What is Query Fan Out, Learn to Use it to Improve AI Search Visibility?

If you are still relying solely on traditional SEO strategies, you are optimising for a version of the internet that is rapidly fading out.

While SEO isn't dead, the rules of visibility have fundamentally changed. To capture attention in 2026, you must adapt to Generative Engine Optimisation (GEO). Recent data underscores this shift: pages optimised for the AI retrieval process are 161% more likely to be cited in AI Overviews.

At the core of this new ecosystem is a mechanism called Query Fan Out. This guide discusses exactly what Query Fan Out is and provides a blueprint on how to use it to dominate visibility in Google AI Overviews, ChatGPT, Perplexity, and Gemini.

Highlights

• Abandon Linear Keywords: Focus on user intent and topic clusters rather than single keywords.
• Optimise for "Background" Search: Remember that AI Mode runs live queries on Shopping Graphs and Finance data; ensure your schema and data feeds are up-to-date.
• Structure is Key: Use tables, bullet points, and clear headings to help AI parse your data.
• Own the Data: Unique, proprietary data is the best defence against being ignored by AI.

Think in Entities: Connect your main topic to all related attributes and concepts to cover the full "Fan Out" spectrum.

What is Query Fan Out in AI Search?

Traditional SEO gave a list of results to user queries based on keyword matching. Query Fan Out puts an end to this redundant technique. It’s an advanced information retrieval technique used by LLMs and AI search engines. 

When a user asks with a complex prompt, the AI does not just search for those specific words. Instead, it "fans out" the request, breaking down the single query into multiple, distinct sub-queries (or sub-intents) to gather a complete picture.

For instance, you search, "What is the best CRM software for a small real estate business?"

• Traditional Search: Looks for pages containing keywords like "best CRM for real estate," "real estate CRM," and "real estate small business."
• AI Search with Query Fan Out: The AI reasons that to answer this effectively, it must answer several unasked questions. It splits the main query into sub-queries such as:
  • What are the top CRM features for realtors?
  • Price comparison of CRM tools for small teams.
  • Integration capabilities of real estate CRMs with listing sites.
  • User reviews for Salesforce vs. HubSpot for real estate.

The AI executes these searches in parallel, retrieves the data, and synthesises it into a single, cohesive answer (like an AI Overview). If your content answers one of these "fanned out" sub-queries, even if you don't rank #1 for the main keyword, you have a high probability of being cited.

How Query Fan Out Works in Google’s "AI Mode" and Deep Search?

While Query Fan Out is a general concept in LLMs, it is the specific engine behind Google’s AI Mode. According to recent insights from Google’s VP of Product, Robby Stein, AI Mode uses this technique to act as an agent on your behalf.

When you engage AI Mode or Deep Search, the system doesn't just look for an answer; it conducts a live research session.

• Real-Time Data Access: Unlike standard LLMs that rely on training data, AI Mode fans out queries to real-time systems. It taps into the Shopping Graph (which updates 2 billion times per hour) and Google Finance to answer sub-queries about price, stock availability, and market trends instantly.
• Scale of Execution: For complex intents, features like Deep Search can trigger dozens or even hundreds of background queries in seconds.
• For example, if you search "Plan a 3-day trip to Goa for a bachelorette party," AI Mode fans this out into distinct research paths:
  • Restaurants with group seating (checking reservations via Maps data).
  • Live music venues with high safety ratings.
  • Hotels with suites available for specific dates.
  • Weather forecasts for packing tips.

It then aggregates these data points into a single, cohesive itinerary. To be visible here, your content must be accessible to these specific "background" searches.

How Does the Query Fan-Out Mechanism Work?

The Query Fan Out mechanism typically operates in four distinct stages within a Retrieval-Augmented Generation (RAG) framework.

1. Intent Decomposition and Reasoning

You ask a question, and the LLM starts operating as a reasoning engine. It analyses the semantic intent behind the query with the words or terms used. 

It asks: What does the user actually need to know to be satisfied? It identifies entities (e.g., "CRM," "Real Estate") and attributes (e.g., "Price," "Features") and generates a "concept map" of necessary information.

2. Parallel Sub-Query Execution

This is the "Fan Out" phase. The system generates 5, 10, or even 20 specific search queries based on the breakdown of the query. These are sent simultaneously to the search index or knowledge graph. 

This is why AI search is "agentive". It acts as a research assistant performing multiple searches in milliseconds.

3. Cross-Source Verification and Retrieval

The AI retrieves passages, not just whole pages, that answer these specific sub-queries. It prioritises sources with high EEAT (Experience, Expertise, Authoritativeness, and Trustworthiness) to minimise hallucinations. It looks for consensus across multiple high-authority domains.

4. Synthesis and Citation

Finally, the LLM aggregates the retrieved text chunks. It writes a unique answer for your query, citing the sources that provided the specific data points for each sub-query. Your goal in GEO is to be the source for at least one of these data points.

What Are the Strategic Benefits and Impacts of Query Fan Out?

Shifting your strategy to accommodate Query Fan Out offers distinct competitive advantages that traditional SEO cannot match.

