching for information.

4. Make sure your content is technically accessible

If you want to build visibility in Google, your content must be accessible to Googlebot and eligible for indexing. The basic requirements of the search engine still apply to content that may appear as supporting links in generative Search features.

There is no additional “GEO certificate” that you need to add to your website in order to automatically appear in an AI-generated response.

5. Build logical internal linking

Not every subproblem needs to be included in one enormous article. If a topic has its own search intent and requires a full explanation, it may deserve a separate piece of content. However, connect it to the main guide in a way that helps the user take the next step.

At this point, you can naturally direct readers, for example, to Greenfields resources on GEO (Generative Engine Optimization) and increasing website visibility in AI.

Should you create a separate page for every possible grounding query?

No. This is one of the easiest ways to turn a valid observation about AI-assisted search into an artificial content strategy.

If a single user problem can be broken down into a dozen supporting questions, that does not automatically mean you should create a dozen separate URLs. An additional page makes sense when it solves a standalone user problem and provides enough value to justify its existence.

A better criterion is:

“Would someone looking for an answer to this problem genuinely benefit from a separate, comprehensive piece of content?”

If yes, a separate page may be justified. If the answer only requires a single paragraph in an existing article, creating an additional URL solely with potential query fan-out in mind usually makes little sense.

Grounding in Google Search vs. Gemini — why should you distinguish between them?

The term “grounding” appears across different Google products, which is exactly why oversimplification is easy. AI Mode and AI Overviews are Google Search features. Grounding with Google Search is also available as a solution used within the Gemini ecosystem and developer tools.

The mechanisms are conceptually related, but you should not assume that every technical detail described for the Gemini API works in exactly the same way in the consumer version of Google Search. Documentation for the specific product should always be your first source of reference.

Grounding and Google-Extended — does robots.txt affect visibility?

In this area, it is important to distinguish Google Search from other uses of Google’s models. Google-Extended is not a crawler responsible for traditional Google Search indexing and is not a search ranking signal.

Google-Extended allows website owners to manage certain ways in which their content may be used by Google’s generative products. It should therefore not be presented as something that can be enabled to improve a page’s ranking in standard Google search results.

What can grounding change in your business strategy?

The biggest change is not about writing articles themselves. It is about how you think about your company’s online presence.

In addition to the traditional question:

“Do we rank highly for keyword X?”

it is worth asking:

“Are we a credible and useful source of information across the areas that contribute to our customer’s decision?”

For a service-based business, this may mean developing content covering costs, selection criteria, the collaboration process, timelines, problems, risks, and differences between service options. For e-commerce businesses, detailed product information, buying guides, comparisons, instructions, and resources that help customers choose the right product for a specific use case may be particularly important.

For local businesses, consistent information about the brand, services, location, and availability remains important. The goal is not to create content exclusively “for AI,” but to build a digital source of information that clearly describes your offering and answers the real problems faced by customers.

How can you measure the business impact of generative search?

Your analysis should not end with whether your brand appears in AI-generated responses. Visibility only becomes useful from a business perspective when it helps you reach the right audience and supports a specific objective—such as a sale, lead generation, booking, sign-up, or contact.

Depending on the reports and platforms available to you, you can analyze factors such as:

• organic visibility and traffic to pages associated with specific user problems;
• referral traffic from AI tools, where it can be identified in analytics;
• conversions and the quality of leads generated by these visits;
• changes in queries and landing pages in Google Search Console;
• citations and mentions in tools monitoring brand visibility across AI systems;
• the impact of content on subsequent stages of the user journey.

However, you should not reduce the entire assessment to the number of citations. A source may be cited frequently while generating few valuable visits or customers. For this reason, SEO and AI Search measurement should be combined with GA4, CRM data, and genuine business objectives.

The most common mistakes when optimizing for grounding queries

The first mistake is treating a grounding query as a new type of keyword that needs to be repeated throughout the content. The system needs useful information, not mechanical exact-match optimization.

The second mistake is mass-producing pages around hypothetical fan-out queries. You do not know every query the system may generate in a specific context, so creating hundreds of similar URLs based solely on such predictions may lead to low-quality content and keyword cannibalization.

The third mistake is ignoring traditional SEO because you believe it has been replaced by GEO or AEO. Technical accessibility, indexing, high-quality content, internal linking, satisfying search intent, and a clear site architecture all remain fundamental.

The fourth mistake is publishing generic content that merely reproduces information already available across many other websites. Original knowledge, experience, examples, and data give you a much better chance of creating content that is genuinely useful to the audience.

Will grounding queries be important for SEO?

The terminology itself may evolve, but the underlying mechanism already has practical significance. Generative systems can perform searches, break more complex questions into subtopics, and use external information when generating responses.

For SEO, this does not mean inventing an entirely new discipline. Rather, it means expanding on what has long formed the foundation of a good strategy: understanding the user, creating useful information, and making that information technically accessible to search engines.

What changes is the potential number of touchpoints. Your brand may be discovered during a more complex search process even if the user did not enter your company name or an exact phrase corresponding to your landing page.

FAQ — frequently asked questions about grounding queries

Grounding queries — summary

Grounding allows an AI model to base its response on information retrieved from external sources rather than relying solely on knowledge previously stored in the model. During this process, the system may perform additional searches, while mechanisms such as query fan-out allow it to break a complex problem down into several related areas.

For your business, this primarily means a change in perspective. It is not worth optimizing content exclusively for a single keyword. A better approach is to build a source of knowledge that reflects the customer’s actual decision-making process.

You do not need artificial content chunking, hundreds of pages targeting hypothetical grounding queries, or a special schema markup for AI Mode. Strong SEO foundations, technical accessibility, clear information about your brand, and content that contributes original knowledge, experience, and genuine value remain far more important.

A company that understands its customers’ problems and can explain them better than its competitors builds a resource that is useful both to people and to systems searching for sources to support their answers.

Sources

1. Google Search Central, AI features and your website / guidance on generative Google Search features, [accessed online: 17 August 2026].
2. Google Search Central, AI features and your website, [accessed online: 17 August 2026].
3. Google AI for Developers, Grounding with Google Search, [accessed online: 17 August 2026].
4. Google Search Help, documentation on how generative search features and AI Mode work, [accessed online: 17 August 2026].
5. Google Crawling Infrastructure, documentation on crawlers and Google-Extended, [accessed online: 17 August 2026].
6. OpenAI, official documentation on OAI-SearchBot and GPTBot, [accessed online: 17 August 2026].

Want to find out whether your website is ready for search in Google and AI systems? Visibility in AI does not begin with another technical trick. The first step is to check whether search engines can correctly discover and understand your content, whether you address your customers’ real problems, and where your competitors provide better information. Explore AI SEO services from Greenfields and see how to approach the topic strategically.