You’ve heard the advice: use AI to update your resume. But if you’ve tried it and you’re still not getting responses — this post is for you.
In our second video, Allora Collective founder Kelly Kugler and tech careers coach Chez Jennings break down what candidates are actually doing with AI tools right now, where those strategies are falling flat, and what the most successful job seekers are doing differently.
The short version: AI is a powerful tool — but only when you are the starting point. When it’s the other way around, the problems don’t show up until you’re sitting across from an interviewer who asks about something on your resume you didn’t actually write.
What We Cover
- Why most AI-generated resumes fail — and it has nothing to do with detection
- Why specificity beats keywords every time, and what that looks like in practice
- The framework for using AI as a repurposing tool, not a content generator
- Real client examples: NotebookLM for technical interview prep; Claude for sales mock interviews
- What to do if your resume already feels generic and you’re not sure where to start
- The brain dump method — and why getting it out of your head first changes everything
The Real Problem with AI-Generated Resumes (It’s Not What You Think)
Let’s get something out of the way: this conversation isn’t about whether recruiters can detect AI. That’s not the issue.
The issue is that most candidates are using AI without giving it anything meaningful to work with. The most common scenario Chez sees on the recruiting side: someone drops their existing resume and a job description into an AI tool, asks it to “update” the resume, and submits the output. The result is a document filled with buzzwords, templated phrasing, and bullets that may technically match the job description — but don’t represent how the candidate actually works or what they’ve actually done.
Here’s the problem that shows up downstream: that resume may pass an ATS scan, but the recruiter reading it sees the same templated language they’ve seen in five other resumes that week. And if the candidate moves forward? They’re sitting in an interview being asked to speak to content they didn’t write. The disconnect is immediate.
Chez shared a specific example from her own recruiting inbox: ten resumes in a row with almost identical phrasing — bullets that were word-for-word from the job description. “It doesn’t actually tell me what the candidate can do,” she explained. “I don’t need it to match 100%. Our AI scoring tools are actually looking for the how — the project context, the qualifiers, the numbers. A bullet with no context doesn’t help the score. It just makes it obvious.”
Actionable takeaway: Before using any AI tool on your resume, ask yourself: am I giving this tool my real experience, or am I asking it to invent one for me? The output is only as good as the input.
Why Specificity Always Beats Keywords on an AI-Scored Resume
Here’s a concrete example from our conversation.
Candidate A lists “Python” in their skills section.
Candidate B writes: “Built Python automation scripts that reduced data processing time by 40%.”
Candidate B writes: “Built Python automation scripts that reduced data processing time by 40%.”
Candidate B screens in immediately — not just because of the keyword, but because the recruiter (and the AI scoring tool) can see how the skill was applied, at what level, and with what result. That’s the difference between a keyword and a story. AI tools, when prompted generically, default to keywords. The specificity has to come from you.
Actionable takeaway: For every skill or tool on your resume, ask: can I show how I used it and what it produced? If the answer is no, that’s where to focus before you touch any AI tool.
The Missing Link: Resume Work Is Interview Prep
Kelly made a point in this conversation that we think is genuinely underappreciated: the process of working through your resume — really working through it, thinking through your examples, finding your metrics, articulating your impact — is interview preparation. It’s how you realize you belong in more jobs than you thought. It’s how you build the confidence to pitch yourself at a level up.
When you hand that process off to AI, you skip something you didn’t know you needed. You end up in an interview not knowing your own resume.
“It’s like the Karate Kid movie— the wax on, wax off training that he thinks is just a chore, but then his teacher shows him it actually built his skill to block an attack. You don’t realize the process is preparing you until you’re in the room.” — Kelly Kugler, Allora Collective
Actionable takeaway: Don’t outsource the thinking. Use AI after you’ve done the work — to tighten, repurpose, and polish — not instead of it.
What Effective AI Use for Job Search Actually Looks Like
Here’s the framework that came through clearly in our conversation. AI works best as a repurposing and polishing tool — not a content generator from scratch. The starting point always needs to be you.
That might look like:
- Preparing a career story using a structure like STAR, SBI, CARL, or SAIL — Allora offers all four frameworks to clients depending on what works best for each person
- Writing bullet points about what you did, how you did it, and what the result was
- A full brain dump of your experience — unfiltered, grammar-optional — that you then hand to AI to organize and sharpen
Once you have your own content, AI becomes genuinely powerful. You can take the same career example and ask AI to repurpose it as a cover letter paragraph, a networking message, a referral request. Same story, different audiences. You don’t have to reinvent the wheel — you just need to give the tool something real to work with.
Kelly’s example prompt: “I used the STAR method to prep for this interview question. Here are my four bullet points. Can you turn this into a short paragraph for a cover letter, appropriate for [role/company]?” That’s a meaningfully different request than “here’s my resume and a job description, please update it.”
Actionable takeaway: Think of AI as your editor and repurposing engine, not your ghostwriter. Give it your stories first, then let it help you tell them better.
What’s Working for Real Clients: Two Job Search AI Success Stories
Two client examples from Chez’s coaching practice show what smart AI use actually looks like in practice:
A Data Science Professional Building a Smarter Study System
This client used NotebookLM to create podcast-style content briefing them on data science concepts, models, and technical trade-offs — content they could listen to while making dinner or going for a walk. It matched their learning style, helped them retain material they’d otherwise have to stare at on a screen, and increased their mid-to-late stage interview conversion rate. They landed multiple offers across health tech, AI, and advertising.