Bypassing the "Top 10" Barrier

The most significant impact of Query Fan Out is that you don’t need to be on the first page of SERPs to get cited. About 68% of citations in AI Overviews come from pages that do not rank in the top 10 for the main query.

Benefit: You no longer need to compete with the giants (like Wikipedia or Amazon) for the main keyword. If you have the best specific answer for a fanned-out sub-query (e.g., specific durability data), the AI must cite you to complete its answer.

Significantly Increased Citation Odds

Optimising for breadth and sub-topics pays off. Pages that rank for fanned-out queries are 161% more likely to be cited in an AI Overview.

Benefit: By covering the "long tail" of a topic exhaustively, you triple your chances of appearing in the coveted zero-position, driving high-intent traffic that has already been qualified by the AI.

Authority Through "Topic Ownership"

Fan Out queries are volatile; they change based on user context. Chasing every single sub-query is impossible. However, sites that demonstrate Topical Authority, covering a subject so thoroughly that they answer most potential fan-outs, win consistently.

Benefit: More resilient brand presence that dominates the Knowledge Graph, ensuring you are the "go-to" entity for the AI.

How Can You Identify Potential Fan Out Opportunities?

You cannot see the exact sub-queries an AI generates, but you can reverse-engineer them. To optimise for Query Fan Out, you must anticipate the AI's reasoning path.

1. Map the Entity-Attribute Matrix

Stop thinking in keywords and start thinking in Entities. An entity is a distinct concept (person, place, thing, concept). AI understands the world through the relationships between entities and their attributes.

• Entity: "iPhone 16"
• Attributes: Battery life, Camera specs, Price, Release date, and Colour options.

For every core topic you cover, list every possible attribute a user might care about. The AI will likely fan out queries for each of these attributes.

2. Leverage "People Also Ask" and Auto-Complete

Google's "People Also Ask" boxes are primitive versions of Query Fan Out. They literally show you the related questions Google associates with a topic. If a People Also Ask question exists, you can bet the AI will fan out a sub-query for it.

3. Use AI to Simulate Fan Out

Use the AI against itself. Input your target keyword into ChatGPT or Gemini and ask: "Act as a search engine logic bot. Break down the query '[Your Keyword]' into 10 distinct sub-queries you would need to run to provide a comprehensive, expert answer. Focus on user intent and specific data points." The output will be your content roadmap.

How toStructure Content for Maximum AI Readability?

Generative Engine Optimisation is heavily dependent on formatting. Even if you have the best information, if the AI cannot read it easily, it will ignore it. You need to structure your content so it is crawlable by search engines, along with being readable by humans. 

Use the "Inverted Pyramid" for Sections

For every sub-topic (which corresponds to a sub-query), answer the question directly in the first sentence. Follow with supporting data, and then context. This style allows the AI to easily "grab" the answer snippet.

Implement Explicit Headings and Lists

Avoid vague and generic headings written just to add keywords. Use descriptive and entity-rich headings, that too, in question format.

• Poor Heading: "Battery Information iPhone 16"
• Good Heading: "How Long Does the iPhone 16 Battery Last?"

Use bullet points and tables for data. AI models are trained to extract structured data from tables highly efficiently. If you are comparing products, a comparison table is virtually guaranteed to be parsed during a Fan Out process.

Adopt "Answer-First" HTML Structure

Ensure your HTML hierarchy (H1, H2, H3) is logical. Wrap distinct answers in their own sections. The cleaner your code and structure, the easier it is for the AI's retrieval bots to identify your content as a relevant "passage" for a sub-query.

How to Measure Your Brand’s Success in Query Fan Out?

Traditional rank tracking is becoming less reliable. Ranking #1 for a keyword doesn't matter if the AI Overview answers the query without showing your link. You need new metrics.

Tracking Brand Mentions and Share of Voice

Monitor how often your brand or content is cited in AI responses. Use AI visibility tools to monitor your brand’s performance. Analyse the frequency with which an LLM mentions your brand when asked about your industry.

Qualitative Analysis of AI Overviews

Regularly perform manual searches for your high-value keywords. Check the AI Overview.

• Are you cited?
• Which part of your content was used?
• Who are your competitors in the "fanned out" citations?

If you see a competitor cited for "pricing" and you are not, it means their pricing page is better structured for that specific sub-query. 

Visble’s AI visibility tool gives you sentence-wise analysis of every piece of content you track. This not only helps you monitor performance, but also gives you a direction to strategise and improve your brand’s visibility.

Is Your Content Strategy Ready for the Agentive Future?

Query Fan Out is not just a technical tweak. It is a fundamental shift in how human knowledge is retrieved. It moves us away from "searching" and towards "asking."

To succeed, you must stop creating content for search engines and start creating content for reasoning engines (don’t forget it’s a human reading your content, keep the tone and language accordingly). 

If you want your brand to get cited by AI, cover topics with exhaustive depth, structure data for easy extraction, and answer the unasked questions that users imply with their searches.

Excellence in GEO belongs to those who can anticipate the Fan Out. Building comprehensive, entity-rich, and answer-focused content will ensure that no matter how wide the AI casts its net, your brand is the one it catches.