The AI Resume Survival Guide (for 2025 and Beyond)
Resumes aren't doing their jobs anymore because of AI. So I wrote a reset for the AI age: how to talk about AI well, how to use AI in resume creation, and how to handle AI resume checkers
I think I get a question about resumes on my TikTok just about every day. And mostly they boil down to three questions: 1) how do I talk about my AI experience on my resume?, 2) how do I responsibly use AI in creating my resume, and 3) what do I do about the AI checkers recruiters are using to catch resumes?
All three of those problems are driven by the same thing: AI has broken the talent market by inflating the supply of cheap tokens. AI has made it very very easy to create a very polished resume, so a resume no longer acts as a signal of quality.
I’ve looked at thousands of resumes in the post-GPT era. In this brave new world, what work stands out? How do you stand out as a candidate in a world where every resume looks (at first glance) perfect? How do we talk about AI positive on our resumes when every resume mentions AI?
I wrote this guide to help reset the conversation around two key areas:
1) how do you talk about AI experience on a resume well? This also acts as a guide to crafting good bullets.
2) what’s a responsible way to handle AI checkers?
For good measure, I broke out tips by a few job areas and included an audit toolkit and a special ChatGPT prompt so you can get a sense of where your own resume is at more easily! Read on, and good luck out there…
First: If you can’t describe your AI work clearly, it doesn’t exist.
AI is on every resume now.
People say they’re “exploring ChatGPT,” “experimenting with LLMs,” “interested in AI product strategy.” Some go further—calling themselves prompt engineers, AI builders, agents experts. But when you actually look at the work? It’s often vague, unimpressive, or totally ungrounded. Even when the work is real, it’s described so poorly that it blends in with everything else.
This creates a strange distortion: people doing good, honest work with AI tools aren’t getting noticed—because they don’t know how to talk about it. And people who are just orbiting the hype are filling up space with inflated claims, vague language, and meaningless titles.
Hiring managers are exhausted. They’ve been told to “hire AI talent,” but no one’s told them what that means. They’re wading through resumes full of buzzwords—trying to guess who actually knows what they’re doing, and who just copy-pasted their LinkedIn headline after reading a Twitter thread.
So let’s get something straight. You don’t need to be an ML researcher to be valuable in this moment. You don’t need to know how to train a model from scratch, or fine-tune llama.cpp from the command line. But you do need to be able to describe your work—clearly, specifically, and in context. You need to show judgment, not just interest. Impact, not just curiosity. Taste.
This is what fluency in AI actually looks like: solving real problems with AI tools, being aware of their limits, and communicating what you did with precision. The way you frame your work is not just a branding exercise—it’s proof of thinking. And in the middle of a hype cycle, thinking is what stands out.
This guide is here to help you do that—by role, by example, and without pretending to be something you’re not.
You’ll see:
The most common mistakes people make when adding AI to their resume—and how to fix them.
Real rewrite examples: what weak bullets sound like, and how to make them sharp, credible, and specific.
A breakdown by function—Product, Engineering, Design, Ops, Generalist—so you can see what good AI work looks like in your lane.
Simple, high-leverage project ideas that actually demonstrate fluency (not just enthusiasm).
And a clear test: if someone read your resume without context, would they understand what you built, how it worked, and why it mattered?
You’re not just trying to “look like an AI person.” You’re trying to make your work legible to smart people who are scanning fast and trying to hire well. That means cutting through the noise. Showing your thinking. Naming your tools. And making your outcomes impossible to ignore.
Let’s start by looking at where most resumes go wrong—and why so much of what passes for “AI experience” is a distraction.
2. The Most Common AI Resume Mistakes—and Why They Fail
If you’re not specific, you’re invisible. If you’re not grounded, you’re not credible.
Right now, hiring managers are inundated with resumes claiming AI experience. But most of those claims fall apart in one of two ways: they’re either too vague to evaluate, or too inflated to trust. And sometimes, worse—people who’ve actually done real work describe it so poorly that it disappears in a stack of applications.
Below is a deeper breakdown of the failure modes I’ve seen most often, including 20+ examples pulled from real hiring conversations, resume reviews, and ghostwriting sessions.
Category 1: The Vague Observer
This group is well-meaning, but too far from the work. They’re interested in AI, maybe even following the ecosystem, but haven’t yet built or shipped anything.
Examples:
“Following trends in generative AI and LLMs.”
“Exploring the potential of AI to transform workflows.”
“Attended AI webinars and events to deepen my understanding.”
“Curious about how GPT can be integrated into products.”
Why it fails:
This reads like background radiation. It tells me what you’re thinking about, not what you’ve done. Curiosity is a good starting point—but without any evidence of application, it doesn’t belong in your Experience section. This belongs in a cover letter at best—or left out entirely.
Category 2: The Tool-Dumper
These bullets try to sound technical by cramming in tool names without any explanation of what they were used for or how.
Examples:
“Used GPT-4.1, Claude 3.5, Zapier, LangChain, Pinecone, and Notion to improve operations.”
“Familiar with OpenAI API, Replit, and vector databases.”
“Integrated LangChain with Pinecone for document processing.”
Why it fails:
Tool lists are not accomplishments. If I can’t tell what problem you were solving or what the outcome was, it doesn’t matter that you used Claude or Pinecone. It’s like saying “used Excel, Word, and Outlook” without saying what you built in them.
Category 3: The Inflated Generalist
These bullets are technically accurate but exaggerated. They use language better suited for a 30-person team building a platform—when the person just built a side project or ran a few prompts.
Examples:
“Architected end-to-end AI solution for autonomous agents.”
“Led AI transformation across product stack.”
“Drove strategic deployment of large-scale LLM pipelines.”
“Spearheaded agent-based multi-modal infrastructure strategy.”
Why it fails:
These phrases collapse under even light questioning. What model? What infra? What transformation? This kind of language is dangerous because it overpromises and under-delivers. It signals insecurity and résumé-padding, which breaks trust.
Category 4: The Undersold Real Work
This is the most tragic category—people who actually built something valuable, but described it so generically that it gets lost.
Examples:
“Worked on AI tooling for customer support.”
“Used LLMs to improve onboarding process.”
“Helped develop internal automation workflows using GPT.”
“Built AI prototype for business process optimization.”
Why it fails:
There’s a good chance these are great projects. But we don’t know what the AI actually did. What was the workflow? Was the AI writing text? Extracting fields? Classifying? Was it used in production? Did people trust it? You can’t assume the reader will infer these things—you need to name them.
Category 5: The Generic Contribution
This often comes from people working on larger teams where AI was part of the project, but their role wasn’t clear.
