What are Query Fanouts

When you type a question into an AI model like ChatGPT or Perplexity, the AI doesn't just process your exact question. Behind the scenes, it breaks your prompt down into multiple related sub-queries: searching across different angles, intents, and phrasing, before generating its final answer.

These sub-queries are called query fanouts.

A simple example

Say someone asks:

The AI may internally generate fanout queries like:

Each fanout query represents a different search path the AI explores to build its answer. The more of those paths lead to your brand or content, the more likely you are to be mentioned in the final response.

Why do query fanouts matter?

AI models don't just retrieve one page, they explore a network of related queries. This means your AI visibility isn't determined by a single keyword or page, but by how broadly your content covers the topics an AI might explore when answering questions in your space.

• A year is often added to fanout queries (e.g. 2026), which means AI models frequently look for recent sources. Make sure your pages have strong freshness signals: update dates, recent stats, current year in your titles and headings.

• A location is often added to fanouts (e.g. Netherlands). If your business serves specific regions, make sure those locations appear in your page titles, meta descriptions, and headings.

• Fanouts can reveal different intents than your original prompt. If the AI fanout explores a topic your current page doesn't cover, it may be worth creating a dedicated page for that specific query.