Query Fan-Out AnalyzerEnter a prompt and this tool extracts and analyzes the search queries (query fan-out) that Gemini actually ran internally. See exactly which searches the AI executed to gather information, based on real execution data rather than generated guesses.
When you ask an AI assistant a question, it does not simply search for your exact query. Instead, it "fans out" your question into multiple related searches to gather comprehensive information. This tool extracts the internal search queries that Gemini actually used, so you see only the search terms the AI genuinely referenced.
Type the question or prompt you want to analyze. Complex queries with multiple aspects work best.
Gemini processes your prompt and runs multiple search queries while generating its answer. This tool extracts those queries from the actual execution log.
See every individual search query that was used to gather information for the answer.
Knowing what the AI actually searched, rather than how it reasoned, is the single most important insight for LLMO and AI search optimization. By basing your strategy on the queries the LLM actually executed, not speculation or generated guesses, you can design content that gets cited and recommended by AI.
A single prompt triggers roughly 3 to 8 different searches. Your content needs to be discoverable across all of these variations.
Discover the exact phrases AI uses to find information, and feed these queries into your content strategy.
See which sources are cited for each fan-out query, and identify opportunities where competitors are mentioned but you are not.
Identify sub-topics within a main query that you are not yet covering. Each fan-out query represents a potential content opportunity.
Try our other free tools for AI search optimization
Query fan-out refers to the behavior of generative AI such as ChatGPT and Gemini running multiple search queries for a single question to gather information. This tool extracts and analyzes the actual search queries Gemini executed while generating its answer, so the analysis is based on measured data rather than generated guesses.
Research shows that generative AI uses roughly 3 to 8 fan-out queries per prompt, and complex queries can generate up to 28 different searches. This varies with the complexity and specificity of your original question.
Use fan-out queries to understand the terms AI uses to find information about your topic, and make sure your content covers those variations. If a fan-out query returns competitor content, that is a content gap you should address.
When Gemini answers entirely from its pretrained knowledge without running a web search, no fan-out queries occur. This tool extracts searches that were actually executed, so if no search happened there is nothing to display. Try prompts that need real-time information, such as recent news or price comparisons.
