You're optimizing the wrong layer. Meet the Fan-Out queries
Everyone talks about optimizing for AI prompts. But prompts aren't what LLMs actually execute. We've tracked millions of prompts and analyzed how AI answers get built. What we consistently observe: a single user prompt never runs as-is. The model transforms it first.
Think of it like asking a librarian a complex question
You might phrase it casually, with extra context, half-formed thoughts. The librarian doesn't search your exact words, they extract what you actually need, then run targeted queries against their catalog. Different people asking the same underlying question in different ways get routed to the same sources. LLMs work similarly. Behind every prompt, the model extracts intent and breaks it into shorter, normalized queries—what we call fan-out queries. Those internal queries determine which sources get retrieved, which content enters the candidate pool, and which brands appear in the final answer.
This is good news for marketers
You don't need to optimize for every possible prompt variation. The fan-out layer normalizes surface-level chaos into more stable patterns. What matters is whether your content answers the core questions that fan-out queries target. Stop thinking like a keyword optimizer. Start thinking like an intent satisfier.
Where visibility actually happens
The prompt is just the messy human wrapper. Fan-out queries are where visibility actually happens.