There is no reliable AI detector for resumes, and recruiters do not run one. What they detect instantly is AI slop: grand adjectives with no numbers, bullets that describe any job rather than yours, and a voice that never varies. AI-assisted resumes are fine — undirected AI-generated ones read as generic precisely when specificity is the whole game.
Unedited AI resume text has a recognizable flavor. Every role is "spearheaded", every project "cross-functional", every outcome "driving significant impact" — intensity without information. Bullets stay perfectly parallel and perfectly vague, because a language model without your facts can only produce the average of every resume it has seen. The absence of numbers is the loudest tell: real work produces amounts, percentages, timelines, and team sizes, and generic text has none to offer.
Recruiters read hundreds of resumes weekly and pattern-match this instantly, the same way teachers pattern-match essay slop. The judgment is not "this person used AI" — it is "this person could not be bothered to say anything specific", which lands worse. A generic resume was a rejection risk long before language models; AI just industrialized the genre.
AI is excellent at the parts of resume writing that are structure, not substance. Rewriting a bloated paragraph into a tight bullet. Suggesting stronger verbs for a draft you supplied. Translating your plain description of what you did into the vocabulary a job posting uses. Spotting that a bullet describes a duty when it could describe a result. In each of these, the facts are yours and the model is an editor — that division of labor produces text that survives scrutiny.
The failure mode is inverting the roles: giving the model a job title and asking it to invent your accomplishments. It will, fluently, and every invented line is either a falsehood on your record or an interview question you cannot answer. The rule that keeps you safe is mechanical: AI may rephrase what you did; it may never originate what you did.
Start with an artifact only you can produce: a rough list, per role, of what you actually did and any number attached — money, users, time saved, people managed, tickets closed. This raw material is the moat no model has. Then use AI on one bullet at a time with instructions that preserve specifics, and reject any output that dropped your number or added a claim you did not make.
Read the final draft aloud once. Anywhere you hear the AI voice — an adjective doing a number's job, a phrase you would never say in an interview — replace it with the plain version. The test for every line is whether you can defend it for two follow-up questions in a live conversation. Text that passes that test is yours, whatever tool helped type it.
No mainstream ATS ships AI-detection, and standalone detectors are too unreliable for hiring decisions. The practical detector is the recruiter's pattern recognition: vague, numberless, adjective-heavy text reads as generic and gets skipped, whether a model or a human wrote it.
No — a resume is a marketing document, and editing help has always been legitimate. The line is factual: AI rephrasing your real accomplishments is editing; AI inventing accomplishments is fabrication, and it collapses the moment an interviewer asks a follow-up question.
Because the model only had your job title to work from, so it produced the statistical average of every similar resume. Feed it your actual facts — projects, numbers, tools, outcomes — and constrain it to rephrasing them. Specificity has to enter from you; no prompt conjures it.
The free scorer flags the classic slop patterns — unquantified bullets, duty-speak, buzzword density — so you can see whether your resume reads generic before a recruiter does.
