What we found
People post their resumes to Reddit every day asking why they aren’t getting interviews. We took 49 of those resumes and ran them through the exact detection logic that scrubs every application generated on Job-Hunt. No bespoke scoring, no AI detector black box — just the mechanical, vocabulary, and style checks our own output has to pass.
Two thirds of the resumes trip at least one detector, and more than a third carry a strong LLM fingerprint: an em dash, invisible unicode, or three or more phrases from the banned vocabulary list. 26 of the 33 users have at least one flagged resume.
51% of these resumes, had they arrived as LLM output, would be modified by the same 40+ checks that clean every generated application.
How we measured
The audit reuses production detection logic. Nothing bespoke, everything reproducible.
- 1
Corpus. 49 resumes from 33 Reddit users, keeping only documents with at least 200 characters of real prose.
- 2
Detectors. The exact method we use in production: mechanical unicode tells, the 35-phrase banned vocabulary list, and the style rules from our generation prompt.
- 3
Scoring. Headline numbers use conservative scoring: the spaced en dash ( – ), a common word-processor artifact, is not counted as a tell. The strict variant is in the comparison table below.
- 4
Privacy. Only aggregate counts left the analysis. No resume content was read, retained, or stored.
Characters humans don’t type
Zero-width unicode cannot be typed on a keyboard. It arrives one way: by copy-pasting out of a chatbot. 1 in 10 of these resumes contains literally invisible characters — the closest thing this audit has to a smoking gun, because no keyboard produces them.
Why the gray bar doesn’t count. The spaced en dash appears in 33% of resumes, but Word and Google Docs autocorrect produce it on their own, so it is excluded from headline scoring. Our product still normalizes it — an ATS parser doesn’t care where a character came from.
The vocabulary problem
22 of 49 resumes (45%) use at least one phrase from the banned vocabulary list, and 11 (22%) use three or more. These are the words recruiters and AI detectors now read as machine writing — whether a human typed them or not. “Cross-functional collaboration” alone appears nine times. The single worst resume packs six different banned phrases from the 35-phrase list.
Style patterns
Style is the softest signal — it means “reads like AI”, not “is AI”. Two patterns stood out, in opposite directions.
How concentrated are the tells?
The tells aren’t spread evenly. Only 15 of 49 resumes are fully clean, and 13 sit at six or more tells — where the AI read is hard to argue with.
of the corpus would be rewritten by our sanitizer if it arrived as LLM output. 26 of 33 users have at least one flagged resume.
Strict vs. conservative scoring
Counting the spaced en dash moves the headline number — but not the strong-fingerprint rate, which never depended on it. Headline metrics on this page use the conservative column; strict is shown for transparency.
| Metric | Strict (en dash counts) | Conservative |
|---|---|---|
| Any tell at all | 41 / 49 · 84% | 34 / 49 · 69% |
| Any mechanical unicode tell | 24 / 49 · 49% | 10 / 49 · 20% |
| Strong LLM fingerprint | 18 / 49 · 37% | 18 / 49 · 37% |
| Any banned AI phrase | 22 / 49 · 45% | 22 / 49 · 45% |
| Participle-tail sentences | 30 / 49 · 61% | 30 / 49 · 61% |
| Completely clean resumes | 8 | 15 |
| Users with a flagged resume | 30 / 33 | 26 / 33 |
The caveats we publish with
The honest footnotes that keep the numbers defensible.
Why this matters when you apply
We don’t know for sure how any given company screens resumes — practices differ wildly between a five-person startup and an enterprise ATS pipeline. Our decision is pure caution: English is rich enough to tell your story without a single one of the tells a filter could read as a red flag.
We checked the resumes people bring to Reddit. 69% would trip at least one AI-detection heuristic. Nearly half use vocabulary recruiters now flag as machine-written — and 1 in 10 literally contains invisible ChatGPT characters. That’s why every application we generate is scrubbed through 40+ checks before you send it to any ATS.
Check your own CV. Run these checks on your resume and see which tells it contains. It runs entirely in your browser — the file is never uploaded, and no account is needed.
Reproducible. Every number comes from the audit itself: production detection logic imported from sanitizeLlmText.ts, run on 7 August 2026. Aggregates only — no resume text retained.