Examples:
“Collaborated on LLM integration.”
“Worked with team implementing GPT for search improvement.”
“Supported development of AI-based features.”
Why it fails:
“Collaborated” is a weasel word when not followed by specifics. Did you write prompts? Design evals? Handle error-handling logic? If the AI component was your teammate’s work, don’t claim it. But if you contributed—even a slice—own it clearly.
Category 6: The Buzzword Blender
These try to impress by stacking jargon on jargon. It looks impressive from 10 feet away, but quickly turns into soup.
Examples:
“Deployed multi-agent RAG pipelines leveraging zero-shot semantic clustering.”
“Integrated context window optimization for hybrid chain-of-thought agents.”
“Implemented function-calling orchestration layer across recursive tool handlers.”
Why it fails:
Even if you understand what you’re saying (which is not always the case), this language isn’t helpful to a hiring manager who’s trying to understand what the AI did. What was the task? Was it actually used by users? Did it run in production?
Pro tip: If your bullet reads like it was generated by GPT trying to sound impressive, it’s probably not helping you.
Category 7: The Over-Owner
Sometimes you did some of the work—but your bullet implies you did all of it. This breaks trust immediately in interviews unless you’re a very senior leader who can plausibly claim you led the team who did all of this (in which case you’re usually already in conversation).
Examples:
“Built Claude-powered Slackbot to power all of customer success.”
“Led company-wide AI deployment.”
“Owned GPT4-based data analysis agent architecture for the entire company.”
Why it fails:
If you were part of a team, say so or clearly describe what you truly did and led. If you wrote prompts but didn’t handle retrieval, say so. There’s a huge difference between “built” and “contributed to.” Precision here doesn’t make you look smaller these days—it makes you look human and trustworthy.
Category 8: The Disconnected Win
These bullets describe an outcome that sounds good, but it’s unclear what part AI played in getting there.
Examples:
“Reduced onboarding time by 30% through AI enhancements.”
“Increased ticket resolution speed using AI-driven workflows.”
Why it fails:
What did the AI do? Summarize? Tag? Prioritize? These sound like business wins, but the AI’s role is murky. If it could’ve been done with a few scripts or macros, you haven’t made the case for AI fluency. The business case part is great, but the AI technical fluency needs to be there in 2025 as well.
Category 9: The Prompt-as-Product Illusion
This is where someone took a one-off prompt and tried to frame it like a software release.
Examples:
“Developed intelligent assistant for legal analysis using GPT-4.”
“Launched agent for automated investment recommendations.”
Why it fails:
If this was just a single prompt run through ChatGPT manually, you’re over-claiming. That’s okay—you can still talk about what you learned, how you iterated, how it failed, how you changed your assumptions. But don’t pretend a clever prompt is an end-to-end product.
In Total: What Most AI Resume Bullets Are Missing
Let’s sum up what’s often left out—and why it matters:
You don’t need all five in every bullet. But if you’re missing all of them, you’re signaling nothing.
Now that we’ve seen the bad—and the almost-good—we can shift to building smarter. In the next section, I’ll show you how to use AI itself to improve your resume: not just to rewrite it, but to reflect on what you’ve done, surface better framing, and sharpen your story.
Because AI isn’t just a thing you brag about. It’s a tool you can use—right now—to make your experience clearer, sharper, and more compelling.
How to Use AI Productively to Improve Your Resume
Don’t just talk about AI. Use it—with judgment—to clarify what you’ve done and why it matters.
Ironically, most people claiming AI experience haven’t thought to use AI on the one thing every hiring manager reads: their resume.
And those who do often use it poorly. They paste in their whole CV, ask ChatGPT to “make it better,” and get back a set of sterile, overpolished bullets that sound like a bad LinkedIn parody:
“Leveraged cross-functional synergies to architect AI-driven excellence at scale.”
This isn’t helping you. If your bullet could double as a performance review for a marketing cloud consultant, it’s too vague to mean anything.
But used well—carefully, honestly, with your hand on the wheel—AI can actually help you clarify your story, remember key outcomes, and sharpen your framing. It won’t invent the experience for you (nor should it), but it can help you describe what you did in a way that makes it legible to someone skimming 100 resumes a day.
Let’s walk through how, and yes we’ll get to AI checkers at the end!
✋ The Wrong Way to Use AI for Your Resume
Most bad uses of AI fall into one of three traps:
1. Overgeneralizing
You paste in 5 bullets, say “make these better,” and get back:
“Implemented cutting-edge AI innovations across business units.”
There’s no specificity, no tools, no verbs that tie to your actual work.
2. Overpolishing
You let GPT make the language more “professional,” and it ends up removing every ounce of personality:
“Strategically synergized LLM deployments to drive impact at scale.”
Ok this is an extreme example but you get the point. This doesn’t reflect how you think or talk—and it makes follow-up conversations awkward when your real voice shows up.
3. Overclaiming
You give AI a vague prompt like “summarize my GPT project,” and it invents outcomes you never achieved:
“Saved $1.2M in operational costs through AI automation.”
No it didn’t. And if you copy-paste this into your resume, you’re lying—and setting yourself up for a credibility wipeout in interviews.
✅ The Right Way to Use AI for Your Resume
AI isn’t here to write your story. It’s here to help you see it more clearly.
Think of GPT or Claude as a smart, fast, but slightly overeager junior editor. They’re great at reframing, but they need tight input. The more context and constraint you give, the better the results.
Here’s how to do it well.
Step 1: Start With Real Substance
Before you give anything to an AI tool, write out—honestly—what you did. No polish, no performance. Just describe it like you would in an email to a colleague:
“I built a Slackbot that summarizes internal support tickets and files them in Jira. I used GPT-4 with function calling. It was tested against a human benchmark and got about 85% overlap. It’s still in use by the onboarding team and saves them 4-6 hours a week.”
This is gold. It’s real, specific, and measurable. But it’s not yet bullet-shaped.
Also, I know you’re going to be saying “I don’t have all those details”—well put down the details you have. One way to jog your memory is actually to use a voice AI and yak into it, because talking sometimes stirs up memories we forgot.
Step 2: Give the AI Very Specific Instructions
Now prompt GPT or Claude like a thoughtful writing coach—not a résumé fluff generator.
“Turn this into a single, sharp resume bullet that includes: the tool used, the task completed, the measurable outcome, and language appropriate for a product manager or engineer. Keep it honest and grounded in what actually happened.”
Result (after a little human editing):
“Built GPT-4 Slackbot using function calling to triage support tickets into Jira; matched human categorization 85% of the time while reducing manual triage by ~5 hours/week.”
