How AI Interprets Your Prompt: The Router and Query Fan-Out

Not every question sends the engine to the web. The first thing that happens after you hit enter is a fast, quiet decision: can this be answered from the model’s memory, or does it need live information? Google’s grounding documentation describes exactly this step, the model analyzes the prompt and decides whether a web search would improve the answer, and only then runs one.

What tips a question toward search is fairly intuitive. Anything that is time-sensitive, specific, or tied to named entities, recent events, current prices, product comparisons, or anything that changes, tends to trigger retrieval. Anthropic describes Claude this way: it searches when the question needs current or verifiable information. Broad, timeless, or definitional questions are more likely to be answered from memory, with no sources attached.

This is why the same engine behaves so differently from one question to the next. Ask it something evergreen and you get a confident, source-free answer from its weights. Ask it something current and it goes and reads. The classifier in front of both paths is what routes you, and it re-decides for every prompt.

When the engine does decide to search, it rarely searches for what you typed. Instead it expands your prompt into a whole set of related searches and runs them at once. Google calls this query fan-out, and describes AI Mode breaking your question into subtopics and issuing a multitude of queries on your behalf, then pulling the results back together into a single answer.

The mechanism isn’t a guess. A Google patent application, ‘Search with Stateful Chat’ (US20240289407A1), describes a method it calls prompted expansion: the model is explicitly instructed to generate a diverse set of synthetic queries from your original prompt. One question in can become many retrieval queries out, and in deeper research modes, far more.

Fan-out isn’t just more of the same query; the patent describes generating queries with intent diversity, deliberately covering different angles of your need. A few recurring types matter most for how your brand gets represented:

The comparative and implicit types are where this gets strategic. Your brand is being compared, evaluated, and positioned inside queries no one on your team ever searched for, and the answer is assembled from whoever happened to have published content matching those hidden sub-queries.

Those sub-queries aren’t matched against your page by exact keywords. AI search works on meaning. Under the hood, both the query and your content are turned into embeddings, numeric representations of meaning, so the engine can find content that says the same thing even when it uses completely different words. (That machinery is the subject of a later part in this series; here, the takeaway is what it does to your writing.)

Because matching is semantic, repeating a target phrase does little. What helps is actually answering the question the sub-query represents, in plain, direct language. A page that clearly explains a concept will match many phrasings of it; a page stuffed with one keyword but thin on substance matches none of them well.

The shift is from targeting a keyword to covering a question space. If the engine turns one prompt into a dozen searches, then being ‘the best page for the head term’ matters less than having a credible answer to each of the sub-questions that head term implies.

This also explains a pattern people find surprising: pages that don’t rank on the first page of classic results still get pulled into AI answers, because they happened to be the best match for one specific sub-query. Fan-out rewards depth of coverage across a topic, not just a single ranking.