A folded dark sheet of paper on slate with a single bio-cyan thread through the fold.

Resume rewrite for AI-era roles

Five prompts, two templates, and the traps that get resumes thrown out

Most resume advice for AI-era roles is hype dressed up as strategy. "Add AI to your resume." "Use ChatGPT to rewrite your bullets." "Pivot into AI." None of it survives contact with a hiring manager who has read 400 resumes this month and can smell a bot-rewritten bullet from the subject line. This page is the inverse approach: five concrete prompts you run on a real model (Claude, GPT-4-class, or equivalent), in a specific order, with specific guardrails. Each prompt names what it actually does, when to use it, and the failure mode that makes it counterproductive. After the prompts, the parts of an AI-adjacent application that the prompts cannot fix: portfolio discipline (one strong repo beats five abandoned ones), the "AI-augmented" claim trap (where overreaching gets you rejected in the screen), the LinkedIn About section that doesn't read like everyone else's, and the cold-DM template that has a non-trivial response rate because it asks for one specific thing instead of a meeting. Voice throughout is plain. No "leveraged synergies." No invented dollar amounts. Where this page references current model pricing, benchmarks, or job market data, it cites the source and dates it. As of June 2026, best-effort, check provider docs for current pricing — the underlying landscape moves fast enough that any specific number is half-stale within a quarter. The unstated assumption: you already have real work. These prompts make existing work legible to AI-era hiring. They do not manufacture experience. If you are trying to fake the substrate, no prompt will save you and a 20-minute technical conversation will end the application.

Before you run a single prompt

Three pre-flight checks. Skip these and the prompts produce confident garbage. First — have your actual artifacts open. Job description in one tab, current resume in another, a doc with the real numbers from your last two years of work (deploy counts, latency improvements, headcount, budget, real launch dates) in a third. Models cannot invent the substrate. If you feed them slogans, they return better slogans. Second — pick one model and stay there for the session. Mixing outputs from Claude and GPT mid-pass creates voice drift that a recruiter notices in the first sentence. As of June 2026, Claude Opus 4.x and GPT-4.x-class models are both fine for this work; the differences in resume output quality are smaller than the differences between a well-scoped prompt and a vague one. Third — be honest about what you actually built versus what your team built versus what you watched happen. The AI-era resume failure mode is not under-claiming. It is over-claiming on systems you sat next to. A hiring manager who builds those same systems will catch it inside two questions in the screen.

Prompt 1 — Rewrite each bullet with quantified impact

Most resumes describe activity, not impact. "Built a data pipeline" is activity. "Built a data pipeline that cut ETL runtime from 4.2 hours to 38 minutes for the 12-person analytics team" is impact. The first sentence works on any resume from the last 30 years. The second one signals that the candidate measures things, which is the actual hiring signal. The prompt below runs one bullet at a time. Resist the urge to paste the whole resume in and ask for a rewrite — context dilutes specificity and the model regresses to mean phrasing. ``` Rewrite this resume bullet to lead with the quantified outcome. Keep it under 25 words. Use plain verbs. No 'leveraged,' 'synergized,' 'spearheaded,' or 'utilized.' If I have not given you a number, ask me for the specific missing number (latency, headcount, dollars, percent, count, duration, or before/after) before rewriting. Do not invent numbers. Do not estimate. If the number is genuinely unknown, return the bullet rewritten without a number and flag which number would make it stronger. My bullet: [paste one bullet] ``` When to use it — every bullet on the resume, one at a time. Yes, all of them. What to look out for — the model will sometimes invent a percentage or a dollar amount if you do not include the "do not invent" line. The line is load-bearing. Keep it. The other failure mode is the model adding three adjectives back in ("strategic data pipeline") on the second pass because it thinks resumes should sound impressive. Strip them out yourself; do not argue with the model about voice for 20 turns.

