Query fan-out in Google AI mode

What is query fan-out? Google’s new AI search technique

Google is changing how search works, and most people haven’t noticed yet.

When you type a question into Google’s AI Mode, you think you’re asking one thing. But behind the scenes, Google’s system takes your single query and explodes it into dozens of related questions. It searches for all of them simultaneously, then weaves the answers together into what looks like a simple response.

This technique is called query fan-out. It’s not just a minor update to how Google works. It’s a fundamental shift that changes everything about how content gets discovered and displayed.

Think about it this way: you search for “best electric SUV,” but Google secretly also searches for “electric SUV safety ratings,” “EV charging infrastructure,” “Tesla Model Y vs competitors,” and twenty other related questions you didn’t ask. Then it builds an answer that addresses all these angles.

For content creators and SEO professionals, this changes the game completely. You’re no longer competing to rank for one keyword. You’re competing to be relevant across an entire constellation of related queries that users never see.

How query fan-out works

The technical foundation

Google’s AI doesn’t just match your words to web pages anymore. It analyzes your question using advanced language processing to figure out what you really want to know. Then it generates what Google calls “synthetic queries” that explore different facets of your original question.

The system runs on dense retrieval technology, which means everything gets converted into mathematical representations called vector embeddings. Your query becomes a vector. Every piece of content on the web becomes a vector. Google finds matches by calculating similarity between these vectors, not by counting keyword matches.

When you ask about electric SUVs, Google’s system might determine that you’re making a purchasing decision. So it generates queries about pricing, safety, reliability, and comparisons. It searches for all of these simultaneously, pulling relevant passages from different sources to build a comprehensive answer.

This happens in milliseconds. You see one response, but Google performed dozens of searches to create it.

From single query to multiple searches

Let’s walk through a real example. You search for “how to improve website speed.”

Google’s query fan-out might generate these related searches:

• “website performance optimization techniques”
• “Core Web Vitals improvement strategies”
• “image compression for faster loading”
• “CDN setup for better performance”
• “JavaScript optimization best practices”
• “mobile page speed optimization”

Each of these synthetic queries retrieves different content. Google then uses reasoning chains to connect information from multiple sources into a coherent answer. Your content might get cited not because it ranked #1 for “website speed,” but because it had the best explanation of image compression techniques.

This explains why you sometimes see content from page 3 of traditional search results appearing in AI Overviews. It wasn’t ranking poorly for your main keyword. It was ranking well for one of the hidden synthetic queries.

Google’s patent evidence

”Systems and methods for prompt-based query generation”

Google filed patent application US20240289407A1 that reveals exactly how query fan-out works. The document describes a system that uses large language models to generate multiple alternate queries from your original search ¹.

The process starts with what Google calls “prompted expansion.” An AI model receives structured instructions to create queries that emphasize different types of intent. It might generate comparative queries (“A vs B”), exploratory queries (“how does X work”), or decision-making queries (“best X for Y situation”).

The patent shows that Google doesn’t just randomly generate related searches. The system follows specific patterns:

• Intent diversity (comparing, exploring, deciding)
• Lexical variation (synonyms and paraphrasing)
• Entity-based reformulations (specific brands, features, topics)

The patent also describes filtering mechanisms that ensure the selected queries span multiple categories and return diverse content types. This prevents the system from getting stuck in one semantic zone.

”Thematic search” patent

In December 2024, Google filed another patent US12158907B1 for something called “Thematic Search”. This system organizes search results into themes and provides AI-generated summaries for each theme ².

The patent describes how a single query can result in multiple sub-queries based on “sub-themes.” For example, searching for “moving to Denver” might generate themes like “neighborhoods,” “cost of living,” “things to do,” and “pros and cons.”

This patent closely mirrors what we see in AI Mode’s query fan-out technique. It shows Google’s been working on these concepts for years, not just since AI became popular.

The thematic search system also reveals how Google combines content from multiple sources under each theme. Your content might appear not because it comprehensively covers the main topic, but because it provides the best information for one specific sub-theme.

Types of synthetic queries in fan-out

Related queries

Google generates queries that are semantically or categorically adjacent to your original search. These often come from entity relationships in Google’s Knowledge Graph.

If you search for “best laptops,” related queries might include “top notebooks,” “portable computers,” or “laptop alternatives.” The system recognizes these terms as related concepts and searches for content using different vocabulary that means essentially the same thing.

Related queries help Google cast a wider net to find relevant information that might use different terminology than your original search.

Implicit queries

These are the queries Google thinks you meant but didn’t explicitly ask. The system infers these based on context clues, ambiguity in your phrasing, and patterns from similar user behavior.

When you search for “iPhone battery life,” Google might implicitly search for “how to extend iPhone battery,” “iPhone battery replacement cost,” and “iPhone battery health check.” You didn’t ask these specific questions, but Google recognizes they’re likely what you want to know.

Implicit queries are where query fan-out really shines. It anticipates the follow-up questions you’ll probably ask and answers them preemptively.

Comparative and reformulation queries

Google automatically generates queries that compare options when it detects decision-making intent. If you search for “project management software,” it might create comparative queries like “Asana vs Trello,” “best project management tools for small teams,” or “project management software pricing comparison.”

Reformulation queries maintain your core intent but use different phrasing. “How to lose weight” might become “weight loss strategies,” “effective diet plans,” or “healthy weight reduction methods.”

These reformulations help Google find content that discusses the same topic using different vocabulary or from different angles.

