Skip to main content
Lynt

AI and resume writing

AI Resume Edits You Should Reject

By · · Updated

Recruiters report spotting AI writing mainly by its phrasing. That makes some AI edits useful and others actively costly. Here is how to tell them apart.

AI Resume Edits You Should Reject

Most advice about AI and resumes argues about whether you should use it at all. Behaviour has largely settled that question: reporting through 2026 suggests a large majority of hiring teams now regularly encounter AI-assisted applications. The more useful question is which edits to keep and which to throw away, because those are not the same decision.

This article assumes you will accept some AI edits. It is about the ones that cost you.

What reviewers say they notice

The signal recruiters report most often is not factual error. It is phrasing. Across surveys of hiring managers, unnatural phrasing is cited as the leading giveaway of an AI-written application, ahead of anything to do with content.

The specific tells reported are consistent, and worth memorising, because every one of them is something a language model does by default:

  • Generic verbs of the leverage and spearhead family.
  • Achievements described with no number attached.
  • Sentences that all arrive at the same length and shape.
  • Polish on the page that the interview does not match.

Take that last one seriously. A resume that reads better than you speak creates a gap, and the interview is where the gap gets found.

Two caveats on these numbers. Reported confidence varies widely between surveys, and much of this research comes from vendors selling products in the same market. Read it as a description of what reviewers pay attention to, not as a measured detection rate.

The three edits worth rejecting

The verb upgrade that removes the fact

A model will reliably turn "wrote the onboarding docs" into "spearheaded comprehensive onboarding documentation initiatives". Nothing was added. A specific action became a vague one, and the sentence picked up two of the reported tells on the way.

Reject any edit where you cannot point at the new information that arrived. Length is not information.

The invented metric

This is the dangerous one. Asked to make a bullet stronger, a model will often supply a number, because bullets with numbers read as stronger. It has no way of knowing your actual figure.

If a percentage or a currency amount appears in an edit and you did not provide it, that is not a suggestion. It is a fabrication you would be signing your name to, and it is exactly the sort of claim that unravels when an interviewer asks how you measured it.

Lynt's assistant writes a bracketed placeholder such as [your metric: %] instead of inventing a figure, precisely because the alternative is a resume that lies on your behalf. Whatever tool you use, treat an unsourced number in an edit as a defect rather than a draft.

The rewrite that flattens your voice

Models converge on a register. Run an entire resume through one and every bullet comes back with the same rhythm and roughly the same length, which is one of the patterns reviewers report noticing.

Some unevenness is evidence of a real person. If one role has three sharp bullets and another has one, that is often the truth of the work rather than a formatting problem waiting to be fixed.

The edits worth keeping

The same tool is genuinely good at work that is tedious and low risk:

  • Cutting a three-line bullet to one line while keeping every fact.
  • Catching tense and person inconsistencies across roles you wrote months apart.
  • Reordering bullets so the one matching this posting sits first.
  • Naming a skill you do have and forgot to list.
  • Turning a paragraph of responsibilities into lines someone can scan.

The pattern is simple. Accept edits that reorganise or compress what you already said. Be sceptical of edits that add.

A test that takes ten seconds

Before accepting a change, read the new sentence aloud and ask whether you could say it in an interview without flinching. If you would not defend the wording in conversation, it does not belong on the document whose job is to win you that conversation.

Where personalisation is missing altogether, reviewers report reacting badly. One figure puts rejection of unpersonalised AI applications at 62%. The failure there is not that a machine helped. It is that nothing in the application is specific to the role.

Why approval should be the default

This is the reasoning behind how Lynt handles AI, so read it as an interested opinion. The argument still stands on its own.

A tool that applies changes automatically optimises for the appearance of progress. A tool that asks you to accept each change forces you to read it, and reading it is the only step that catches an invented metric before an interviewer does.

If your current tool applies edits without asking, the workaround is manual. After any AI pass, reread every line that changed and confirm you can defend it.

Sources

These are industry and vendor surveys rather than peer-reviewed research, and their figures disagree with each other. They are cited as evidence of what reviewers report noticing, not as a measured detection accuracy.