Resume Building

    Which Words Make Your Resume Sound AI-Written — and What Should You Say Instead?

    Pukar Khanal

    By Pukar Khanal, Product Lead at ResumeAI

    13 min readPublished

    Pukar Khanal leads product at ResumeAI, working on AI resume parsing, ATS scoring, and semantic job matching. He writes about how applicant tracking systems read resumes and how job seekers get past them.

    Recruiters are not running AI detectors on your resume. They react to genericness: the same tell-words and the same unquantified claims arriving in near-identical applications. The fix is specificity. Use your real numbers, your real stack and your real project names.

    ResumeAI is the free Resume AI platform that builds your resume and matches you to real jobs across the hidden job market. The decoder table below covers every word worth replacing.

    Key takeaways

    • No detector. No major applicant tracking system advertises built-in AI-writing detection. It parses your resume and lets recruiters search and rank.
    • Generic ranks lower. Vague phrasing matches fewer of the specific terms recruiters search inside the ATS.
    • Vague claims fail in the interview. A bullet you can't back invites exactly one follow-up question.
    • Draft with AI, then edit. Replace every abstraction with the real datum it stands in for. If no real datum exists, cut the bullet.

    Which words make a resume read as AI-written?

    The quoted words below are the subject matter, so they appear in full here and nowhere else. Each row explains why the word reads as generated text and gives the replacement move: name the action, the object and the measurable change. Every example in the "instead" column is an illustration, with placeholders where your real data goes.

    The tellWhy it reads as AI / genericWhat to write instead
    “Spearheaded”The single most recognizable template verb. It asserts leadership without saying what you led, and it shows up in AI drafts so often that it reads as boilerplate before the recruiter finishes the line.Name the act: “led a team of four,” “proposed and built,” “made the call on the queueing design.” If you did not lead anything, use the verb for what you did: built, migrated, debugged, shipped.
    “Leveraged”Business-school filler for “used.” Models reach for it because it sounds senior. The sentence says nothing more than “used” would.Say used, name the tool, attach the result: “used Redis to cache session lookups so the profile page stopped hitting the database on every request.”
    “Dynamic”Describes nothing verifiable. A “dynamic environment” or “dynamic team player” is a claim no interviewer can test and no recruiter can search for.State the actual condition: “a two-engineer team where I owned the release pipeline end to end.”
    “Results-driven”Every candidate claims it. Nobody can prove it in adjective form. It is a self-assessment, and self-assessments are exactly what AI drafts pad summaries with.Show one result: “reduced checkout page load from Xs to Ys,” with your real numbers in place of X and Y, beats any adjective.
    “Passionate”Passion is asserted, never demonstrated, and it appears in AI-generated summaries almost by default. Recruiters skim straight past it.Give the noun form of passion: a side project with a link, or an open-source contribution the reader can click.
    “Cutting-edge”Vague technology flattery. If the technology matters, its name matters more than any adjective attached to it.Name the technology and the scale: “migrated the nightly ETL jobs to Spark on EMR.”
    “Seamlessly”An adverb claiming perfection. Nothing integrates without friction, and everyone who reviews resumes knows it. The word signals the writer wasn't there.Describe the seam you solved: “handled the cutover with a dual-write period and a rollback script.” The friction is the story.
    “Meticulous”A self-graded personality trait. AI drafts use it to fill the skills summary. Nobody can search, rank or verify it.One concrete act of care: “caught a currency-rounding bug in code review before it reached production.”
    “Delve”Rare in human resume writing, common in model output. It's one of the vocabulary quirks readers now associate with ChatGPT-era text.Plain verbs: investigated, profiled, traced the bug, read the source.
    “Furthermore”Essay connective tissue. Resumes are bullets, not paragraphs. Transition words signal the text was generated as prose and pasted in.Delete it. Bullets don't need transitions. Each one stands alone.
    “In today’s fast-paced world…”The canonical AI opener. It stalls for a full sentence before saying anything about you, and hiring readers have seen it hundreds of times.Open the summary with your role and proof: “Backend engineer, four years on payment infrastructure.” Then add the scale you worked at.
    Em-dash chains — clause after clause — stitched with dashesA punctuation rhythm that model output overuses. One dash is style; several per paragraph is a fingerprint.Break the sentence. Short declarative sentences read as human and scan faster under a recruiter's skim.
    The “X, Y, and Z” triad: “improving efficiency, scalability, and collaboration”Models love triads of abstract nouns because they sound complete. Three vague claims are not more convincing than one specific one.Pick the one that is true and prove it: “cut deploy time from forty minutes to twelve” (your real before-and-after numbers).

