Understanding Query Fan-Out and Visibility in Large Language Models (LLMs) and AI Search: A Strategic Perspective for SEO Professionals
As the landscape of search engine optimization (SEO) continues to evolve with advancements in AI and large language models (LLMs), marketing professionals and SEO strategists face new challenges and opportunities. Recent discussions across platforms such as Reddit’s r/seo, on X (formerly Twitter), LinkedIn, and industry events underscore the importance of understanding how queries are processed within LLMs and how this affects visibility and brand positioning.
This article aims to shed light on the concept of Query Fan-Out, its impact on LLM-based search visibility, and how these insights can inform your SEO strategies.
The Growing Dialogue Around LLM and AI Search Visibility
Amidst a flood of information and opinions, there remains a notable gap in practical understanding of how LLMs generate search outcomes. A recent poll on X, garnering over 280 votes within 24 hours, revealed widespread confusion about the mechanics behind Query Fan-Out and its direct relationship to a brand’s visibility in AI-driven search environments.
This gap highlights a crucial need for data-backed exploration rather than speculative reasoning—especially as industry conversations tend to be influenced by assumptions rather than demonstrable examples.
Differentiating Between Google and LLM Search Visibility
Traditional search engine visibility relies on ranking algorithms designed to surface relevant web pages based on a complex array of signals, including schema, backlinks, and content quality. However, when it comes to LLM-based searches, the criteria shift significantly.
Many professionals have observed that a brand appearing prominently in standard Google search does not necessarily translate to equivalent visibility within LLM outputs. This discrepancy can notably stem from how queries are “fan-ed out” or expanded within the model, fundamentally altering which data points or sources influence the results.
The Concept of Query Fan-Out: A Closer Look
Query Fan-Out refers to the phenomenon where an initial query triggers multiple related searches, each designed to probe different aspects or variations of the original intent. For example, searching “SEO Agency NYC” in Google yields a set of relevant results, but when an LLM like Perplexity or ChatGPT processes this query, it often recreates a series of related searches internally, such as:
These variations—fan-outs—are crucial because the prominence of