Query Fan-Out Simulator

Free, no signup. Map the research questions around a query, label how they were generated, then check lexical coverage against your content.

Built by Patrick Stox

Separate pages with ---SOURCE---. Start a pasted block with SOURCE: label, or use a bare URL block. URL-only blocks are fetched; pasted content stays in the browser.

Use your own Gemini API key (optional)

The key is sent directly from this browser to Google and is never sent to this site, stored, or logged here. Google may apply project quota or charges. Clear the field when finished.

Requests are processed by our server and are not stored after processing. Generic sub-queries and pasted-source competition run locally. The query may be sent to the selected AI path for optional calibration; URL-only sources are fetched through the protected text endpoint. Pasted source text is not sent for calibration. Anonymous run-level outcome counters may be used for aggregate research; URLs, domains, IPs, and identifiers are never included, and no statistic is released below 100 runs.

Feedback
Report a bug

Found something broken in Query Fan Out? Let us know what happened — this goes straight to a private triage queue, not a public list.

What will be sent 
 No tool inputs, uploads, pasted source, complete results, query parameters, or URL fragments are attached automatically. You can edit or remove the selected passage above. Browser and anti-abuse metadata is processed for spam prevention. 
  
 
  
 
 
  
 
 
  
 
Sample report Deterministic generic example
 
For canonical tags with no page text, the local generator always returns five unique rows:
 
canonical tags · generic · entity-expansion · mixed
What is canonical tags? · generic · definitional · mixed
canonical tags benefits and limitations · generic · entity-expansion · mixed
canonical tags vs alternatives · generic · comparative · mixed
How to choose canonical tags · generic · implicit · mixed
 
Because no source text was supplied, every row says to paste page content rather than inventing a coverage percentage.
 
 
 
How to use it
  1. Enter the main query you want to explore.
  2. Optionally paste a page’s text or HTML to add a lexical coverage check.
  3. Optionally enter your Gemini API key for a direct Google request. Otherwise the tool tries the bounded site model and falls back to the deterministic generic set.
  4. Select Simulate fan-out, keep only relevant sub-queries, then review gaps against the strongest page chunk before editing.
 
 
     
 Local data 
Saved targets, named lists, and recent check summaries remain only in this browser.
 
   
 
     
 
What the simulation shows
  • generic — a visible deterministic template, not provider behavior.
  • calibrated — a model-generated approximation for this run, labeled as Gemini or Workers AI provenance.
  • Type — a rule-based intent label such as comparative, freshness, or definitional.
  • Answer mode — a heuristic guess that the task is memory-, retrieval-, tool-, or mixed-led.
  • Covered / Gap — whether the strongest lexical chunk reaches the 55% query-term coverage threshold.
 
How it works
The fallback expands the entered query with four fixed research shapes, removes duplicates, and classifies each row from visible keywords. With a key, the browser sends a query-only prompt directly to Gemini 3.5 Flash and parses one sub-query per line. If that fails, the bounded Workers AI path is attempted before the generic fallback. For coverage, supplied content is extracted, chunked at roughly 300 tokens with overlap, ranked lexically, and compared with a 55% threshold. Generated rows are approximations; no provider’s private query logs are accessed.
 
Features
  • Always-available deterministic fan-out.
  • Optional direct BYO-key Gemini approximation without routing the key through this site.
  • Multi-page, per-sub-query lexical retrieval competition.
  • Winning page and chunk with visible matching terms and runner-up coverage.
  • Consensus versus corpus-unique claims, numbers, and quotes with explicit originality and support caveats.
 
Limitations
Real answer systems can plan, search, and reformulate in ways this simulator cannot observe. Generic wording can be awkward, classifications are keyword rules, and lexical matching misses synonyms and deeper semantic equivalence. A generated row can also be irrelevant to the page’s intent. Treat the map as an editorial brainstorming aid, not evidence of ranking or inclusion in an AI answer.
 
AI answers may continue to reflect cached or previously retrieved material for days to weeks after a fix. Recheck over time before treating an unchanged answer as proof that the fix failed.
 
Frequently asked questions
What is query fan-out?
Query fan-outQuery fan-out is the technique where an AI search system breaks a single user question into multiple related sub-queries, runs those searches concurrently, and synthesizes the retrieved results into one answer. Google confirms AI Overviews and AI Mode 'may use a query fan-out technique' issuing multiple related searches across subtopics. is the use of multiple related searches or retrieval tasks to help answer a broader prompt. Search providers may use it, but the exact sub-queries for a live answer are generally not exposed.
Are these the queries Google or an AI system actually used?
No. Generic rows are deterministic templates and calibrated rows are a model-generated approximation. The tool labels provenance explicitly and never presents either tier as observed product behavior.
How is page coverage calculated?
The page is split into chunks and ranked locally with lexical retrieval. Coverage is the share of query terms represented in the strongest chunk; a row is marked covered at 55 percent or higher.
Does a coverage gap mean I should add a new section?
Not always. Add material only when the sub-query is relevant to the page’s purpose and you can answer it accurately. Memory-led, off-intent, or redundant rows can be ignored.
Does the tool use embeddings?
No. The current coverage check uses BM25/cosine-style lexical components and reports that embeddings were not evaluatedEmbeddings are dense numerical vectors — lists of floating-point numbers — that represent the meaning of text in a high-dimensional space. Semantically similar content lands close together, so search and AI systems can match by meaning, not just keywords.. Semantic coverage can therefore be missed when a passage uses different wording.
What happens to my Gemini API key?
It stays in the password field for this tab and is sent directly from your browser to Google in the x-goog-api-key request header. The site does not receive, store, log, or persist it. Google API usage can consume your project quota or incur charges under your Google account.
 
Next stepChunk Tester — verify it with a direct check.

Explore more
ToolAI Search Volume EstimatorToolKeyword Idea GeneratorToolAI Answer PreviewArticleGrounding in AIArticleContent FreshnessArticleHow AI Search Works
 
 
 
Feature requests for Query Fan Out
 

Upvote what you want most. New ideas can be submitted from the floating Feedback menu;
      requests appear here once approved, and the most-wanted rise to the top.

   
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Common issues & how to fix them
  
     
  •  Warnings Page leaves a sub-query uncovered Fix: Add a self-contained passage that directly answers the uncovered sub-query in the answer format its intent requires. 
  •  Information Page only partially covers a sub-query Fix: Expand the best matching passage with the missing criteria, steps, entities, or evidence needed to answer the full sub-query. 
  •  Warnings Best matching passage is weak Fix: Rewrite the matching passage with a direct answer, precise query terms, and supporting evidence that stands on its own. 
  •  Information Fan-out branches overlap Fix: Merge overlapping branches into one distinct question or rewrite them so each branch covers a separate intent. 
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