Pratique du Shadowing: Write a Killer Résumé in the AI Era: 5 New Rules! - Apprendre l'anglais à l'oral avec la vidéo
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Finding reliable resume advice online was already hard enough.
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And now that employers are using AI to screen job applications, it's only fair to ask, do resumes even matter anymore?
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And so I spent weeks analyzing hiring reports and academic studies from institutions like MIT and Oxford,
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covering more than 4 ,000 hiring managers and nearly 2 million applications.
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And bottom line up front, I found that resumes still matter, but the rules have changed.
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In this video, I'll start off by sharing the five most important findings and what each one means for you.
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Then I'll break down exactly what you need to do to stand out in the age of AI.
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Let's get started.
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Kicking things off with the five key findings.
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First, 87 % of hiring managers say their AI hiring software reads simple, text -based resumes more accurately than fancy visual ones.
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Second, tailored resumes saw 84 % higher interview rates, but resumes with the most keywords actually performed worse.
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Third, 59 % of hiring managers see candidates using AI as a good sign,
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but 28 % reject AI -heavy resumes that show little to no effort.
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Fourth, resumes that quantified a candidate's impact saw 75 % higher interview rates than those that only described responsibilities.
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And fifth, 60 % of hiring managers want candidates to prove their AI skills, not just list them.
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I've linked the sources I went through down below, along with the prompts and bullet point examples I showcase in this video.
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Now, before diving into each finding, let's quickly cover the two stages your resume has to pass in this new era.
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Pass the AI and pass the human.
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Stage one, pass the AI.
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Basically, companies use AI to read, rank, and organize applications.
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So your resume needs to be easy to process and clearly show how your experience matches the role.
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Stage two, pass the human.
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So if we squint really hard, there is some good news in
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that only 6 % of hiring managers say AI is the decision maker when it comes to passing or rejecting candidates.
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Meaning, humans still play a central role in hiring, so your resume needs to take the human reader into account as well.
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Put simply, passing the AI gets your foot in the door, and passing the human gets your application moved forward.
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And the five rules we're about to cover address both parts of that equation.
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On that note, rule number one, make sure the AI can read your resume.
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For context, while 87 % of employers say their AI hiring software prefers simple,
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text -based resumes, an explosion of AI design tools actively encourages job seekers to stand out with graphics,
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icons, and large images, since it's much easier to sell you a fancy resume template than to tell you boring is best.
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Therefore, we should be optimizing our resumes for readability, not appearance.
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And although there is no universal file size limit across every hiring system, some tools explicitly state they cannot parse resumes larger than 2 .5 megabytes,
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and high -quality images are one common reason files cross that limit.
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All this to say, even if you have all the right qualifications for a role,
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none of that matters if the AI software can't even read your resume properly.
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So here's what to do.
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First, keep the formatting intentionally boring.
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Use one column and conventional headings like summary, experience, education, and skills.
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And for God's sake, avoid skill bars like it's the plague.
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The one caveat is to follow local norms.
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For example, in China, headshots are very common on resumes and many employers even ask for a candidate's zodiac sign.
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True story.
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Second, export your resume as a selectable text PDF, ideally below 2 .5 megabytes.
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A quick check is to open the PDF and see if you can highlight and copy the text.
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If you can't, the text may be trapped inside an image.
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Moving on to rule number two, make your fit obvious.
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As a quick reminder, studies found that tailored resumes saw 84 % higher interview rates, but resumes with the most keywords actually performed worse,
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which sounds contradictory until you learn the difference between keyword mapping and keyword stuffing.
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Keyword mapping is when you use relevant keywords from the job description to describe work you've actually done, which is not new advice.
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But AI makes this process much faster.
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Keyword stuffing is when job seekers cram in every phrase from the job description, whether or not their experience backs it up.
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And AI makes this process much faster as well.
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Taking a closer look at the data, we see that across nearly 2 million applications,
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untailored resumes had a 3 .09 % interview rate versus 5 .71 % for tailored resumes,
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which is also where the 84 % increase from earlier comes from. In
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that same study, we also see resumes with the highest keyword coverage receive 21 % fewer interviews compared to those with moderate coverage.
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And so in other words, using AI to over -optimize can do more harm than good.
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For example, imagine you're applying for a customer success role.
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A keyword stuffed bullet might read, delivered customer support, customer service, and customer focused communication to improve the customer experience.
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I wish I was kidding, but I legitimately read something like that during my time at Google.
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Whereas a thoughtfully mapped bullet would say, reduce customer complaints by 31 % using AI to create an onboarding email sequence from help center documents.
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So here's how to use AI to tailor your resume faster without keyword stuffing.
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First, give AI the job description and your base resume, ask it to identify the problems the employer needs solved,
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extract the skills and keywords that matter most, and only then ask AI to suggest stronger bullet points.
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Next, review every suggestion and keep only what you can prove.
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Meaning if you can't point to a real experience that supports a keyword, remove it.
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So basically, the AI is smart enough to figure out whether you're just adding random keywords for no reason
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or including keywords intentionally.
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And that completes stage one.
