Fan-out queries: the key concept
A single question asked to an AI often splits into several sub-queries sent in parallel. Understanding this mechanism changes how you should cover a topic.
When a user asks a generative AI a question, that single question frequently gives rise to several distinct searches, launched in parallel, each covering a different angle. This is what's called the fan-out query mechanism: a query that "fans out" into several sub-queries before the system synthesizes a single answer. It's probably the most useful concept to understand for anyone thinking about content for GEO, because it shifts the question from "what's THE right keyword" to "what are ALL the questions related to this topic." This shift in perspective often requires more upfront organizational effort: mapping out the likely questions around a topic before writing, rather than starting from a single keyword and unspooling content from there.
The principle: one question, several searches
Take a simple example: a user asks "what's the best invoicing software for a small business in France." Rather than running a single search with those exact words, a RAG system may break the question down into several distinct sub-queries: "invoicing software for small businesses in France," "invoicing software comparison for SMBs," "invoicing software compliant with anti-VAT-fraud law," "reviews of invoicing software for small business." Each of these sub-queries is processed separately, produces its own batch of results, before everything is merged to build the final answer. The exact number of sub-queries generated isn't fixed: it depends on how complex the question is perceived to be and on the tool used, ranging from as few as two or three reformulations to a much broader breakdown for a topic perceived as complex.
Why AI systems work this way
This decomposition allows several facets of an intent that's often implicit in a natural-language question to be covered. A single literal query would poorly capture the diversity of real needs behind the question: price, legal compliance, ease of use, other users' reviews. By multiplying search angles, the system increases its chances of retrieving documents that, put together, genuinely cover the topic rather than just one aspect of it. Take another example: a question like "how can I reduce my social contributions as a freelancer" can break down into sub-queries about the most suitable legal status, existing exemption schemes, common mistakes to avoid, or other freelancers' experiences. A site that only addresses one of these angles will only capture a fraction of the fan-out generated by that single question. This is consistent with how a RAG-based LLM builds its answer in multiple steps, as described in the lesson How a RAG-based LLM builds an answer.
What this means for how you cover your content
The direct consequence of this mechanism is that a single article, however well written, has little chance of answering all the sub-queries generated by a complex question. A site that only addresses a topic from one angle — say, only price, without ever covering legal compliance or user reviews — will only be retrieved for a fraction of the fan-out's sub-queries, and so will be mentioned partially, or not at all, in the final synthesis. Conversely, a site that covers a topic from several complementary angles, across multiple well-structured pages or sections, statistically has a better chance of appearing in several of the generated sub-queries, and so of being more strongly represented in the final answer. This doesn't mean everything needs to be crammed into one overloaded article: it's often better to have several solid pages, each focused on a precise angle, rather than a single page that tries to cover everything superficially and loses clarity on every point.
Thinking in "topical coverage" rather than a single keyword
This logic brings GEO closer to a discipline of topical coverage than precise keyword targeting. It's no longer about aiming for an exact query, but about covering the full range of questions a user might implicitly be asking around a topic: price, alternatives, use cases, limitations, reviews, comparisons. This approach ties directly into the logic of the question-and-answer format, covered in the lesson The question-and-answer (FAQ) format and GEO, which is precisely what lets you anticipate several of these potential sub-queries within a single well-structured piece of content.
A mechanism that also explains the weight of brand presence
Fan-out also explains why brands already mentioned in multiple places across the web have a structural advantage: the more a brand is associated with related topics across different sources, the more likely it is to surface in several of the sub-queries generated by a question, even one phrased unexpectedly. This link between a brand's repeated presence across the web and its likelihood of being cited is developed in the lesson Brand mentions: the number-one factor in AI visibility. In other words, fan-out doesn't just play out at the level of a single page, but also at the level of all the signals available about a brand across the web, which directly ties this mechanism to a broader visibility strategy than content production alone.
The essential takeaway: a question asked to an AI is almost never treated as a single query. It breaks down into several parallel searches, and your content needs to cover enough angles of a given topic to appear in several of those sub-queries, rather than betting on a single phrasing. Practically, this means auditing your existing content less by keyword ranking and more by the range of angles it actually addresses on a given topic. Over time, this kind of mapping tends to reveal gaps in a site's content that keyword-based planning alone would never have surfaced.