WROITER Blog
Every guide here starts from the same premise: a number alone tells you nothing. Run the diagnostic, read the flags, then decide whether the text deserves a closer look or a conversation — not an accusation.
Start here
- How to Check AI Writing — the full review workflow: sample size, pattern evidence, context checks, and what to do with the result.
- How to Spot AI Writing — the strongest visual tells, the weak clues people overuse, and how to verify a suspicion without overreacting.
- WROITER Diagnostic — paste text, inspect the flags, read the score with the evidence in front of you.
Read by question
How do AI detectors actually work?
How AI Detectors Work — what detectors measure, why two tools can disagree on the same text, and why essays and edited prose trigger false positives.
Do they work at all?
Do AI Detectors Work? — the short answer, the longer reliability brief, and the policy-safe way to treat detector output as triage instead of proof.
What about false positives?
False Positive Hall of Fame — documented cases where clearly human text was flagged, and why detector-only decisions are dangerous.
Go deeper
The blog answers the questions people search for. The method hub explains how the diagnostic works, which patterns it tracks, and where it breaks down.
- How It Works — score construction, signal families, and the interpretation boundary.
- Pattern Library — every flagged pattern with examples and rewrite guidance.
- Limitations — false positives, false negatives, genre traps, and safe review policy.
- Method Changelog — what changed and when.
Interpretation rule
A higher score means stronger overlap with AI-typical surface patterns. It is not proof of authorship, cheating, or intent, and it is never enough to skip human review. For the full interpretation boundary and score ranges, see How It Works. If policy is involved, read Limitations first.