Checking Text for AI: How to Read the Result
Checking text for machine origin is not a verdict or proof, but an assessment of probability based on a set of signs. Understanding its limitations is more important than knowing the number. You can run the text through an advanced detector that counts over a dozen metrics and shows them separately.
What Detectors Look For
- Predictability. Machine text is generally smoother: fewer unexpected words and phrases.
- Variation in sentence length. Humans have more variation.
- Repetition of structures and typical connectors.
- Poverty of vocabulary despite formally correct language.
- Lack of specificity — names, numbers, details that the model does not know.
Why You Can't Trust the Number Literally
| Situation | What the Detector Will Show |
|---|---|
| Live text in a strict business style | Often a high “machine” percentage — the style is smooth by definition |
| Machine text after editing | Usually a low percentage |
| Translation of human text | Shifts towards “machine” |
| Short fragment | Assessment is unreliable: volume is needed |
How to Use the Result
- Don’t chase a zero percent — it’s not a quality metric.
- Look at individual metrics: sentence uniformity and poor vocabulary can be fixed with editing.
- Add what the model won’t come up with: numbers, examples, personal experience.
- Check readability and tone — readability, tone.
- If the text is being edited for a platform — remove unnecessary characters with the tag detector.
Why Run Text Through a Detector at All
Not for the percentage, but for editing: metrics indicate where the text is uniform, where the vocabulary is poor, and where specificity is lacking. These are the same edits that make the material clearer to a human — just found faster than by reading with the eyes.
Our Position
We do not hide that articles for publication are generated by a model, and we explain why they still need a proper structure and content: this affects whether the publication will survive moderation and get indexed. More details in the article “Who Writes Articles”.