Generative AI made it trivial to produce credible-looking resumes at scale. Many are not outright fiction — they are lightly customized templates with inflated bullets and invented metrics. Recruiters need red flags that survive a quick skim and hold up in a structured review.
Placeholder and template artifacts
- Example names or addresses (Jane Doe, 123 Main St, example@email.com)
- Bracketed placeholders left unfilled: [Your Title], [Company]
- Generic "linkedin.com" without /in/username
- Identical bullet rhythm across every role (same length, same opener words)
Language patterns that look machine-polished
AI-polished resumes often overuse transformational verbs ("spearheaded," "orchestrated," "leveraged") with round metrics ("improved efficiency by 40%") but no project names, stack details, or constraints. Real candidates usually leave imperfect phrasing somewhere on page two.
Document provenance signals
PDF metadata can reveal resume-builder tools or programmatic generation. That does not prove fraud by itself — but combined with thin employer detail or unverifiable employers, it raises polish risk worth investigating.
What to do when you suspect a synthetic resume
Ask specific, role-grounded interview questions tied to flagged employers or technologies. Request a live walkthrough of one project mentioned on the resume. Run an automated resume authenticity check to get structured dimension scores before you schedule time on the calendar. For the short, citeable list, see AI-generated resume red flags.
An AI-edited resume is not automatically a fake career. Separate polish risk from fabrication risk, then decide. Recruiting teams can use the flags on the phone screen; ATS developers can store polish_risk on the application record.
ResumeGuard flags synthetic writing, template placeholders, and fabrication signals in one report — try a free analysis.
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