That’s a strong bullet. It names the tool. It describes what the AI did. It gives a measurable outcome. It’s not just a one-liner. And it keeps the tone professional without going overboard.
Step 3: Use AI to Benchmark Alternate Framings
Use AI’s ability to generate cheap optionality in your favor! Once you have one version, try asking:
“Can you give me 2 more variations on this bullet, one with more technical detail and one with a focus on user impact?”
Or:
“What tradeoff or constraint should I mention here to show judgment?”
AI is excellent at reframing the same content for different readers. You might use a more technical version for engineering roles, and a more impact-driven version for product or generalist roles. You still need to polish though.
Step 4: Use AI as a Reflective Tool
Beyond rewriting, you can use AI to help surface patterns in your work.
Try feeding in 5–10 bullets and asking:
“What themes show up across these projects? What does this suggest about how I approach work?”
Or:
“What types of problems am I solving repeatedly with AI tools?”
This can help you articulate a narrative in your summary, your portfolio, or your interviews—not just what you did, but how you work.
Final Note: Keep the Voice Yours
AI is a powerful mirror. But it will reflect back whatever tone you feed into it. Don’t let it erase your personality. Don’t let it polish away the detail that makes your work real.
The best AI-assisted resume bullets still sound like you. They’re just tighter. Sharper. Easier to read under pressure.
Use AI to think better—not to pretend.
Next, we’ll go role by role—Product, Engineering, Design, Ops, Generalist—and show what high-signal AI experience looks like in each lane. For every role, you’ll see:
Bad bullets
Good rewrites
Real project ideas
A short framework for surfacing your own best work
Let’s make your experience legible, grounded, and unmistakably yours.
What AI Work Looks Like on a Resume — Product Managers
For PMs, the pressure to “do something with AI” is everywhere—but the bar for what counts as signal is rising fast.
You’re not expected to be training models. You’re not expected to write retrieval pipelines. But you are expected to know how to scope an AI-powered feature, evaluate its usefulness, manage tradeoffs, and ship something users trust.
And yet, most PM resumes say almost nothing meaningful about AI. They talk in vague generalities—“evaluated AI opportunities,” “explored LLM use cases,” “partnered on AI strategy” or lately “prototyped using AI in order to…”—without ever naming the model, the friction, or the impact.
If you’re a PM trying to showcase AI fluency, here’s what it takes.
Common PM Mistakes
Let’s start with some weak bullets and why they fail. Each of these is based on real-world examples I’ve seen.
❌ “Explored generative AI use cases across product workflows.”
This is resume wallpaper. You could have built something amazing, or just read a few blog posts. There’s no task, no model, no outcome.
❌ “Collaborated with engineers to integrate LLM functionality.”
“Collaborated” is too soft here. If you wrote the spec, say so. If you handled prompt design, say so. If you ran model evals, say so.
❌ “Helped define AI roadmap for the product team.”
Which features? What friction were they solving? What model class was chosen—and why?
❌ “Worked on GPT-4 integration into product experience.”
This is almost strong—it names a tool—but it’s still too shallow. What part of the experience? What did GPT-4 do? How did it perform?
These kinds of bullets make a hiring manager think: “Okay, but what did you actually do?” Your resume becomes a credibility gap they have to manually close in the interview. Most won’t bother.
What Strong PM Bullets Look Like
Let’s now walk through strong, high-signal bullets. These are rewritten examples that:
Name the tool or model
Describe what the AI actually did
Describe what the PM actually did also!
Include an outcome (quantitative or qualitative)
Reveal judgment and taste, not just experimentation
🟢 “Prototyped GPT-4 onboarding assistant that answered first-session user questions; reduced drop-off by 22% in internal pilot.”
→ Names the task, the outcome, the audience. Also implies user testing and iteration.
🟢 “Built AI-based feedback parser using Claude 3.5 + LangChain; clustered 1,300 NPS comments into themes for roadmap planning.”
→ Shows real user-scale application, tool choice, and decision impact.
🟢 “Designed workflow to evaluate Gemini 1.5 vs Claude 3.7 in summarizing multi-party support tickets; selected Claude for tone control + accuracy.”
→ Demonstrates decision-making, testing, and real-world model judgment.
🟢 “Shipped ‘smart escalation’ feature using GPT-4 to triage customer complaints into risk tiers; reduced manual review by 60%.”
→ This shows not just use of AI, but real integration into a high-trust flow.
🟢 “Co-led rollout of GPT-powered internal spec generator; decreased PM writing time by 35% with 4.6/5 satisfaction in survey of 17 users.”
→ PM’ing a tool for PMs. Shows user value, feedback loop, outcome measurement.
🛠 Realistic, Strong Side Projects for PMs
You don’t need to launch a SaaS startup to show you can work with AI. Here are real projects that signal credibility—especially when framed correctly.
1. Internal Support Synthesizer
What it is: A GPT-powered tool that summarizes intercom or support transcripts into themes, tags, or insights.
Why it’s good: Shows integration into real ops flow. Bonus if you benchmark it against human output.
Bullet example:
“Built support summarizer with GPT-4 to extract themes from 500+ tickets/month; enabled faster sprint planning with 1-hour weekly ops sync reduction.”
2. Feature Prioritization Bot
What it is: A bot that takes in NPS comments or sales call summaries and maps them to tagged themes.
Why it’s good: Demonstrates how you reduce noise into action.
Bullet example:
“Used Claude 3.5 to tag 300+ feedback entries across 12 feature areas; output drove quarterly roadmap adjustments.”
3. UX Microcopy Rewriter
What it is: A tool that takes raw error messages, modal text, or onboarding copy and rewrites it for different tones or reading levels.
Why it’s good: Shows detail-oriented use of AI on real customer experience elements.
Bullet example:
“Used GPT-4 to generate variant onboarding copy for users with accessibility flags; 9% increase in task completion in A/B test.”
4. AI Evaluation Framework (Non-technical)
What it is: A structured doc or prototype comparing GPT, Claude, and Gemini outputs across use cases.
Why it’s good: Shows taste, tool comparison, and thoughtful test design.
Bullet example:
“Ran model evaluation of GPT-4.1, Claude 3.5, Gemini 2.5 for tone-matching in customer support; selected Claude for least hallucination and highest empathy score.”
5. AI Risk Memo
What it is: A short internal memo that defines when not to use AI in your product—and why.
Why it’s good: PMs who understand restraint are rare and valuable.
Bullet example:
“Wrote AI risk guide for PM team outlining misuse risks and fallback patterns; adopted across 3 teams building GPT-integrated flows.”