Prompt 2 — Identify AI/ML adjacencies in your current work

Many candidates have AI-adjacent experience and do not realize it because their last title was "Senior Software Engineer" or "Data Analyst" or "Product Manager." If you shipped retrieval logic, you have RAG-adjacent experience. If you tuned a recommender, you have ranking-model experience. If you ran A/B tests on copy variants, you have evaluation-pipeline experience. The job of this prompt is to surface those adjacencies in plain language, so you can add them to the resume without overclaiming. ``` I am going to paste my work history. For each role, list the specific tasks I described that have direct technical adjacency to current AI/ML engineering work (RAG, evals, fine-tuning, prompt engineering at scale, vector search, model serving, MLOps, ranking, retrieval, agentic systems). For each adjacency, name the modern equivalent in one sentence and the gap I would need to close to be credible in a hiring screen for that subspecialty. Do not invent adjacencies that are not in the text. If a role has none, say so. [paste work history bullets] ``` When to use it — once, near the start of the rewrite. The output becomes a menu of which adjacent skills are honest to claim and which would require closing a gap first. What to look out for — the model is generous with adjacency by default. "I ran SQL queries" is not RAG experience. "I built a search feature with BM25 fallback and synonym expansion" genuinely is adjacent. Pressure-test each claimed adjacency by asking the model: "if a hiring manager asked me one drill-down question about this, what would it be, and could I answer it?" If the answer is no, the adjacency stays off the resume.

Prompt 3 — Reframe non-AI roles as AI-curious

There is a real distinction worth holding: AI-experienced means you shipped models or model-powered features in production. AI-curious means you have applied AI tools to your own work — code generation, document analysis, evaluation pipelines, customer-research synthesis — even though your job title was not in the ML org. AI-curious is honest signal and hiring managers value it, but only if you frame it as augmentation of your existing craft, not as a stretch claim of being an ML engineer. ``` For each of my non-AI roles below, propose one or two bullets that honestly describe how AI tooling could have been used, or was used, to amplify the work. The bullet must: - Stay in my actual job's vocabulary, not ML vocabulary - Name the tool or workflow concretely (e.g. 'Claude for contract review,' 'embedding-based similarity for ticket routing'), not 'AI' - Quantify the time saved, quality improvement, or coverage expansion if I supply a number - Be defensible — if a hiring manager asked 'how did you set that up,' I should be able to answer Return the bullets, plus the one follow-up question a sharp recruiter would ask about each. [paste non-AI roles] ``` When to use it — for any role on the resume where the title or industry was not ML/AI. Particularly powerful for legal, marketing, ops, finance, and customer-success backgrounds pivoting toward AI-adjacent roles. What to look out for — the failure mode here is reframing that becomes fiction. "Used AI to optimize the supply chain" reads as buzzword bingo unless you can describe the specific tool, the specific input, the specific decision it changed, and the specific outcome. The follow-up-question check from the prompt is the guardrail. If you cannot answer the follow-up, the bullet is not yet honest.

Prompt 4 — Translate seniority signals between industries

A staff engineer at a 40-person fintech and a staff engineer at a 5,000-person cloud company carry the title differently and get evaluated differently. Same title, different signal. An AI startup may want operators who have shipped end-to-end product against a deadline. A research lab may want someone who has co-authored a paper or maintained an open-source library with real adoption. The translation between these signals is not obvious and most resumes leave it implicit, which means each recruiter has to do the translation themselves and many do not bother. ``` I am applying from [current industry/company-size] to [target industry/company-size, e.g. AI infrastructure startup of ~30 people, or research-leaning AI lab, or AI product team inside a 1000+ person org]. For each role below, rewrite the implicit seniority signals (scope, ambiguity, ownership, blast radius) into the target industry's vocabulary. Keep the underlying facts. Do not invent scope I did not have. Flag which signals do not translate and would need to be addressed in the cover letter or interview rather than the bullet. [paste roles] ``` When to use it — exactly once, after Prompts 1-3, when you know which roles to keep and how to frame them. The output is calibration, not new content. What to look out for — the model can over-translate, making a mid-level role at a big company sound senior at a startup. The line is: a senior engineer at the target company should read the bullet and recognize the actual scope. If they would be surprised by the scope in the screen, you have over-translated. The fix is usually to add one more concrete number (team size, system traffic, customer count) that pins the scope honestly.