A Career Pivoter Going from Engineering to Tech Sales
This client had strong industry experience in software engineering but wanted to transition into tech sales — keeping the technical knowledge while moving toward the client-facing work they loved. Rather than starting from scratch, Chez and the client took existing resume bullets from cross-functional projects and adapted them to speak to the client-side impact and pre/post-sales work instead of the technical build. The client also used ChatGPT Voice and Claude to build mock interview Q&A customized to each specific role — including the faux business scenarios and pitching exercises common in sales interviews — and landed multiple offers.
Both used AI for something specific, with real context, in active service of preparation they were already engaged in. Not replacing the thinking — supporting it.
Actionable takeaway: Match the tool to the task. AI for studying, practicing, and repurposing is a genuine advantage. AI for generating content from scratch — without your input — is where it breaks down.
The Allora Search System: Job Searching Without Losing Your Mind
One more practical tool worth covering: Chez builds personalized Boolean search systems for Allora clients based on their experience, preferences, and target companies — reusable search strings that work across LinkedIn, Google site or X-ray searches across ATS’s, and job boards.
The goal is efficiency. Instead of reading job descriptions for hours, clients spend 15 to 20 focused minutes a day, move quickly on strong matches, and use the rest of their time on higher-impact activities: networking, outreach, interview prep. The last thing anyone in a job search should be doing is reading descriptions all day. It’s not efficient, and it’s demoralizing.
These searches aren’t set-and-forget. They’re iterated over time. If a search isn’t pulling the right results after a few weeks, it gets updated — new targets, broader or narrower strings, different boards. Staying agile is built into the system.
For more on advanced search techniques, search for Boolean job search strategy on the Allora blog and you’ll find blogs from Chez and Kelly with templates you can use immediately.
Actionable takeaway: Stop spending hours reading job postings. Build a search system, test it, and iterate. Use the time you save on the things that actually move the needle.
If Your AI-Generated Resume Feels Generic Right Now — Start Here
If you’re reading this and your resume was largely AI-generated, here’s where Kelly recommends starting: create a “long resume.” A private document that’s your full career content resource. Add real metrics, specific context, the how behind your accomplishments, stakeholder titles, project names. Write it in your own words first. Don’t edit while you’re thinking. Get it out. She calls it a ‘brain dump’ or ‘word vomit’.
Once you have your notes, then use AI to help you organize it, tighten it, and pull from it for specific applications. That document becomes your source of truth for everything — resumes, cover letters, networking messages, interview prep. All repurposed, all starting from the same real content.
And if writing blocks you? Voice memo it. Walk and talk about it. Text yourself. Kelly keeps a running Notes app of loose ideas that later become content, curriculum, resume bullets and interview stories. For clients who feel blocked when writing, she’ll have them speak freely in session while she creates the notes. Some of the best content comes out when you’re not sitting in front of a blank document trying to sound impressive.
“AI is most powerful as a repurposing and polishing tool — not a content generator from scratch. When the content starts with the candidate, it stays personal and in their voice. When it doesn’t, it falls flat — whether they’re trying to write about it or speak about it in an interview.” — Kelly Kugler, Allora Collective
The TLDR
- AI is a tool, not a replacement for doing the work
- Quality over quantity — mass-applying with AI-generated resumes is playing the lottery
- The resume development process is interview prep — don’t skip it
- Used right, AI can be a serious advantage. Used wrong, the problems don’t show up until you’re in the room
Watch the Full Conversation
Watch the full 34-minute video to hear more examples, get specific prompting strategies, and learn what to do when you feel completely stuck — including how to use AI to figure out what you don’t want, which turns out to be just as clarifying.
Video Timestamps
0:04 – Intro: flipping from the employer side to the candidate side
1:06 – What candidates are actually doing with AI tools
2:45 – The key distinction: when content starts with YOU vs. the tool
4:10 – The STAR method as a starting point + same story, different audiences
6:30 – How to use AI to repurpose your own content (example prompt)
8:13 – Client story: pivoting from software engineering to tech sales using AI
9:42 – Why specificity beats keywords: the Python example
12:00 – The real problem with AI-generated resumes (it’s not detection)
13:25 – “Wax on, wax off” — why skipping the resume process costs you in interviews
15:26 – Real example: Using NotebookLM for data science interview prep
17:08 – Real example: ChatGPT Voice + Claude for sales mock interviews
18:30 – The Allora Search System: Boolean strings, job boards, staying agile
22:41 – TLDR: AI is a tool, not a replacement for doing the work
23:28 – What to do if your resume feels generic right now
25:00 – The brain dump method: get it out of your head first, edit after
28:29 – Flow state, time away from your desk, and career clarity
30:35 – Using AI to figure out what you DON’T want (and why it helps)
Want Personalized Support with Your Job Search?
Whether you’re actively searching, planning a career transition, or trying to figure out why your AI-assisted applications aren’t converting — Allora Collective offers personalized 1:1 career coaching with real coaches who stay with you through the whole process.