PM Resume Audit: 5-Point Checklist
When reviewing your own resume, ask:
Do I name a real model or tool? (GPT-4.1, Claude 3.5, LangChain, n8n, Bolt)
Do I describe what the AI actually did? (summarize, classify, draft, guide, extract)
Is the task grounded in product value? (retention, feedback, decision support)
Is there a measurable or observable outcome? (time saved, drop-off reduced, accuracy improved)
Do I show judgment in how I scoped or constrained it? (tradeoffs, fallback logic, user trust)
Hit 3 out of 5, you’re already stronger than most. Hit all 5? Well give yourself a slap on the back, because you’re in the 1% of resumes lol
One Last Point for PMs: It’s Okay to Build Lightly—But Speak Precisely
You don’t need a full-stack AI product to stand out. Even a small, scrappy prototype can say a lot—if you name the task, describe the tool, and show that you thought about the experience.
You are being hired for how you think. Your resume should make that thinking visible.
What AI Work Looks Like on a Resume — Engineers
If you’re a software engineer, showing AI fluency on your resume isn’t about listing tool names or model APIs. It’s about showing you understand what these tools can do, cannot do, and how to build real systems around their quirks and failure modes.
In 2023, it was enough to say you’d built something with GPT. In 2024, the bar is higher. Engineering teams are now asking:
Did you build with evals in mind?
Did you handle retries, fallbacks, caching?
Did you monitor model behavior in production—or know when to stop trusting it?
Do you know where not to use AI?
Strong AI engineering bullets don’t just say “used LangChain.” They show architecture decisions, design constraints, and measurable outcomes.
Let’s go from vague to specific.
Common Engineering Resume Mistakes
These are everywhere—and often written by people who’ve built real things but framed them too generically.
❌ “Built LLM-based chatbot using LangChain and Pinecone.”
→ This could be a clone of a tutorial. No insight into what the bot does, what content it retrieved, or how performance was evaluated.
❌ “Integrated OpenAI API into customer support system.”
→ Integrated how? To generate replies? Summarize tickets? Escalate edge cases? This is drive-by detail.
❌ “Used function calling to enhance agent abilities.”
→ What were the functions? What tasks were automated? What made this successful?
❌ “Implemented autonomous agent pipeline with tool use.”
→ This sounds like a buzzword bingo. What tools? Why an agent? What broke?
❌ “Worked on embedding pipelines for semantic search.”
→ If you didn’t specify: what content was embedded? How did you validate quality? Which model was used? It reads like boilerplate.
These bullets don’t fail because the work is bad. They fail because they don’t show ownership, specificity, or system-level thinking.
Strong Engineering Bullets (With Rewrites)
Let’s rewrite a few of the vague bullets from above—preserving the project but improving the framing.
Before: “Built LLM-based chatbot using LangChain and Pinecone.”
After:
“Built GPT-4 chatbot with LangChain + Pinecone to answer 2,000+ internal HR questions via Slack; added hybrid search and fallback-to-human trigger after 2 weeks of evals.”
Before: “Used function calling to enhance agent abilities.”
After:
“Built agent using Claude 3.5 + function calling to extract structured data from PDFs; added retry logic and hallucination guardrails using custom regex validation.”
Before: “Worked on embedding pipelines for semantic search.”
After:
“Engineered a real-time RAG pipeline for multi-document Notion workspaces using OpenAI’s text-embedding-3-small; enhanced retrieval accuracy by 31% through advanced chunking strategies and elimination of redundant vector slices.”
Before: “Integrated OpenAI API into customer support system.”
After:
“Integrated GPT-4.1 to auto-summarize customer support conversations into CRM notes; reduced manual documentation by 60% and improved QA coverage with eval benchmarks.”
Each of these shows:
Specific task
Tool and model used
Systems behavior (e.g. retries, evals, fallbacks)
A measurable or observable outcome
These are the resume bullets that make hiring managers pay attention.
Real Engineering Projects That Signal Strong AI Fluency
These are practical, useful, and can (often) be shipped in days—not weeks.
1. Hybrid RAG Chatbot (Docs + Summaries)
What it is: A chatbot that combines vector-based RAG with pre-written document summaries, choosing which source to trust based on query type.
Why it’s good: Shows retrieval fluency, fallback logic, and system-level design.
Bullet example:
“Built hybrid RAG chatbot using Gemini 2.5 + LangChain; routed between embeddings and structured summaries based on query classification, reducing hallucinations by 40%.”
2. Eval Framework for GPT Output
What it is: A test suite that compares LLM responses against human-labeled truth data across precision, tone, and hallucination likelihood.
Why it’s good: Shows rigor and maturity—AI as part of a system, not magic.
Bullet example:
“Built eval framework to test Gemini 2.5 vs GPT-4.1 response accuracy on internal Q&A bot; improved match rate from 68% to 89% through prompt tuning and content preprocessing.”
3. Function-Calling Action Agent
What it is: An agent that parses user input and uses defined functions to perform tasks (e.g., calendar booking, form-filling).
Why it’s good: Shows practical orchestration with constraints.
Bullet example:
“Built GPT-4.1 nano agent with function calling to parse Slack commands and trigger internal tooling via webhook; added retries, rate limits, and failure recovery with audit logs.”
4. Latent Chain Debugger
What it is: A CLI or small web tool that traces token-level generation from an LLM and compares hallucination rates across different prompt variants.
Why it’s good: Shows deep model awareness and debugging mindset.
Bullet example:
“Built prompt-chain debugger to test hallucination hotspots in a multi-step Llama RAG pipeline; reduced critical error rate by 22% with token-level stop loss filters.”
Engineering Resume Audit: 5-Point Checklist
When reviewing your AI-related resume bullets, ask:
Is the task specific and technical?
→ Did I describe what the AI actually did, not just that I used it?Do I name the model or tool?
→ GPT-4.1, Claude 3.5, LangChain, Pinecone, etc.Did I show system-level awareness?
→ Fallbacks, evals, retries, latency, error handling, monitoring?Is there an outcome?
→ Time saved, errors reduced, performance improved, QA increased?Did I signal judgment?
→ Did I mention constraints, failure cases, or improvements made after launch?
Again, 3 of 5 is strong. 5 of 5, and you’re a standout.
Final Thought for Engineers: Building Is Not Enough. Framing Is Everything.
If you’re already building with AI—great. But don’t assume the work speaks for itself. These systems are still opaque to many hiring managers, even at top companies. You need to translate what you built into the language of decision-making, trust, and system behavior.
