When interacting with an LLM, users enter a single prompt and receive a single answer. However, this answer is rarely built from one question alone.
Behind the scenes, LLMs like ChatGPT, Gemini or Claude often expand a prompt into multiple internal queries to retrieve information, compare viewpoints and structure their response. This mechanism is called query fan-out.
Query Fan-out provides a clearer lens to understand:
how presence is distributed across multiple sub-questions
how influence and visibility emerge across LLM answers
how indirect traffic and brand exposure can be generated
1. Definition: What is Query Fan-out?
Term | What It Means | Key Attributes |
Query Fan-out | The process by which an LLM expands a single user prompt into multiple internal queries to generate its answer. | - Happens internally in the model |
In practice, the model does not answer one question.
It answers several related questions, then synthesizes them into a single response.
Example
User prompt:
“What is the best project management software for small teams?”
To answer this, the model may internally explore queries such as:
What tools are commonly used by small teams?Which features matter most?How do popular solutions compare?What are their main strengths and limitations?
Each internal query contributes a piece of information used to assemble the final answer.
2. Query Fan-out, Intent and Answer Construction
Query fan-out is closely linked to how the model interprets what the user is trying to achieve.
Rather than treating a prompt as a single request, the LLM:
breaks it down into several angles
explores each angle through an internal query
combines these perspectives into one answer
In practice, fan-out queries often cover:
explanation or definition
comparison between options
examples or recommendations
This is how LLMs translate intent into smaller, answerable questions and assemble a complete response.
3. What We Observe in Practice (based on internal tests)
The following observations are based on internal experiments conducted by our data science team and reflect current behaviours.
A single prompt can generate from zero up to 15–20 fan-out queries, depending on the model and its version. Some prompts may trigger little to no fan-out, especially when the question is very narrow or requires limited exploration.
Fan-out is not fixed over time: the same prompt can produce different internal queries at different moments. This helps explain why LLM answers are not always perfectly stable, even when the prompt does not change
Being ranked on Google for fan-out queries does not directly increase the probability of appearing as a Source or a Link in LLM answers
Some models expose, via their APIs, the URLs associated with fan-out queries
Being present in these URLs increases the likelihood of later appearing in the final answer (as a Source or a Link)
The relative position compared to competitors appears more important than absolute ordering
These signals should be read as interpretation cues, not deterministic rules.
To Recap
Query fan-out describes how a single prompt expands into multiple internal queries
These queries explore different angles of the same question
Fan-out varies by engine, version and over time
It plays a key role in how sources, links and brands appear in LLM answers
To go further