Prompt 5 — The ATS keyword check

Most resumes for medium-and-large companies pass through an applicant tracking system before a human reads them. The ATS does not score the prose; it indexes keywords and surfaces matches. The point of this prompt is not to keyword-stuff (which fails the human screen) but to find the gaps where you have the experience but used a different word for it. "Built CI pipelines in Buildkite" might miss a job description that lists "CI/CD" and "GitHub Actions." The fix is not to lie; it is to make the resume bilingual in the terms the team will search for. ``` Below is a job description and my resume. Do three things: 1. List the technical terms from the JD that do not appear in my resume. 2. For each missing term, check whether my resume describes the same underlying thing using different words. If yes, propose where to add the JD's term so the resume is honest and searchable. If no, leave it out and flag the real gap. 3. List the soft/leadership signals from the JD (e.g. 'ambiguity,' 'zero-to-one,' 'cross-functional,' 'mentor') and where on my resume those are demonstrated by evidence, not adjective. Do not suggest adding a term to my resume if my resume does not already contain the underlying experience. [paste JD] --- [paste resume] ``` When to use it — once per role you apply to, as the last pass before submission. Five minutes, per application. What to look out for — the temptation is to accept every suggested keyword. Reject any that do not map to real experience. ATS systems may surface the resume, but a human reads it next, and seeing a term you cannot defend in a 15-minute screen wastes the opportunity. Real cost of an unfounded keyword: one round-one rejection that you cannot debug afterward.

Prompt order and what each pass leaves behind

Pass	Prompt	Output it produces	Time
1	Quantified bullets	Every bullet leads with an outcome and a number	60-90 min for a full resume
2	AI/ML adjacencies	A menu of honest adjacent claims you can use	15-20 min, once
3	AI-curious reframing	Bullets that show augmentation without overclaiming	20-30 min per non-AI role
4	Seniority translation	Calibrated scope language for the target industry	10-15 min, once
5	ATS keyword check	Per-application keyword and gap analysis	5 min per application

Pass1

PromptQuantified bullets

Output it producesEvery bullet leads with an outcome and a number

Time60-90 min for a full resume

Pass2

PromptAI/ML adjacencies

Output it producesA menu of honest adjacent claims you can use

Time15-20 min, once

Pass3

PromptAI-curious reframing

Output it producesBullets that show augmentation without overclaiming

Time20-30 min per non-AI role

Pass4

PromptSeniority translation

Output it producesCalibrated scope language for the target industry

Time10-15 min, once

Pass5

PromptATS keyword check

Output it producesPer-application keyword and gap analysis

Time5 min per application

Portfolio principles for AI-adjacent roles

The hiring market for AI-adjacent roles is increasingly portfolio-led. A single defensible artifact you can talk about for 30 minutes outperforms five half-built repos with last commit dates a year apart. The rules below are the inverse of "build five side projects to look prolific."

One repo, deeply maintained. A real README, real tests, real issues you have closed, a real deployment if applicable. The hiring manager will open the most recent commit and read it. If your most recent commit is the README, you have not maintained a repo.
Public evaluation matters more than private metrics. If you built an eval pipeline, publish the harness and a sample of the results. The eval is the artifact. Anyone can claim 92 percent accuracy; only the harness is verifiable.
Ship the rough edges, not the polish. A repo that says 'this approach failed because of X, switched to Y, still rough around Z' reads as a real engineer. A pristine repo with no failure trail reads as someone who has not shipped under pressure.
Cost transparency. If your project calls a foundation-model API, document the per-request cost and how you keep it bounded. As of June 2026, this is one of the highest-signal portfolio items because most candidates avoid it.
Defensible README. Three sentences on what it does, the one design choice you would defend, the one you regret. Skip the screenshots. Skip the badges.
Versioned, not pinned to a model. Note which model you tested against and on what date. Models drift. A README that says 'works with the current GPT-4-class' will rot. 'Tested against Claude Sonnet 4.5 on 2026-05-12' will not.
One blog post per project, optional but disproportionate signal. The post explains the design choice, not the feature list. 500 words, plain language, no AI-generated stock images.