The impact on search results

From ranking to reasoning

Traditional search ranked web pages based on relevance signals like keywords, links, and user behavior. Query fan-out introduces reasoning into the mix. Google’s AI doesn’t just find relevant pages. It reasons about how different pieces of information connect to answer your question comprehensively.

This means your content gets evaluated not just on its own merits, but on how well it fits into a larger reasoning chain. Google might select a passage from your article not because your entire page is the most relevant, but because one specific section provides the best explanation for step 3 in a 7-step reasoning process.

The system also uses what Google calls “pairwise ranking prompting.” Instead of scoring content in isolation, it asks an AI model to compare two passages and determine which better answers the user’s question. This happens across many passage pairs to build a ranked list.

You’re no longer competing against other pages for a keyword. You’re competing passage-by-passage in head-to-head AI evaluations.

Passage-level retrieval

Google now indexes and retrieves content at the passage level, not just the page level. This means individual paragraphs or sections from your content can be selected and cited independently.

A single search result might combine passages from five different websites, each contributing the best information for different aspects of the query. Your 3,000-word comprehensive guide might contribute just one paragraph to the final answer, while a competitor’s brief FAQ section provides another piece.

This changes how you should think about content structure. Every passage needs to be semantically complete and valuable on its own, not just as part of a larger article.

What this means for content strategy

Topic clusters vs. single keywords

The days of creating one page to rank for one keyword are over. Query fan-out means you need to think in topic clusters that cover all the related questions someone might have about your subject.

Instead of creating separate pages for “email marketing,” “email automation,” and “email segmentation,” you might create a comprehensive email marketing hub that thoroughly covers all these related concepts. Or you might create tightly connected pages that link to each other and collectively demonstrate expertise across the entire topic cluster.

The key is comprehensive coverage. Google’s query fan-out will find gaps in your content coverage when it searches for related concepts and doesn’t find your content addressing them.

Semantic richness and entity relationships

Your content needs to be rich with entities that Google can recognize and connect to its Knowledge Graph. This means using specific names, places, products, and concepts rather than vague references.

Instead of writing “this popular social media platform,” write “Instagram.” Instead of “the search engine giant,” write “Google.” Clear entity references help Google understand what your content is about and connect it to related queries.

You also need to make relationships between concepts explicit. Don’t just mention that “content marketing helps with SEO.” Explain how content marketing improves search rankings, what specific SEO benefits it provides, and how the two strategies work together.

Optimizing for query fan-out

Content structure best practices

Structure your content so AI systems can easily parse and extract relevant passages. Use clear headings that directly answer questions. Write concise paragraphs that make complete points without requiring surrounding context.

Each section should be able to stand alone as an answer to a specific question. If Google extracts just one paragraph from your article, will it make sense and provide value to the reader?

Use formatting that makes information scannable: bullet points for lists, numbered steps for processes, and clear subheadings that preview what each section covers. But don’t overuse lists. Most information flows better in narrative form with strategic use of formatting for emphasis.

Include FAQ sections that directly address common questions about your topic. These often get pulled into AI-generated answers because they’re already in question-and-answer format.

Answering the questions behind the question

Think beyond the obvious questions about your topic. What would someone naturally want to know next? What concerns or objections might they have? What related problems are they trying to solve?

If you’re writing about home security systems, don’t just cover features and pricing. Address installation complexity, monthly monitoring costs, false alarm rates, and integration with smart home devices. These related concerns often become synthetic queries in fan-out.

Examine People Also Ask sections to identify these secondary questions. But don’t stop there. Think through the complete user journey and what information they’ll need at each stage.

Measuring success in a fan-out world

Beyond traditional rankings

Traditional keyword rankings become less meaningful when Google might cite your content for dozens of related queries you never targeted. You might rank #15 for your main keyword but get cited prominently in AI answers because you provide the best information for a specific sub-topic.

Focus on topic-level visibility instead of keyword-level rankings. Are you being cited across multiple related searches in your topic area? Are you showing up in AI Overviews and AI Mode responses for various angles of your subject matter?

Track brand mentions and citations even when they don’t include links. In a query fan-out world, being referenced without receiving a click might still provide significant value through brand awareness and authority building.

New analytics approaches

Traditional web analytics miss most of the value you provide in query fan-out scenarios. Users get their answers without clicking through to your site, but your content still influences their decisions.

Monitor AI search platforms directly. Search for various related terms in your topic area and see where your content appears in AI-generated responses. Track this over time to measure your topic-level authority.

Use tools that monitor conversational AI platforms like ChatGPT, Perplexity, and Google’s AI features. These give you insight into how often your content gets cited across different AI systems.

Consider setting up alerts for your brand name and key concepts to track mentions across AI platforms, even when they don’t drive direct traffic.

The future of search with query fan-out

Query fan-out represents just the beginning of how AI will transform search. As these systems become more sophisticated, they’ll generate even more synthetic queries and reason across increasingly complex information relationships.

We’re moving toward a world where search engines don’t just find information. They understand it, synthesize it, and present it in exactly the format users need. This means content creators must shift from thinking about keywords to thinking about knowledge contribution.

The winners in this new environment will be those who create genuinely comprehensive, well-structured content that serves as reliable source material for AI systems. Surface-level content optimized for specific keywords won’t survive when AI can reason about quality and depth.

This doesn’t mean SEO is dead. It means SEO is evolving into something more sophisticated: helping AI systems understand and utilize your content effectively. The fundamentals of creating valuable, well-structured content remain important. But the tactics for optimization are changing completely.

Query fan-out is Google’s way of bridging the gap between what users ask and what they actually want to know. For content creators, this means your job is no longer just answering the question that was asked. Your job is anticipating and answering all the questions that should have been asked.