Because real fluency isn’t just about using GPT. It’s about understanding how it behaves, when to trust it, and how to ship around its failure modes. That’s the engineering bar in 2024—and if your resume shows that, you’re ahead of the pack.
What AI Work Looks Like on a Resume — Designers & UX Professionals
For designers working in AI products, the canvas has changed—but the fundamentals haven’t. You’re still responsible for clarity, trust, comprehension, and affordance. The difference is that now you’re designing with and around uncertainty, and often for outputs that can’t be fully predicted.
AI experiences are non-deterministic. They break expectations. They shift cognitive load. They hallucinate. And yet: the user still needs to understand what’s happening, and feel in control.
As a result, UX in AI is not just UI polish—it’s product design at its most essential. But that doesn’t always show up well on resumes.
Here’s how to change that.
Common Design & UX Resume Mistakes
Designers often undersell their AI work. They describe it like any other feature, or worse, like a speculative concept.
❌ “Worked on AI chatbot UI.”
→ What kind of chatbot? What was the goal—speed, trust, containment? Did you handle fallback or user correction?
❌ “Designed generative UX flows for LLM integration.”
→ “Generative UX flows” doesn’t mean anything without a task or model behavior behind it.
❌ “Explored interfaces for agentic systems.”
→ Explored how? Through prototypes? User testing? Interface simulations?
❌ “Experimented with AI-enhanced onboarding designs.”
→ This could be a mood board. What made it AI-enhanced? What problem were you solving?
❌ “Built responsive interface for GPT integration.”
→ Did the user interact directly with the model? Was there context shown? What UX patterns were used to signal uncertainty?
Designers tend to de-emphasize technical detail—which is fine. But in AI products, how the system behaves is your material. If you don’t name it, the reader has no way to understand your constraints—or your skill.
Strong Design Bullets (With Rewrites)
These show you understand the experience of working with a model, not just drawing around it.
Before: “Worked on GPT chatbot interface.”
After:
“Designed GPT-4 interface with transparency cues for ambiguous queries; added editable responses + confidence visual to reduce mis-clicks by 27% in user test.”
Before: “Explored generative UX flows for onboarding.”
After:
“Prototyped three AI-assisted onboarding flows—persona-led, guided, and adaptive; guided version had 38% higher task completion in usability test.”
Before: “Designed interface for AI feature in dashboard.”
After:
“Redesigned metrics dashboard to include GPT-generated explanations; included ‘why?’ hover tool and ‘re-run’ button to increase user trust after content errors.”
Before: “Contributed to AI-enabled user journey design.”
After:
“Mapped revised IA for human+LLM workflows in support UX; separated system actions, AI suggestions, and user decisions in UI to reduce confusion and improve fallback clarity.”
Each of these does the following:
Identifies a behavior of the model (uncertainty, reactivity, tone shift)
Describes a UI or UX pattern used to handle that behavior
Includes a metric (quant or qual) from testing or feedback
Real AI Design Projects That Signal Strong UX Fluency
Here are four high-signal projects a designer can tackle alone or with a partner—and which map directly to common hiring use cases.
1. Trust UX Patterns for Hallucination Scenarios
What it is: Design UI states for an AI tool when confidence is low or wrong output is detected.
Why it’s strong: Shows maturity around model imperfection, fallback logic, and user perception.
Bullet example:
“Designed fallback UX for AI-generated insights in analytics tool; added editable output, hover-on-source, and confidence meter; reduced user drop-off after false positive by 42%.”
2. Agent Correction Feedback Loop
What it is: Design an in-context correction or feedback mechanism for an AI assistant or agent.
Why it’s strong: Shows restraint, correction pathing, and support of learning systems.
Bullet example:
“Added feedback and retry mechanism to LLM-powered assistant; 17% increase in user correction rate, 21% decrease in task abandonment.”
3. Compare-and-Choose Prompt UI
What it is: Design a UI that presents multiple AI-generated options for a given task—e.g., rephrasing a message.
Why it’s strong: Shows understanding of uncertainty, user preference, and latency-aware UI.
Bullet example:
“Prototyped 3-option generative UI for tone selection in messaging tool; ‘select and edit’ flow had 2x usage over single-shot flow in testing.”
4. Uncertainty Legend for LLM Interfaces
What it is: A small component or tooltip system that helps users interpret model behavior (e.g., hallucination risk, source reliability).
Why it’s strong: Demonstrates commitment to transparency, design ethics, and user autonomy.
Bullet example:
“Designed uncertainty key for GPT-powered legal Q&A app; 84% of users reported increased confidence understanding AI response limits in post-test survey.”
UX Resume Audit: 5-Point Checklist
Ask these questions of any AI-related bullet you write:
Did I name a specific behavior or output of the model?
→ Tone, latency, hallucination, unpredictability, sourcing, personalizationDid I design for or around that behavior?
→ Tooltips, states, fallbacks, undo flows, edit options, copy previews, etc.Did I test it or observe a reaction?
→ User test result, feedback session insight, in-app usage patternDid I define the design problem clearly?
→ “Trust drop after first hallucination,” “low comprehension on long outputs”Did I treat the model as a collaborator, not just a feature?
→ Users interacting with suggestions, rewrites, agentic actions, system decisions
Strong bullets for AI designers show interpretation of the model—not just interface placement.
Final Note for Designers: You’re Not Just Making AI Legible—You’re Making It Usable.
LLMs are unpredictable, verbose, overconfident, and often wrong. That makes UX design the first line of defense—and the clearest proof that someone understands what it means to productize AI.
If you can:
Create UI that adapts to model behavior
Design fallback paths that reduce user confusion
Build trust-enhancing explanations or previews
And test what changes when AI shows up on the screen
You’re not just an “AI UX designer.” You’re a core architect of how intelligent systems become usable software.
Make that visible in your resume. Show the friction you identified. Show the behavior you anticipated. Show the affordance you introduced. Show what changed.
What AI Work Looks Like on a Resume — Operations, BizOps, and Chiefs of Staff
If you work in operations, your job is to make things run smoother, faster, cleaner—and to do it without always needing to code, deploy, or wait for a product release.
That’s why Ops roles are quietly becoming the one of the most effective places to apply AI tools right now.
You’re close to the processes. You feel the friction. You work in tools like Google Docs, Notion, Airtable, Excel, Salesforce. You see the same weekly reporting decks, onboarding checklists, and support escalations happening over and over. That means you’re often in the best position to automate, delegate, and accelerate using AI.
The challenge is: most of this work is invisible. It doesn’t live in the product. It doesn’t get called out in a roadmap. It’s a Notion doc you quietly made smarter, or a workflow that saves your team hours each week but never makes it to the all-hands.