    Every replacement is a concrete act plus its object, with room for a real number. The words in the left column share one defect. Someone who was not there could have written them. Nobody else could write the replacements.

    What does a de-AI'd resume bullet actually look like?

    Here is the same editing applied to whole bullets. Every pair below is an illustration written for this article, not taken from a real resume. The placeholders show where your own stack, scope and numbers go. Don't copy the rewrites. Copy the move.

    Experience bullet: web project · illustration, not a real resume

    Before: "Spearheaded the development of a dynamic web application, leveraging cutting-edge technologies to seamlessly enhance the user experience."

    After: "Built the checkout flow in Next.js with Stripe; cut the steps to purchase from five to three (swap in your real step count and the metric your team tracked, such as completed-checkout rate or support tickets)."

    The rewrite survives the follow-up question. “Which technologies? Enhanced how?” kills the first version in an interview. The second version already answers it.

    Summary line · illustration, not a real resume

    Before: "Results-driven software engineer passionate about crafting seamless, scalable solutions in fast-paced environments."

    After: "Backend engineer, four years on payment systems, ships Go services on Kubernetes, maintainer of an open-source webhook relay (link it). Replace every item with your real tenure, domain, stack, and one clickable proof."

    The first version fits every engineer alive, which is exactly why it convinces no one. The second fits only its author.

    Experience bullet: quality claim · illustration, not a real resume

    Before: "Meticulous attention to detail, leveraging data-driven insights to drive impactful business outcomes."

    After: "Found and fixed a rounding bug in the billing pipeline that had been overcharging annual-plan customers (name your real system, state what you found; add the correction amount only if you can back it in an interview)."

    Nobody can verify a trait. An incident they can check. One real incident outweighs a paragraph of self-description.

    Experience bullet: collaboration claim · illustration, not a real resume

    Before: "Utilized advanced problem-solving skills to deliver innovative solutions, ensuring seamless integration across cross-functional teams."

    After: "Integrated the mobile app with the new auth service: migrated the token endpoints, wrote the fallback path for legacy sessions, and coordinated the cutover with the iOS and Android teams (your real endpoint count and cutover details go here)."

    “Cross-functional collaboration” is the claim. Naming the teams and the cutover is the evidence.

    What do recruiters actually detect: AI writing or something else?

    No major applicant tracking system advertises built-in AI-writing detection. The ATS parses your resume into structured fields and lets recruiters search and rank the candidate pool. It has no opinion on who or what wrote the text. We found no reliable public evidence of hiring software silently rejecting resumes for being AI-written, and we won't quote a survey we couldn't verify.

    One separate shift is real. Some recruiters now paste resumes into LLM tools or use LLM-powered screening features that rank by meaning instead of keyword counts. How that changes your writing is its own post, LLM resume screening vs keyword ATS.

    What goes wrong is quieter, and it happens twice. First, in the ranked pile. When many applicants paste lightly edited model output, their resumes converge on the same vocabulary and the same unquantified claims. A recruiter doesn't need a detector to notice that candidate twelve reads like candidates four through eleven.

    Interchangeable candidates get interchangeable treatment. Generic phrasing also matches fewer of the specific terms recruiters search inside the ATS. So the generic resume tends to rank lower before a human reads a word of it.

    Second, in the interview. Model-drafted claims are often claims you cannot back. A bullet claiming ownership of an architecture you only observed invites one follow-up question. The gap between the resume's confidence and your answer does more damage than any word choice. Remove the vagueness and there is nothing left to flag.

    For what published research does and does not show about resume wording and interview odds, see normal vs. optimized resume interview rates. If your real problem is silence at volume, with hundreds of applications and no responses, word choice is probably not the first thing to debug. The response-rate diagnostic tells you which system to fix first.

    How do you use AI on your resume without sounding like it?

    Treat the model as a drafting tool, not an author. AI is good at structure, at tightening a three-line bullet and at proposing verbs you wouldn't have reached for. It can't know your p95 latency, your team size, the name of the internal service you rebuilt, or which of your changes moved a metric. So it fills the gaps with confident filler, and readers recognize the filler.

    Generate the draft. Then go bullet by bullet and replace every abstraction with the real datum it stands in for. If no real datum exists, delete the bullet.

    Then align the vocabulary with the job you're applying to. Recruiters search their ATS using the phrasing of their own job description. Mirroring the posting's terms, only where they are true of you, does more for your visibility than any amount of wordsmithing. The technique, and how it differs from buzzword-stuffing, is covered in how to get past the ATS.

