Query Fan-Out

What is Query Fan-Out?

Query fan-out is how AI search engines like Google AI Mode, ChatGPT, Perplexity, and Mistral split one user question into multiple related sub-queries, retrieve data for each, and combine the results into a single synthesized answer. It is the core mechanic behind how LLMs reason through complex questions.

Instead of returning one direct result like traditional search engines, AI platforms perform multiple searches behind the scenes to understand context, verify facts, and build the best possible answer.

How Query Fan-Out Works

When a user asks an AI assistant a question, the system:

1. Breaks down the prompt into multiple related sub-queries (e.g., definitions, comparisons, reviews)
2. Runs parallel searches across its indexes and data sources
3. Retrieves content from many different URLs and sources simultaneously
4. Synthesizes the findings into a single coherent, cited answer

For example, the question “What’s the best laptop for a high-school student?” might trigger sub-queries about battery life, durability, price ranges, student software compatibility, and recent reviews — all processed in parallel.

Query Fan-Out in Practice

Google popularized the term at Google I/O 2025 when introducing Google AI Mode. As Head of Search Elizabeth Reid explained: “AI Mode isn’t just giving you information — it’s bringing a whole new level of intelligence to search. Under the hood, Search recognizes when a question needs advanced reasoning. It calls on our custom version of Gemini to break the question into different subtopics, and it issues a multitude of queries simultaneously.”

Data from ChatGPT shows that approximately 31% of prompts trigger a web search, with an average of 2 fan-out searches per query. Sectors like Jobs & Career and Software average nearly 3 fan-out searches per prompt.

Why Query Fan-Out Matters for Marketing

Query fan-out changes content strategy fundamentally. Because AI engines pull from multiple sources simultaneously, brands must ensure their content is authoritative enough to be included in the reasoning chain and cited in the final answer.

This means:

• Depth beats keywords — Topic clusters and comprehensive coverage increase the chance of appearing across multiple fan-out branches
• Structure matters — Clear headings, definitions, FAQ blocks, and schema markup help AI systems parse and reuse your content
• Writing for NLP is essential — Short paragraphs, self-contained sections, and explicit definitions make content extractable
• Traditional SEO is still foundational — Fan-out queries often pull from search engine indexes, so strong SEO supports AI visibility

How to Optimize for Query Fan-Out

1. Cover full topics including sub-topics and related questions
2. Use structured data — FAQ schema is especially important for question-based queries
3. Build content clusters — One article is not enough; you need a corpus of content around each topic
4. Write in extractable chunks — Create self-contained sections that can stand alone as AI answer material
5. Track performance — Use GEO analytics tools like Superlines to see which fan-out queries surface your brand and where competitors dominate