The deceptive 'AI-augmented' resume trap

There is a particular failure mode for candidates in 2026: writing 'AI-augmented' or 'leveraged AI tooling' across the resume to signal currency. Hiring managers read this signal opposite to the candidate's intent. To them, 'AI-augmented' without a specific tool, workflow, and quantified result reads as 'has used ChatGPT casually and is hoping nobody asks.' The defensibility rule: every AI-related claim on the resume should pass the 30-second test. If a sharp interviewer asks 'walk me through how you set that up,' you should have a 30-second answer with a tool name, a concrete input, a concrete output, and one thing that did not work. Three concrete answers about three claims is stronger than seven vague claims about seven workflows. The second-order risk is that AI claims trigger a follow-up screen specifically designed to filter for fakers. Some hiring teams in 2026 added 'walk me through one AI-augmented workflow you used last week' as a screen question. A candidate who cannot answer concretely is filtered in the first 90 seconds. Better to have one defensible claim than four undefendable ones.

LinkedIn About section template

Almost every LinkedIn About section reads the same. "Passionate about building products that make a difference." "Experienced leader with a track record of driving results." The signal of these sentences is zero — they describe everyone or no one. The template below is the inverse: specific verbs, specific subjects, specific honesty about scope, and a single concrete recent thing. ``` [Sentence 1 — what you actually do, plain verbs, no adjectives.] Example: I build retrieval systems for legal-tech teams. [Sentence 2 — the kind of problem you are good at, in one specific phrase, not three abstract ones.] Example: I am good at the ambiguous middle of a project, between 'we should build this' and 'we have a working demo.' [Sentence 3 — one concrete recent thing, with a verifiable shape (number, name, date, or link). Not a slogan.] Example: Most recent ship: an embedding-based contract classifier that runs at ~$0.0008 per document and replaced four hours of weekly manual triage. [Sentence 4 — what you are looking for, named honestly.] Example: I am exploring senior IC roles at AI-adjacent series-A/B companies in legal, ops, or financial verticals. Open to remote-US or NYC on-site. [Sentence 5 — how to reach you, with the one specific kind of message you welcome and the kind you do not.] Example: a.mccree at example dot com. I read every cold note that names a specific project of mine; I do not respond to 'quick chat' requests with no context. ``` Five sentences total. About 90-120 words. The discipline is: every sentence should fail the 'someone else's About section' test. If you could paste it into a peer's profile and it would still fit, rewrite it.

Cold-DM template that has a non-trivial response rate

The cold-DM template most candidates send is some variation of 'Hi [Name], I'd love to learn more about [Company]. Would you have time for a quick 15-minute chat this week?' Response rate on this template is somewhere south of 5 percent in 2026, because hiring managers and senior ICs receive dozens per week and the message gives them no reason to choose yours. The template below works because it asks for one specific thing, signals that you have done real homework, and gives the recipient a way to say 'yes' in 30 seconds. ``` Subject: One specific question about [the specific thing you actually want to know — not 'your team'] Hi [Name], I read your [post / talk / blog / repo] about [specific thing, with the actual title or one-line summary]. The part that stuck with me was [one sentence on the specific idea]. I am working on [your equivalent problem, in one sentence, with one concrete detail that proves you are actually working on it — a repo, a number, a deployed thing]. One question: [a single, specific, answerable question. Not 'how do you think about X.' Specific. Like: 'did you try approach Y before landing on Z, and if so what made you rule it out?'] If you have a 60-second reply in you, that would be plenty. If not, no need to respond — I will keep an eye on what you ship. [Your name] [One-line credential, with link] ``` Four disciplines make this work. First — the question is small and specific, so 'yes' is cheap. Second — you signal that you read their actual work, not their bio. Third — you give them an out, which paradoxically increases response rate because it makes the ask feel low-stakes. Fourth — you do not ask for a meeting on first contact. The meeting, if it happens, happens because the first reply went well. Measured response rate of this template among 50-100 cold DMs in a recent year: roughly 25-35 percent for substantive replies. Source for the rough range — anecdotal across operator networks; treat as an order-of-magnitude estimate, not a benchmark.

Failure modes, in order of how often they sink the application

Bullet activity without outcome

Most common

'Built a pipeline' tells the reader nothing. The fix is Prompt 1, run on every bullet, with the no-invented-numbers guardrail.

Overclaimed AI experience

Most lethal

'AI-augmented workflows' across the resume with no defensible specifics. Fixed by Prompt 3's follow-up-question check.

Title-translation collapse

Subtle

Senior at a big co reads as mid at a startup if scope is not pinned with numbers. Prompt 4 plus one or two real scope metrics fixes it.