    Full disclosure: the ResumeAI team writes this blog. ResumeAI's free AI resume builder is built around this workflow. It drafts bullets from the details you give it about your own work, instead of inventing a plausible engineer from nothing. The AI resume builder guide walks through it. Before you send anything, the free ATS checker shows how your wording matches the job description you're targeting, so you can see the gap.

    How we know this, and what we cited

    Pukar Khanal, Product Lead at ResumeAI, wrote this article. It was last reviewed on . ResumeAI is the free Resume AI platform that builds your resume and matches you to real jobs across the hidden job market. The team reads AI-drafted resumes and their edited versions every working day, in the product's output and in the resumes users bring to it.

    This post cites zero external statistics, on purpose. The tell-word list comes from that daily editing work: which phrases recur across unedited model drafts, and which edits make a bullet defensible in an interview. We didn't find a published study on this topic that survived verification.

    Claims about mechanism, meaning how ATS parsing and recruiter search behave, are reasoned from systems we build and operate. This site also holds its own writing to the standard described here. The phrases in the decoder table are banned in our posts too, and the body prose avoids them outside the quoted examples.

    Sources and further reading

    Frequently asked questions

    Does my resume sound like ChatGPT?

    Run it against two checks. First: could the same sentence appear on a stranger's resume without editing? If yes, it is generic — and genericness is the property people actually mean when they say a resume "sounds like ChatGPT." Second: does each bullet name a specific system, tool, or measurable change, or does it stack abstractions like "innovative solutions" and "impactful outcomes"? Default AI output is fluent but unspecific, because the model does not know your projects, your numbers, or your stack. A bullet that only you could have written cannot sound like everyone else's.

    Can recruiters tell if a resume is AI-written?

    Not reliably, and most are not trying to. What a recruiter working through a stack of applications notices is repetition: the same vocabulary and the same unquantified claims recurring across many resumes at once. That pattern-matching flags generic writing whether a model produced it or a human copied a template. The practical consequence is identical either way — a resume indistinguishable from the pile gets treated like the pile. The goal is not to hide AI involvement; it is to stop being interchangeable.

    Does the ATS detect AI writing?

    No major applicant tracking system advertises built-in AI-writing detection, and mechanically the ATS is doing something else entirely: parsing your resume into structured fields, then letting recruiters search and rank candidates by how well the text matches the role. A generic, AI-flavored resume loses inside that system not because software flagged it as AI, but because vague phrasing matches fewer of the specific terms recruiters search for. The realistic risk is low rank and silence — not an "AI detected" rejection email.

    Should I stop using AI to write my resume?

    No. The tool is not the problem; unedited output is. AI is genuinely useful for drafting structure, tightening overlong bullets, and suggesting phrasings you would not have reached on your own. It becomes a liability when the draft ships as-is, because the model fills the gaps it cannot know with confident generalities — claims you then have to defend in an interview. Use AI for the first pass, replace every generality with your real numbers, systems, and project names, and delete anything you could not explain out loud to an interviewer.

    What words should I remove from my resume?

    Start with the words that carry no verifiable content: "spearheaded," "leveraged," "results-driven," "dynamic," "passionate," "cutting-edge," "seamlessly," "meticulous," "delve," essay connectives like "furthermore," and openers like "in today's fast-paced world." But the real test is not a banned list — it is whether a word can be replaced by evidence. "Spearheaded X" converts cleanly to "built X" or "led the four-person team that shipped X." If deleting a word costs the sentence nothing, the word was filler.

    Is "spearheaded" bad on a resume?

    It is not disqualifying on its own — recruiters have been reading it for decades — but it has become one of the most recognizable markers of template and AI writing, because it appears constantly and asserts leadership without evidence. The stronger move is to state what the leadership consisted of: who you directed, what you decided, what shipped. "Led three engineers through the payment-service rewrite and made the final call on the queueing design" says more than "spearheaded" ever can, and nobody wonders whether a model wrote it.

    How do I make an AI-drafted resume sound human?

    Feed specificity back in. Go bullet by bullet and ask what the sentence would need before only you could have written it: the system's name, the stack, the scale, the measurable before-and-after. Replace abstract nouns ("solutions," "impact," "outcomes") with concrete ones — the service, the migration, the number. Cut every line that describes a quality ("detail-oriented") rather than an act. Then read the result aloud: anything you would not say to an interviewer's face gets rewritten in the words you would actually use.

    What to ask next

    Each follow-up below links to the page that answers it.

    Specific beats generic. Start with your real data.

    ResumeAI's builder drafts bullets from the details you give it about your own work: your stack, your projects, your numbers. Then check the result against the job description you're targeting. No credit card required.

    Build my resume from my real dataCheck my wording against a real JD

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