Optimization

Query Fan-out

Expansion of a single user query into multiple sub-queries that an AI system runs and aggregates before returning one answer.

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What is Query Fan-out?

Query fan-out is the expansion of one prompt into multiple search queries. One query can return several documents, and several queries can return the same source. Document, passage, and citation counts therefore do not reveal the number of search queries. In Qwairy, the available query data reflects searches exposed by the provider and captured for the response.

How Qwairy Makes This Actionable

Qwairy shows search queries captured from provider responses when available. Use these observed queries to explore related topics. Coverage and counting can vary by provider and view; an empty query list does not prove that no web search occurred.

Frequently Asked Questions

Understanding query fan-out reveals optimization opportunities most brands miss. When a user asks 'best CRM software', AI platforms may fan out into dozens of sub-queries: 'CRM for startups', 'affordable CRM pricing', 'CRM integrations', etc. If you only optimize for the core query, you miss citations from these fan-out queries. By targeting the full fan-out pattern, you maximize your chances of being retrieved and cited. Observed related queries can suggest additional topics to address, without guaranteeing retrieval or citation.

Inspect the search queries captured for the responses to your tracked prompts, where the provider exposes them. Use those queries to identify related topics. Do not infer query counts from citations: ten cited sources can come from one search, and several searches can return the same source.

Search behavior varies with the provider, model, prompt, and enabled tools. The queries exposed to Qwairy can also vary. Compare observed searches within a consistent scope instead of assuming a fixed ranking between platforms. Missing query data alone does not establish that search was disabled.

Retrieval Augmented Generation(RAG)

Technical

AI architecture that retrieves relevant information from external sources in real-time before generating responses.

Prompt

Technical

Natural-language question or instruction a user submits to an LLM; in GEO, tracked prompts reveal how AI platforms talk about a brand.

Perplexity

AI Platforms

Conversational search engine that combines LLM and real-time web search with source citations.

Source Citation

Metrics & Analytics

Reference to a URL or website as a source of information in an AI response.

ChatGPT

AI Platforms

Conversational assistant developed by OpenAI, based on GPT models.

Provider

Company that builds and operates a language model or AI platform, such as OpenAI, Anthropic, or Google.

Query Patterns

Recurring structures, themes, and characteristics in how users formulate questions to AI systems.

← Back to GlossaryLast updated Jun 9, 2026