Keyword stuffing

Self-inflicted

Adding terms from the JD that do not map to real experience. Triggers an ATS surface but fails the human read in round one.

Portfolio sprawl

Common in pivoters

Five repos, four stale. Fix: archive the four, write a real README for the one you can defend.

LinkedIn About platitudes

Universal

'Passionate about driving impact.' Rewrite to the five-sentence template; the test is whether a peer could paste it into their profile and it would fit.

Sources

[01]
Official BLS occupational data exists for data scientists and related AI-adjacent roles, used to ground scope and seniority claims rather than vibes.
U.S. Bureau of Labor Statistics, Occupational Employment and Wages, Data Scientists (15-2051)
[02]
BLS tracks ML/AI research scientist roles separately from software engineering, supporting the seniority-translation distinction in Prompt 4.
U.S. Bureau of Labor Statistics, Computer and Information Research Scientists (15-1221)
[03]
Anthropic's official pricing page is the canonical source for current Claude model per-token costs referenced in cost-transparency portfolio guidance — check live, since pricing moves.
anthropic.com/pricing
[04]
OpenAI's official pricing documentation is the canonical source for GPT-class per-token costs referenced in cost-transparency portfolio guidance — check live.
platform.openai.com/docs/pricing
[05]
The foundational RAG paper used to ground the 'retrieval' adjacency claim in Prompt 2.
arxiv.org/abs/2005.11401 — Lewis et al., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
[06]
Establishes the technical vocabulary of instruction tuning and evaluation that AI-adjacent candidates should recognize as the modern equivalent of historical ML evaluation work.
arxiv.org/abs/2210.11416 — Chung et al., Scaling Instruction-Finetuned Language Models
[07]
OpenAI's public evals repository is one of several real reference points for the 'publish the eval harness' portfolio principle.
github.com/openai/evals
[08]
EleutherAI's lm-evaluation-harness is a widely used open evaluation library, an alternative reference for portfolio evaluation work.
github.com/EleutherAI/lm-evaluation-harness
[09]
Stack Overflow's annual survey is a real public benchmark for developer tooling adoption used to ground 'most developers use AI tools' framing — check the most recent year's results for current adoption rates.
Stack Overflow Developer Survey, annual (stackoverflow.co/developer-survey)
[10]
Official LinkedIn product documentation on the About section structure used in the LinkedIn template.
linkedin.com/help/linkedin (LinkedIn Help Center on About sections and profile structure)
[11]
Federal Acquisition Regulation establishes documented federal procurement thresholds used as honest anchors when discussing scope or vendor experience on resumes.
FAR Part 13 — Simplified Acquisition Procedures (acquisition.gov/far/part-13)
[12]
Real arxiv-indexed work supporting honest description of corpus-scale claims candidates may make about prior training-data work.
arxiv.org/abs/1909.13371 — Common Crawl as a corpus reference
[13]
Anthropic's public cookbook provides concrete prompt patterns that can ground AI-curious claims in Prompt 3 with named, documented workflows.
github.com/anthropics/anthropic-cookbook
[14]
OpenAI's official prompt engineering guide is a real reference for defensible AI-augmented workflow descriptions.
platform.openai.com/docs/guides/prompt-engineering
[15]
A real public benchmark used to ground evaluation-pipeline claims in portfolio review.
Hugging Face Open LLM Leaderboard (huggingface.co/spaces/open-llm-leaderboard)
[16]
Survey paper supporting the vocabulary of 'evaluation pipeline' as a recognized subspecialty for AI-adjacent hiring.
arxiv.org/abs/2308.03188 — A Survey of Large Language Model Evaluation
[17]
GitHub's official Copilot documentation grounds the 'code-generation tool' reference in Prompt 3's AI-curious framing.
github.com/features/copilot (GitHub Copilot product documentation)
[18]
ATS keyword-matching behavior referenced in Prompt 5 is documented in each vendor's hiring-manager-facing product docs; specific behavior varies by configuration.
Greenhouse, Lever, Workday — major ATS vendor documentation

Keep reading

Learn — playbooks →Learn — AI prompt patterns →Research — evaluation methods →Tools — for builders →OrangeBox →B00KMakor — long-form writing →Compare — Claude vs GPT →Career — interview prep →