When someone types a question into Google AI Mode, ChatGPT search, or Perplexity, they are not really running one search. The system is running many. It expands the single query into a network of related sub-queries, retrieves information across all of them, and only then composes the response the user reads.
This process is called query fan-out, and it is the mechanism that quietly decides whether your content gets surfaced and cited in AI search results. If you have been focused on ranking for a single keyword, this is the part that explains why some content keeps getting cited while other content disappears from AI responses entirely.
In this post, we will look at what query fan-out is, why entities are the structural unit it relies on, and what this means for your content. The aim is to give you the conceptual picture and the practical implications. The full operational system — the BRIDGE framework, entity blueprints, citation tracking, the live brand audit masterclass — is in the AI Search & LLMs course.
What query fan-out actually is
A concrete example. Someone asks an AI assistant: “What’s the best project management tool for remote teams?”
A traditional search engine would match that query against indexed documents and rank them. An AI system does something more elaborate. It interprets the query, identifies the entities and concepts inside it, and generates a network of sub-queries:
- What features matter for remote teams?
- How do major project management tools compare on integration with Slack and Zoom?
- What is the pricing of leading project management platforms?
- What are common complaints about project management tools from remote teams?
- Which tools handle async communication well?
- Which integrate with GitHub?
The system retrieves information across these sub-queries, evaluates which sources contribute distinct, citable answers, and synthesises a single response. Most of these sub-queries were never typed by the user. They were generated on the fly to research the topic comprehensively.
This is query fan-out, and it is documented in Google’s patents. The Thematic Search patent describes how a single query can result in multiple sub-queries based on themes, and the query variants patent outlines how trained generative models create those variants in real time.
The critical thing to understand: the user sees one answer. The system ran a network of searches behind it. And your content competes across that entire network, not just the original query.
Why entities are the structural unit
Fan-out is not random. It is entity-driven.
When an AI system breaks down “best project management tool for remote teams” into sub-queries, it does so by identifying the entities and attributes embedded in the question. Project management tool is a product entity. Remote teams is a context entity. Best signals a comparative intent that triggers sub-queries about features, pricing, alternatives, and reviews.
Each entity in the original query becomes an anchor for the fan-out. The system asks, “What do I know about this entity? What attributes are relevant? What related entities should I research?” The sub-queries are generated by traversing the entity graph the system has built from the web.
This means your content competes for retrieval across the full set of sub-queries the system generates around your space. If your content covers a project management tool but only mentions integrations in passing, you will not be retrieved for the integration sub-query. If your content does not name competitors, you will not be retrieved for the comparison sub-query. If your content has no pricing details, you will not be retrieved for the pricing sub-query.
Entity clarity is what makes you retrievable. Entity coverage is what makes you retrievable across the fan-out.
What this changes about content strategy
A keyword-focused content programme produces narrow content that targets a head term and ignores the surrounding entity space. In a traditional ranking context, that content can still win on individual queries through ranking strength.
In a fan-out-driven context, the same narrow content gets retrieved less often, surfaced less prominently, and cited less reliably than content built around comprehensive entity coverage. The pages that win are the ones that systematically cover the entities and attributes the system asks about — even when the user did not type those words.
A few specific shifts follow from this.
Cover the entity space, not just the keyword. For each topic you care about, map the related entities and attributes a thorough AI system would research. If you write about CRM software, your content should naturally cover the related entities — pricing models, integration partners, common use cases, comparison alternatives, target audiences. Not because users typed those words, but because the system will fan out into them.
Cover sub-questions explicitly. A strong content piece anticipates the implicit follow-ups: how does it compare, what does it cost, who is it for, what are the gotchas, what are the alternatives. These are exactly the sub-queries fan-out generates. The FAQ format works particularly well here because each Q&A pair is essentially a pre-answered sub-query.
Name entities precisely. Do not rely on pronouns or shortened references where entity recognition matters. “Our platform integrates with Slack” is weaker than “Acme Project Manager integrates with Slack, Zoom, and Google Workspace.” The first version assumes the system knows what “our platform” refers to. The second version is unambiguous and citable.
Structure for chunked retrieval. AI systems retrieve at the chunk level — usually a paragraph or a logical section. Self-contained sections with clear headings and explicit entity references are more retrievable than long prose where the entity context is established once at the top and lost downstream.
