What an AI watermark is and why you cannot rely on it in a text
What a model's hidden mark in a text looks like, which models add one today and why you cannot lean on it when judging a text.
Blog
How AI text detection works for Czech and Slovak, how to read the results and what to do with them at school, in editorial work and in your own writing.
How it works
Why AI text detectors do worse in Czech and Slovak than in English
Czech and Slovak have free word order, rich inflection and smaller training corpora. What that does to detection and how to read the results.
What the percentage from a detector means and what to do with it
A score of 0 to 100 is not the probability of cheating. How to read it, where the thresholds are and what to do with a result in the middle.
When your own text comes out as AI: what false positives are
Why an honestly written text sometimes ends up with a high score, and how to resolve that situation without accusations.
Perplexity, burstiness and stylometry: three different routes to detection
The three approaches AI text detectors are built on, how they differ and why they give different results on the same text.
Detection against your own hand: what a personal style baseline is
How comparing a text against your own older texts works and why it gives a different answer than a general detector.
How to spot AI text by eye: nine signs to watch for
The specific patterns that give generated text away even without a tool — and why none of them is enough on its own.
How much text a detector needs to make sense
Why short texts cannot be judged reliably, where a reasonable minimum lies and how to split a long text.
Practice
How to get through thirty term papers in a morning
A procedure for working through a whole batch of student papers without spending half an hour on each one.
Humanising a text vs. rewriting by hand: when each is worth it
The difference between rewriting a sentence automatically and rewriting it yourself, and how to use humanisation so the text does not end up worse.
Checking a master's thesis chapter by chapter
Why not to check a whole thesis at once, where false alarms pile up and when to schedule the check.
How to fit detection into an editorial process with freelancers
Where an AI check belongs in an editorial workflow, how to communicate it to authors and what to do with the result.
Cover letters written by AI: what to do about it in hiring
Why cover letters are now almost useless as a filter, and how to adjust hiring instead of hunting for culprits.
Verification questions instead of accusations
Why a conversation about the text works better than confronting someone with a number, and how to prepare the questions.
Why keep a history of checks and what you can read from it
A one-off check says little. What several checks of the same author over time can tell you.
Checking PDF and DOCX without copying the text
Which formats can be uploaded, what to do with a scanned PDF and why checking the file beats checking an excerpt.
Choosing
How to choose an AI text detector: seven things worth comparing
What to compare AI text detectors on — Czech and Slovak, sentences instead of a single number, handling false positives, history and how your data is treated.
AI content and SEO: what search engines actually object to
Search engines do not punish AI text as such. What really drags results down, and where detection is useful for content teams.
Rules
Can a detector result be proof?
What a detector result can carry and what it cannot, how to handle it at school and in a newsroom, and what to do instead of accusing.
What happens to the text you put into a detector
What to ask about any tool, where the text is stored in DetekceGPT and why this matters with other people's work.
How to write school rules for using AI
What a rule needs to contain to work — concrete boundaries instead of a ban, and an agreed procedure when there is a suspicion.