This section will help you name it, frame it, and give it the weight it deserves on your resume.
Common Ops Resume Mistakes
Most ops folks either undersell what they built—or describe it in vague process terms that obscure the AI part entirely.
❌ “Used AI to streamline reporting.”
→ AI did what? Generate summaries? Draft insights? Translate KPIs? This tells me nothing.
❌ “Built automation using GPT.”
→ Automation of what? Drafting emails? Filling out forms? Writing Jira tickets?
❌ “Explored AI tools to support internal workflows.”
→ Explored how? Used them? Deployed something? Measured anything?
❌ “Helped implement generative workflows for the team.”
→ “Helped implement” is too soft, and “generative workflows” is too abstract.
❌ “GPT for internal docs.”
→ This is a Slack message, not a resume bullet.
In ops, you need to do what AI tools do best: structure the unstructured. You have to make your work clear enough that someone outside your team can see the value immediately.
Strong Ops Bullets (With Rewrites)
Let’s take those same examples and reframe them with clarity, specificity, and outcomes.
Before: “Used AI to streamline reporting.”
After:
“Built Claude 3.5 automation to summarize 6 departmental reports into weekly leadership update; reduced prep time by 4 hours/week and increased on-time delivery to 100%.”
Before: “Built automation using GPT.”
After:
“Automated hiring scorecard generation using GPT-4.1 and interview notes; cut post-interview admin by 60% across 3 hiring pods.”
Before: “Helped implement generative workflows for the team.”
After:
“Scoped and deployed GPT-based task intake assistant for onboarding checklist triage; reduced ops team manual touchpoints by 40%.”
Before: “GPT for internal docs.”
After:
“Used Llama 4 + Zapier to tag, summarize, and file 100+ meeting notes/month into Notion; enabled full-text search + retrieval in <5 sec from Slackbot query.”
These examples show:
The specific tool used (Claude, GPT, Zapier, Notion)
The task performed (summarizing, triaging, generating)
The impact (time saved, accuracy gained, latency reduced)
The system context (where it lives, how it runs, who uses it)
Real Ops + AI Projects That Stand Out
These projects are the bread and butter of high-functioning ops orgs—and any of them could be a case study in AI fluency.
1. Executive Update Generator
What it is: Use an LLM to summarize multiple reports, updates, or metrics into a single status doc.
Why it’s good: Reduces manual writing. Shows internal comms mastery.
Bullet example:
“Used Claude 3.7 to compile 8 team updates into weekly exec summary; reduced turnaround time by 75% and improved alignment on team priorities.”
2. Task Intake Classifier
What it is: AI assistant to triage inbound requests (Slack, Jira, Airtable) and route or prioritize automatically.
Why it’s good: Shows orchestration, decision logic, and workflow simplification.
Bullet example:
“Built GPT-4.1 powered classifier for inbound requests; routed 90% of asks to correct team or backlog, cutting triage time by 3 hours/day.”
3. Hiring Scorecard Auto-Drafter
What it is: Use an LLM to read interview notes and generate candidate evaluation summaries.
Why it’s good: Saves time, improves consistency, adds structure to subjective input.
Bullet example:
“Automated interview scorecard generation using GPT 4.1 nano and structured note template; decreased post-interview admin time by 66% and improved submission rate by 40%.”
4. Meeting Note Synthesizer
What it is: Tool to generate summaries, action items, or decision logs from Zoom or Fireflies transcripts.
Why it’s good: Shows workflow sensitivity and multi-tool orchestration.
Bullet example:
“Used Fireflies + Gemini 2.5 to auto-generate summary + action item docs from weekly team syncs; published to Notion via Zapier, saving 2 hours/week.”
5. SOP Compliance Auditor
What it is: Use an LLM to audit SOP documents for missing fields, outdated info, or compliance gaps.
Why it’s good: Shows high-leverage safety & quality work with AI.
Bullet example:
“Built GPT-based auditor for 43 SOP documents; flagged 112 outdated policies, reduced compliance review time by 80%.”
Ops Resume Audit: 5-Point Checklist
For every bullet you write, check:
Did I name a real tool or model?
→ GPT-4.1, Claude 3.7, Zapier, Notion, Fireflies, Slackbot, Airtable, etc.Is the task something real, observable, and repeatable?
→ Summarizing, tagging, classifying, triaging, drafting, routingDid I measure time, throughput, or adoption?
→ Hours saved, coverage increased, latency reduced, accuracy improvedIs the system integrated into the way work actually happens?
→ Slack, email, dashboards, weekly meetings—not “somewhere in a prototype”Did I signal judgment or iteration?
→ “Tuned prompt over 3 weeks to reduce missed items”; “added fallback for low-confidence outputs”
If you check 3 out of 5, you’re on solid ground. If you hit 5 of 5, you’re not just AI-aware—you’re AI-operational.
Final Note for Ops Professionals: You Are AI’s Secret Weapon
You don’t need a product team. You don’t need code access. You don’t need to wait.
You have the processes. You have the pain points. You have the documentation. That means you have the leverage.
The best AI resumes from Ops folks show:
You saw the bottleneck
You prototyped a fix
You shipped a working solution
And you measured what changed
That’s it. That’s what really matters, and that’s what you’re focused on. Just judgment, ownership, and outcomes.
What AI Work Looks Like on a Resume — Generalists, Explorers & Career Switchers
This is the hardest category to write for—and arguably the most important. I’ve seen a lot of these resumes because AI is so hot right now, and I know they’re tough.
If you’re a generalist, independent contributor, or someone pivoting into AI from an unrelated role, you’re operating without a traditional lane. You don’t manage engineers. You don’t design interfaces. You’re not embedded in a product team. And yet—you’re building, learning, integrating AI into your life and work. You’ve probably explored more tools than the average PM. You’ve tried Claude 3.7, GPT-4.1, Gemini 2.5, Perplexity. Maybe you’ve built a few agents, written some automations, or started sharing experiments publicly.
But how do you make that experience legible on a resume?
How do you go from “I’ve played with a lot of AI tools” to:
“I am someone who can solve problems with AI—and I can provably explain how.”
This section is your answer, based on all the resumes I’ve seen and stared at and a lot of ping pong balls thrown at the wall.
Common Mistakes for Generalists
Most generalist AI resume bullets fall into one of three traps:
❌ “Prompt engineer.”
→ A title isn’t enough. Without a clear project, output, or purpose, this just signals hype-chasing.
❌ “Exploring AI tools for productivity.”