What makes content citable in fan-out responses
There is a difference between being retrieved and being cited. AI systems retrieve broadly, then make decisions about which sources actually contribute distinct information to the synthesised response. Several patterns separate citable content from content that gets used as context but never named.
Specific, verifiable facts. Pricing in dollars. Integration counts. Feature lists. Date ranges. AI systems can cross-reference these against the Knowledge Graph and other sources. Vague claims like “industry-leading” or “best-in-class” are not citable because they cannot be verified.
Clear entity attribution. Content that names the entity in the answer is easier to cite than content that talks around it. A paragraph that opens with “Acme Project Manager offers three pricing tiers…” is structurally citable. A paragraph that opens with “We offer three pricing tiers…” requires the system to infer who “we” is before citing.
One complete thought per paragraph. Long paragraphs that mix multiple facts are harder to retrieve as clean chunks. Short, focused paragraphs that each answer one specific question are what fan-out responses are built from.
Connection to recognised entities. Content that mentions established entities (well-known companies, recognised standards, named methodologies, cited experts) signals to the system that you are operating in a connected entity space rather than a disconnected silo. This is the co-citation effect: appearing alongside authoritative entities transfers credibility.
Building entity coverage and co-citation patterns systematically is what the BRIDGE framework is designed for. The course walks through the inventory, relationships, implementation, distribution, growth, and evaluation phases that turn this into operational practice.
The practical implication: think in entity networks, not keyword lists
Most content strategies stop at topic clusters — a pillar page surrounded by supporting articles, all built around keywords. This works for traditional search ranking but it does not match how LLMs think. LLMs think in entities — people, products, concepts, organisations — and the relationships between them.
To win in AI search, you need to transform clusters into entity networks. Take a topic like cybersecurity for small businesses. Within this topic, you need to identify the product entities (password managers, endpoint protection tools), the person entities (IT consultants, security researchers), the organisation entities (vendors, certification bodies), and the concept entities (zero trust, multi-factor authentication, phishing prevention).
Then you need to create explicit relationships using properties like is part of, is related to, knows about, certified by. This transforms a flat content structure into a rich, interconnected network that fan-out can traverse.
This is not just an editorial change. It is a structural change in how you plan, produce, and connect content. The pages that get cited in AI search are the ones whose structure mirrors how AI systems think.
How to start applying this
A few things you can do this week without needing the full course infrastructure.
Audit a single key page against fan-out expectations. Pick one of your most important pages and write down ten sub-queries you would expect an AI system to fan out into around the topic. Then check whether your page addresses each one. The gaps you find are content gaps that are costing you AI search visibility.
Run entity extraction on the same page. Use Google’s Natural Language API to see which entities your content actually surfaces. If the entities you intend to be central are not appearing with high salience, that is fixable with editing.
Check your competitor’s entity coverage. Run the same analysis on a competitor’s equivalent page. Where they cover entities you do not, you have a clear content opportunity. Where you cover entities they do not, you have a differentiation opportunity to lean into.
Add specific, verifiable facts. Replace one vague claim with a specific, citable fact. Then do it again next week. Over time, your content becomes structurally more citable.
These are not the full system. They are starting moves. The full system — the entity blueprint, the schema implementation, the cross-platform distribution, the citation monitoring — is what compounds over months into durable AI search authority.
Continue your learning (MLforSEO)
This post covered what query fan-out is, why entities anchor the sub-queries that get generated, and how to start building content that gets retrieved across the network of searches AI systems run on your behalf. The full system — including the BRIDGE framework for entity development, the schema markup patterns for multi-entity relationships, the cross-platform distribution playbook, the LLM citation tracking framework, and the live brand audit masterclass — is in the AI Search & LLMs: Entity SEO and Knowledge Graph Strategies for Brands course on MLforSEO.
Enrolling also gets you into the dedicated course channel inside the MLforSEO Slack community, where Beatrice Gamba and Lazarina Stoy answer course-specific questions and discuss ongoing implementation projects with course-takers. That is the best way to get personalised support as you work through the material.
Beatrice Gamba
The future of search and content discovery will be dialogical, personalized and agent-mediated. Digital leaders need to start integrating these concepts in their strategies to be ready for what’s coming.
- Beatrice Gamba
- Beatrice Gamba
- Beatrice Gamba
- Beatrice Gamba
