LLM Visibility Checker
An LLM visibility checker is a tool that scores how easily an AI model can quote a passage. Paste a paragraph to score it on six signals, entirely in your browser.
How to use this
Checking a paragraph here is a four-step job, and nothing is uploaded.
- 1Paste a paragraph from one of your pages.
- 2We show how likely an AI assistant is to quote it — and why.
- 3Apply the suggested rewrites to make it clearer and easier to quote.
- 4Repeat for your most important paragraphs.
What the LLM visibility score measures
The score is the share of six prose signals the passage passes, and each signal is a known reason a model skips a paragraph instead of quoting it.
Two signals look at the opening: whether the first sentence states a fact or definition, and whether it is short enough to lift on its own. Four look at the whole passage: hedging, passive voice, sentence length and readability. The table below lists the exact threshold each one uses.
It does not query ChatGPT, Claude or Perplexity, and it cannot tell you whether they cite you today. It measures the passage, which is the part you control.
The six signals and what passes
Each signal is pass or fail, and the score is the share that pass. The thresholds below are read from the checker's own code.
| Signal | What passes |
|---|---|
| Opens with a direct answer | The first sentence states a fact or definition (an is / are / means shape) |
| Short first sentence | The first sentence is 30 words or fewer |
| Confident wording | Fewer than 1.5 hedging words (may, might, perhaps…) per 100 words |
| Active voice | Under 25% of sentences in the passive voice |
| Concise sentences | An average of 24 words per sentence or fewer |
| Readability | A Flesch Reading Ease score of 50 or more |
Frequently asked questions
Does this check whether ChatGPT mentions my brand?+
No. It scores a passage you paste, and never queries an AI model. To see how your whole page and brand read to answer engines, run the full audit on its URL.
Which paragraph should I test?+
The first paragraph under each main heading. It is the passage most likely to be lifted when a question matches that heading.
Is the LLM visibility checker free?+
Yes. It is free with no sign-up, and the text is analysed in your browser, never sent to a server.
Does it work for languages other than English?+
Not reliably. The readability and hedging tests are built for English, so treat scores on other languages as noise.
How to read the result
This checker grades how easily a language model can extract a clear claim from the text you paste. It is a readability and structure measure, not a quality judgement — well-argued prose can score badly here, and that is useful information rather than a criticism.
Use it on the opening paragraph of each section rather than a whole page. That is the unit an answer engine retrieves, and it is where a low score is worth acting on. A weak score on a narrative introduction is expected and usually fine; a weak score on the paragraph that answers your main heading is the one to rewrite.
The heuristics are English-language and run entirely on your device, so nothing you paste is transmitted. They approximate how a model reads; they do not query one, which is what keeps the tool free and private.
Rewriting a passage that scores badly
The fix is almost always structural rather than stylistic, and it is usually one of four moves. Put the answer in the first sentence instead of the third. Replace a pronoun that points at something in the previous paragraph with the noun itself. Split a sentence carrying two claims into two sentences carrying one each. Add the specific — the number, the threshold, the named standard — that turns an assertion into something checkable.
Restating the subject at the top of every section feels repetitive when you read the page straight through, and that instinct is what makes most well-written pages hard to quote. An answer engine never reads straight through: it retrieves one passage and discards the rest, so the repetition a human notices is exactly the context a model needs.
Sources
Last reviewed .