TL;DR
- Most resume feedback focuses on surface issues — formatting, length, word choice — because those are the easiest things to spot at a glance.
- The more useful review asks a harder question of every line: could you defend this, specifically, if someone asked a follow-up question about it?
- A good reviewer, human or AI, should flag vague claims (“led cross-functional initiatives”) and ask what specifically happened, not just approve the sentence as written.
- A review grounded in your actual documented history can check claims against evidence, not just against how professional the sentence sounds.
- The best outcome of a resume review isn't a cleaner-sounding resume — it's a resume where every line is something you could confidently expand on if asked.
What most reviews actually catch
Typos, inconsistent formatting, passive voice, a summary that's too long. All real, all worth fixing, and none of it touches whether the content underneath is actually strong or just well-formatted.
The harder, more useful question
For every bullet: could you explain this, specifically, if someone followed up? “Led cross-functional initiatives to drive growth” survives a formatting check and fails this one immediately — what initiatives, what growth, how much.
What a good review flags instead
Vague verbs without a specific object — “improved,” “optimized,” “drove” — that aren't attached to a number, a scope, or a named outcome. A good review doesn't just clean up the sentence — it asks what actually happened and pushes for the specific version.
Why this needs your real history, not just the resume text
A review that only looks at the resume can polish the wording but can't tell you whether a claim is actually true or exaggerated. A review grounded in your documented work — the actual STAR entries, the actual numbers — can check the resume against the record, not just against how it reads.
What a review checks beyond the bullets
A thorough review doesn’t stop at individual bullets that hold up under a follow-up question. It also checks whether the resume is the right length for the career behind it, whether the summary line earns its space or should be cut, and whether the skills section is backed up by evidence elsewhere on the page — including whether a listed AI tool is a demonstrated skill or just a claim. For roles that involve international hiring, it means checking the resume follows the right CV vs. resume convention for where it’s headed. And for portfolio-heavy roles, it means checking whether the portfolio itself backs up what the resume claims.
Frequently asked questions
Can AI do a useful resume review, or does it need to be a person?
AI can be useful if it's checking claims against your actual documented history rather than just judging whether sentences sound professional — otherwise it tends to reward confident-sounding language regardless of whether it's specific or true.
What's the single most common issue in resume reviews?
Vague, unquantified claims — verbs like “improved” or “led” without a specific number, scope, or outcome attached.
Should every bullet have a number in it?
Not every one, but most should have something specific — a number, a name, a scope, a timeframe — that makes the claim checkable rather than generic.
How do I get a review that checks claims, not just formatting?
Ask the reviewer directly to flag anything vague or unverifiable, and, if using AI, point it at your actual documented work history so it can check claims against real evidence.
Related reading: How to build a brag doc with AI.