→ Exploration is a learning phase—not a resume entry. What did you build or improve?
❌ “Using GPT-4.1 to improve workflows.”
→ Improve how? Which workflow? For whom? What was the outcome?
❌ “Built personal AI assistant.”
→ What does it do? What was automated? Did anyone else use it? Did you measure anything?
These are red flags for hiring managers—not because you didn’t do the work, but because they don’t know what to ask next. You’ve given them ambiguity where they need clarity.
Strong Generalist Bullets (With Rewrites)
Let’s rewrite a few of those vague lines into sharp, specific bullets that emphasize task, tool, and outcome.
Before: “Built personal AI assistant.”
After:
“Built GPT-4.1-based assistant that generated personalized weekly task plans based on calendar, goals, and recent notes; used by 150+ users via shared Replit fork.”
Before: “Explored AI for workflows.”
After:
“Used Claude 3.5 + Zapier to summarize and tag 40+ weekly sales calls; exported action items into Airtable to support pipeline review meetings.”
Before: “Prompt engineer.”
After:
“Designed and iterated 12+ prompts for policy document extraction; reduced hallucination rate from 18% to 4% with regex validation and chunk tuning.”
Before: “Building in public.”
After:
“Published 10 case studies comparing GPT-4.1, Claude 3.5, Claude 3.7, and Gemini 2.5 across real-world business tasks (e.g., contract review, product spec drafting); grew audience to 8K readers.”
These examples work because they:
Highlight a real task
Name the tools and methods
Show a measurable or observable impact
Reflect the thinking behind the project, not just tool usage
Even if it’s a solo project or self-directed experiment, it becomes credible when you add structure and outcome.
Side Projects That Help Generalists Stand Out
Here are five project ideas that demonstrate clarity, curiosity, and value—without needing formal team access.
1. Multi-Model Comparison Blog / Memo
What it is: Take a real task (summarize a policy, write a job description, translate a customer email), and compare how 3 different models perform.
Why it’s good: Shows depth of experimentation and model judgment.
Bullet example:
“Compared GPT-4.1, Claude 3.5, and Gemini 2.5 on 5 HR document workflows; published benchmarking memo focused on accuracy, tone control, and error handling.”
2. Workflow Rebuilder
What it is: Take a manual process—meeting notes, feedback tagging, data entry—and rebuild it using GPT and no-code tools.
Why it’s good: Shows practical automation + cross-tool fluency.
Bullet example:
“Rebuilt weekly hiring ops workflow using GPT-4.1, Fireflies, and Airtable; reduced human review time by 70% while improving tagging consistency.”
3. Prompt Pattern Notebook
What it is: Document 10–20 prompts across different tasks, showing evolution and refinement over time.
Why it’s good: Shows critical thinking, iteration, and prompt engineering maturity.
Bullet example:
“Created prompt pattern library for customer service response tuning; optimized tone, length, and brand voice across 5 prompt iterations.”
4. Public-Facing AI Tool or Template
What it is: Launch a simple AI-based tool, prompt template, or automation on Replit, Notion, or Gumroad.
Why it’s good: Shows initiative and shipping instinct—even without formal role.
Bullet example:
“Launched GPT-4.1 nano-based resume bullet generator; reached 2,500 users and generated 12K+ AI-written bullet drafts across 6 job categories.”
5. Applied AI Use Cases in Your Domain
What it is: Apply an LLM to a task in your field—law, finance, teaching, HR—and show the outcome.
Why it’s good: Shows depth, not just breadth.
Bullet example:
“Used Gemini 2.5 to summarize legal deposition transcripts into structured outlines; reduced attorney review time by 4 hours per case.”
Generalist Resume Audit: 5-Point Checklist
Here’s how to check if your resume shows AI fluency—even outside a formal role.
Does the bullet describe a clear task?
→ Something anyone could picture or attempt themselves.Does it name the tool or model?
→ GPT-4.1, Claude 3.5, Gemini 2.5, Lovable, n8n, etc.Does it reflect iteration or insight?
→ “Refined 4x to reduce noise”; “compared 3 models on same task”Is there any kind of outcome?
→ Time saved, usage count, improvement %, audience reached, insight gainedDoes it sound like someone who builds, not just consumes?
→ Clear verbs, concrete artifacts, no vague hype
Final Note for Generalists: You’re Allowed to Be Self-Taught—You Just Can’t Be Vague
You don’t need permission to build. You don’t need a title to prototype. And you don’t need a team to think clearly about what AI can do.
What you do need is precision:
What did you try?
What did you build?
What did you learn?
What changed?
If you’re pivoting into AI, show me your experiments. Show me your reflections. Show me a system, even a small one, that got better because you applied AI to it with intent.
That’s all a great hiring manager wants to see.
Audit Your Own Resume
Want to make this real? Grab a link to an AI audit tool that you can use against your own resume here. Below is is a peek at what you get:
Or Let ChatGPT Give You a First Pass
Want help applying all this stuff? Grab a ChatGPT prompt designed to score your resume against this article here.
AI Detectors Are Silently Killing Real Candidacies. Here’s How to Stay Human.
There’s a growing risk in hiring pipelines right now—one that’s quiet, opaque, and very real: your resume might be flagged as AI-generated, even if you wrote it yourself.
This isn’t speculative. It’s happening. Companies overwhelmed by applicant volume are increasingly running tools like GPTZero on everything that comes in—cover letters, writing samples, and yes, even resumes. If your document scores “too AI-like,” many hiring teams simply move on. No email. No interview. No explanation. Just filtered out and gone.
The danger here isn’t that you cheated. It’s that you sound like you did.
If you’ve ever used ChatGPT or Claude to help polish your bullets, rewrite a summary, or clean up your language, you’re at risk. If you modeled your resume off of a friend’s that got traction. If you tightened your prose to sound “professional.” If you copied a formatting style from a blog. All of these things can inadvertently make your writing look like it came from an AI—even if every word came from your brain and your work.
And once you’re flagged, you don’t get to clarify. These detectors aren’t always accurate, but they’re being used as hard filters. That means real humans, with real experience, are being screened out by machines trained to spot other machines.
Why is this happening?
Because the market is saturated. Everyone says they’ve “used AI.” Everyone wants to look like they’re AI-native. And hiring managers are tired of reading the same vague, GPT-flavored resume bullets over and over: “Leveraged GPT-4 to improve productivity.” “Explored LLM-based workflows.” “Built AI chatbot.”
So some teams are turning to AI detectors to cut through the flood. But most of these tools aren’t subtle. They don’t assess whether you did the work. They just judge how predictable your sentence structure is. How uniform your phrasing feels. How “templated” you sound.