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The AI can now process your resume and see why you match the role.
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But you still need to convince the human to pass you, which brings us to stage two.
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By the way, if you want to get 1 % better at AI every week,
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sign up for my free newsletter newsletter to receive one insanely actionable AI tip you can apply in under two minutes.
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Link down below.
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Next up, rule number three, know where AI should stop.
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Remember, 59 % of hiring managers like to see candidates using AI, but 28 % reject AI -heavy resumes that show little to no effort,
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and the research shows why both can be true.
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A randomized experiment from MIT involving nearly nearly half a million job seekers found that using AI to help with spelling,
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grammar, and wording increased the probability of getting hired by 8%.
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That said, a separate experiment giving applicants access to ChatGPT made their pitches more similar
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and reduced evaluator's screening by up to 9%, meaning it became harder to identify who was actually more qualified.
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I mean, just imagine if you're the hiring manager and you read, spearheaded cross -functional initiatives to drive operational excellence across 20 resumes,
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you'd give up as well.
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Put simply, the experiments show that thoughtful AI use is fine, even encouraged, while lazy prompts like make my resume better produce the same
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generic BS like results -driven professional with a proven track record of success that hiring managers are sick and tired of reading.
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So how do you use AI thoughtfully?
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Here's a simple two -step process.
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First, focus on brain dumping your raw thoughts for every work experience you have, and just make sure you cover three things for each.
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The final result, what you specifically contributed, and how you achieved it.
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And don't worry if the notes are a complete mess because the goal at this step is just to capture the facts,
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not make them sound good.
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For example, your brain dump might look something like this.
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I compared the replies from sales and support, and there were so many conflicting answers.
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So I wrote a refund guide that they can both use, and once both teams used it,
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duplicate tickets dropped by 50 % and the customers got consistent answers.
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Cool.
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Next, upload the job description along with your rough notes
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and ask AI to strengthen the wording for each bullet without changing any of the underlying facts.
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Then review every suggestion and only keep the language you could explain naturally in an interview.
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So your final bullet point might look something like this.
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Decrease duplicate tickets by 50 % by creating a shared refund guide for sales and support so customers received consistent answers.
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To quickly recap, hiring managers and recruiters read enough resumes to spot the difference between thoughtful AI use and lazy bullshit.
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So do the hard thinking first, then use AI to make that thinking more clear and concise.
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Up next, rule number four, prove your impact with numbers.
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In a nutshell, the research found resumes
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that quantified a candidate's impact saw 75 % higher interview rates than those that only described responsibilities.
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And this makes perfect sense since two people can have the exact same responsibilities and yet produce completely different results.
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Just imagine a hiring manager reading, responsible for planning social media campaigns 10 times in a row.
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And the 11th resume says, drove a 30 % year -over -year increase in short -form views over two months.
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It's not hard to guess which candidate stands out.
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Now, none of this is new.
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Adding metrics to your resume has been best practice for decades.
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What is new is that AI makes the two hardest parts much easier.
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Figuring out which metrics actually matter and turning your rough notes into polished bullet points using a proven framework.
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Here's what that means in practice.
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First, upload your resume and tell AI, help me identify the most relevant metrics for each experience on my resume.
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Ask me clarifying questions if needed.
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This matters because a lot of candidates assume revenue is the only metric that counts when there are many, many other ways to measure impact,
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such as time saved, speed, scale, accuracy, et cetera.
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Next, after you've supplied numbers, you can verify, tell the AI, rewrite each bullet using Google's XYZ formula.
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Accomplish X as measured by Y by doing Z.
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Do not add or change any facts or numbers.
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Building on the earlier example, a revised bullet might look something like this.
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Drove a 30 % year -on -year increase in short -form views within two months by testing different video openings
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and repeating the best performing formats.
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So the takeaway here is simple.
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Job seekers used to skip metrics because they either didn't know what to measure
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or didn't have the time to rewrite every bullet.
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But now AI removes both of those barriers, turning one of the highest impact resume improvements into one of the easiest.
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And finally, rule number five, prove your AI skills.
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So it shouldn't be surprising
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that 60 % of hiring managers want candidates to prove their AI skills and not just list them, right?
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And when we look at how they want that proof, 26 % prefer an interview or task, 19 % want work examples or outcomes,
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and 15 % look for certifications or courses.
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On top of that, an experiment from Oxford University found
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that adding role -relevant AI skills increased a candidate's chance of being selected for an interview by up to 15 percentage points.
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Put simply, writing ChatGPT in your skills section is not enough.
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You need to actually demonstrate how you use AI to solve a problem for the role you're applying for.
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So here's what to do.
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First, whenever possible, make the first bullet under each of your experiences an AI achievement that is directly relevant to the job description.
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For example, cut weekly customer feedback reporting from two hours to 30 minutes by using
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Cloud Code to build a shared database with recurring issues.
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If you don't have a relevant workplace example yet,
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complete a small project for the job you want and list it under a separate project section in your resume.
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For example, created a customer insights report from a hundred public product reviews by using ChatGPT work to identify recurring complaints,
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checking the themes and recommending three improvements.
