What an AI content detector can and cannot do
Large language models write in recognisable ways: very even sentences, a fondness for certain phrases, tidy transitions and little personal voice. A detector looks for those patterns and estimates how closely a text matches them. That is useful as a prompt to read more carefully, but it is not proof, and anyone selling certainty should be treated with caution. Even the companies that build AI models have found reliable detection very hard.
The signals this tool measures
- Even sentence rhythm: people mix short and long sentences; AI text often keeps a steady length.
- Stock phrases: words such as "delve", "tapestry", "navigate the complexities" and "plays a crucial role" appear far more often in AI output.
- Formal transitions: sentences that keep opening with "Moreover", "Furthermore" or "In conclusion".
- Repeated wording: the same three-word groups returning again and again.
- Chat-style formatting: em dashes, bold text, headings and bullet lists.
- Personal voice: contractions, first-person words and questions pull the estimate towards human.
How to read the result
- Check the length. Under 150 words is only a rough guess.
- Look at the reasons, not just the number. A high score driven by one signal is weaker than several signals agreeing.
- Read the highlighted sentences yourself and decide whether they sound like the author.
- Compare with other writing by the same person if you can.
Using it fairly
Teachers, editors and hiring managers should never rely on a detector alone. Non-native writers and formal styles are often wrongly flagged. If you publish content, the better goal is quality: add real examples, original data and your own voice. Our guides on generative engine optimization and using ChatGPT for business explain how to use AI well without sounding generic.
Related tools: the word counter, text compare and case converter.