In other words: the more polished and safe your writing is, the more likely it is to be flagged.
That means well-meaning, high-integrity candidates are losing opportunities because they edited their resumes too well.
How AI detectors actually work
Tools like GPTZero use a few key metrics—especially perplexity and burstiness—to guess whether a piece of writing came from a human or an AI.
Perplexity measures how “surprising” a given piece of text is to the model. If your sentences are predictable, safe, and cleanly structured, your perplexity score goes down—making you look more like a bot.
Burstiness measures the variation in sentence structure and rhythm. Human writers tend to mix things up—short fragments, long clauses, abrupt shifts in tone. AI often doesn’t.
Low perplexity and low burstiness? You look like ChatGPT.
Even if you’re not.
The kicker: many of us have unknowingly internalized ChatGPT’s tone. We read so much AI-written copy that we start to mimic it—smooth, mid-length, evenly paced, overly formal, cautiously optimistic. We round off the edges of our voice without even realizing it.
And you know what? We get rewarded for that dammit. Like ChatGPT-generated content is actually really good at communicating to humans. We rate it highly. We often prefer it. So in a sense we are being trained since 2022 to adapt our own style to LLM-uniform styling. And we’re probably not talking about that impact on human language enough.
We don’t have time to dive too far in there, so we’ll leave it that that uniformity is exactly what these detectors are trained to flag.
How to tell if your resume will get flagged
The best way to find out is simple: run it through the same tools hiring managers use. GPTZero offers a free detector. There are other tools out there as well. Copy-paste your resume in, and see what comes back.
Don’t take the output as gospel. These tools are far from perfect. But if the entire document gets marked as “AI-generated,” you should assume someone reading your application might see the same—and act accordingly.
Some tips if your writing gets flagged:
Look at which sections scored lowest on perplexity or burstiness. Are your sentences too uniform in length? Are you repeating the same structure (“Built X using Y to achieve Z”) in every line?
Check your verb usage. GPT loves safe verbs: leveraged, utilized, developed, enhanced. Replace them with tactile ones: rebuilt, debugged, restructured, scrapped, retrained.
Vary your rhythm. Mix short sentences with longer, winding ones. Break up the flow with a punchy phrase or a clause that feels lived-in. Don’t be afraid of friction.
This isn’t about fooling the tool. It’s about reclaiming your voice.
The fix: Write like a human again
This sounds cheesy, but it’s real: you can’t sound like a person if you don’t read what people actually sound like, especially before ChatGPT came along in 2022. The best way to understand what AI detectors are looking for isn’t to reverse-engineer their algorithms. It’s to re-expose yourself to human text in all its messy glory.
Look I’ll be honest here: I listen to early 20th century comic novels every night these days partly for the relentless linguistic exposure to pre-GPT registers and slang. I want to retain my ear.
Go read things that were written before ChatGPT was trained.
Before everyone started optimizing for LinkedIn engagement or resume templates.
Pick up:
Old blogs from the 2000s
Product case studies from 2011
Essays and newsletters from writers who weren’t trying to sound clever
Even your own writing—from before you ever touched GPT
And you know, novels lol
You’ll notice something strange and valuable: humans are messier than machines. We have sentence fragments. We say weird things. We make analogies that don’t quite land, but still hit. Our writing has a rhythm that comes from thinking through the page—not just producing output.
That’s what AI detectors are (attempting) to listen for.
That’s what hiring managers want to feel.
This is not going to be a blog post about how terrible I think AI detectors are. I’ve made videos about that. I think they’re scammy and damaging to careers because fundamentally a word is a word and you can’t really tell where it came from. But I don’t make the rules, AI detectors exist, and we have to expect that people will use them at this point.
Practical steps to protect yourself
Here’s a simple, humane checklist to reduce the risk of getting flagged:
Don’t paste AI-generated bullets directly into your resume. Use AI to reflect or draft, but always rewrite in your own words.
Run your final draft through GPTZero before you submit. Especially for competitive roles, or if you’ve used AI help at any point.
Vary your sentence structures. Avoid stacking five bullets that all start with “Built” or “Leveraged.” Change the rhythm.
Use verbs that reflect real thinking and struggle. Not “optimized”—say what you changed. Not “executed”—say what broke and how you fixed it.
Trust that a little imperfection builds credibility. You’re not being hired for sounding like a white paper. You’re being hired because you can think clearly about your work—and express it with precision, not polish.
Final word: Don’t let your resume get mistaken for a machine
You did the work. You shipped the project. You debugged the prompt. You dealt with the hallucinations. You retried, iterated, tuned, evaluated, and shipped again.
Don’t let all of that get mistaken for something you pasted in from a tool.
Write like you mean it. Sound like you did it. And remember: in an era when everyone is claiming “AI fluency,” the most compelling signal is simple—
You still sound human.
Don’t Just Claim AI. Make It Legible.
Ok let’s wrap things up: You don’t need to be an AI expert to stand out right now. But you do need to be clear.
Clear about what you built.
Clear about how it worked.
Clear about what the AI did—and what you did to make it useful.
That’s what separates noise from signal.
We’re living through a strange moment: everyone’s talking about AI, but few people are describing their work in a way that builds trust. The hype is loud, the tooling is evolving daily, and the line between real experience and resume theater is blurrier than ever.
Which means: legibility is leverage.
If you can make your work easy to understand—if your resume tells a smart, skeptical hiring manager what you built, how it performed, and what you learned—you’re already ahead of 95% of people applying for the same roles.
The good news? That’s a skill. It’s learnable. And it gets sharper every time you write a better bullet, frame a clearer project, or explain your thinking with more precision.
So go back through your resume.
Look at every line that mentions “AI” or “GPT” or “automation.”
Ask yourself the hard questions:
Would someone outside my team know what I actually did?
Could they see what the AI was doing—and where the human work lived?
Does this bullet describe judgment, not just experimentation?
If not, fix it.
Because in the end, your resume isn’t just a list of tools. It’s a record of how you think.
And in an AI-saturated world, the people who rise are the ones who still know how to think clearly—about complexity, about ambiguity, about the weird, imperfect systems we’re all building in public.
Show that thinking.
Show your work.
And make it unmistakably yours.
Thanks for the reminder Nate. Competent but quiet doesn't work in our age, where noise pollution buffers our signal
I'm interested in learning about the AI Engineering marketplace you described.
The section on AI detectors flagging overly polished writing is wild. We've trained ourselves to sound professional by copying smooth, mid-length sentences—and now that same uniformity makes us look like bots.