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Next, make your proof easy for the hiring manager to inspect by linking directly to your portfolio.
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Here's a real example for my friend Zara Zhang who publishes all her AI projects on GitHub.
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So any hiring manager can immediately see what she built and how she used AI.
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And if you have no idea what GitHub means, no problem.
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Simply create a Google Doc and under each project, explain the situation, how and why you used AI and what changed as a result.
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Pro tip, unless you're a fresh graduate, put your experience above your education since 86 % of hiring managers value relevant work experience over formal education.
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All right, let's quickly recap by closing the loop on the two stages.
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Rules one and two help you pass the AI by making your resume readable and connecting your experience to the role.
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Rules three, four, and five help you pass the human by using AI thoughtfully, quantifying your impact and proving your AI skills with real work.
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Now, was every finding from this video new and mind -blowing?
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No, not really.
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But hopefully, now that you've seen the data behind the advice, you can follow them with more confidence,
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especially now that AI is involved on both sides of hiring.
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If you enjoyed this, you might wanna check out my AI playlist next.
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See you on the next video.
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In the meantime, have a great one.
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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale s’appuie sur la vidéo « Write a Killer Résumé in the AI Era: 5 New Rules! ». Les mots qui reviennent le plus souvent : resume, hiring, keyword, manager, bullet. Cette vidéo contient 163 phrases et 2270 mots à répéter en shadowing. La partie parlée dure 13:08. Le locuteur parle à un rythme naturel d’environ 173 mots par minute, proche d’une conversation courante. Seuls 79 % des mots font partie des 3 000 mots les plus courants en anglais, le vocabulaire est donc exigeant.
Vocabulaire clé de cette vidéo
15 mots de la vidéo à retenir, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| resume verbe | /ˈɹɛz.ə.meɪ/ | recommencer, reprendre |
| hire verbe | /ˈhaɪɹ/ | louer |
| bullet nom | /ˈbʊlɪt/ | balle, projectile |
| metric adjectif | /ˈmɛt.ɹɪk/ | métrique |
| experiment nom | /ɪkˈspɛɹ.ə.mənt/ | expérience |
| employer nom | /ɪmˈplɔɪə/ | employeur, employeuse |
| tailor nom | /ˈteɪlɚ/ | tailleur, tailleuse |
| consistent adjectif | /kənˈsɪs.tənt/ | constant |
| format nom | /ˈfɔːɹ.mæt/ | format |
| encourage verbe | /ɪnˈkʌɹ.ɪd͡ʒ/ | encourager |
| solve verbe | /sɒlv/ | résoudre, régler |
| complaint nom | /kəmˈpleɪnt/ | plainte, réclamation |
| lazy adjectif | /ˈleɪ̯zi(ː)/ | paresseux, fainéant |
| suggestion nom | /səˈd͡ʒɛst.ʃən/ | suggestion, proposition |
| dump verbe | /dʌmp/ | se débarrasser de |
Les verbes à particule que vous entendrez
| Mot | Sens |
|---|---|
| figure out verbe | cerner |
| break down verbe | tomber en panne |
| check out verbe | mater, regarder |
| give up verbe | se rendre |
| go through verbe | traverser |
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | have changed · I've linked · you've actually done |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | may be trapped · being selected · is involved |
Prononciation à surveiller
Le locuteur utilise 22 contractions et formes réduites, comme you're, can't, you've. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « th »: thoughtful /ˈθɔːtfəl/, strengthen /ˈstɹɛŋ(k)θən/, thoughtfully /ˈθɔtfəli/
- Les sons « sh » et « zh »: suggestion /səˈd͡ʒɛst.ʃən/, intentionally /ɪnˈtɛn(t)ʃənəli/, capture /ˈkæp.(t)ʃɚ/, initiative /ɪˈnɪʃ.ə.tɪv/, polish /ˈpɑ.lɪʃ/
- Mots longs — placez bien l’accent: experiment /ɪkˈspɛɹ.ə.mənt/, intentionally /ɪnˈtɛn(t)ʃənəli/, universal /ˌjunəˈvɜɹs(ə)l/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, initiative /ɪˈnɪʃ.ə.tɪv/
Les sons difficiles pour les francophones :
- /h/ — il se souffle, il n’est pas muet: hire /ˈhaɪɹ/, heading /ˈhɛdɪŋ/, harm /hɑːm/, whereas /(h)wɛɹˈæz/, highlight /ˈhaɪˌlaɪt/
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: encourage /ɪnˈkʌɹ.ɪd͡ʒ/, suggestion /səˈd͡ʒɛst.ʃən/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, percentage /pə(ɹ)ˈsɛntɪd͡ʒ/, pitch /pɪt͡ʃ/
- /r/ anglais — langue recourbée, sans frotter la gorge: resume /ˈɹɛz.ə.meɪ/, hire /ˈhaɪɹ/, metric /ˈmɛt.ɹɪk/, experiment /ɪkˈspɛɹ.ə.mənt/, format /ˈfɔːɹ.mæt/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme resume, hire, bullet.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.