跟讀練習: Write a Killer Résumé in the AI Era: 5 New Rules! - 通過影片學習英語口語

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Finding reliable resume advice online was already hard enough.

And now that employers are using AI to screen job applications, it's only fair to ask, do resumes even matter anymore?

And so I spent weeks analyzing hiring reports and academic studies from institutions like MIT and Oxford,

covering more than 4 ,000 hiring managers and nearly 2 million applications.

And bottom line up front, I found that resumes still matter, but the rules have changed.

In this video, I'll start off by sharing the five most important findings and what each one means for you.

Then I'll break down exactly what you need to do to stand out in the age of AI.

Let's get started.

Kicking things off with the five key findings.

First, 87 % of hiring managers say their AI hiring software reads simple, text -based resumes more accurately than fancy visual ones.

Second, tailored resumes saw 84 % higher interview rates, but resumes with the most keywords actually performed worse.

Third, 59 % of hiring managers see candidates using AI as a good sign,

but 28 % reject AI -heavy resumes that show little to no effort.

Fourth, resumes that quantified a candidate's impact saw 75 % higher interview rates than those that only described responsibilities.

And fifth, 60 % of hiring managers want candidates to prove their AI skills, not just list them.

I've linked the sources I went through down below, along with the prompts and bullet point examples I showcase in this video.

Now, before diving into each finding, let's quickly cover the two stages your resume has to pass in this new era.

Pass the AI and pass the human.

Stage one, pass the AI.

Basically, companies use AI to read, rank, and organize applications.

So your resume needs to be easy to process and clearly show how your experience matches the role.

Stage two, pass the human.

So if we squint really hard, there is some good news in

that only 6 % of hiring managers say AI is the decision maker when it comes to passing or rejecting candidates.

Meaning, humans still play a central role in hiring, so your resume needs to take the human reader into account as well.

Put simply, passing the AI gets your foot in the door, and passing the human gets your application moved forward.

And the five rules we're about to cover address both parts of that equation.

On that note, rule number one, make sure the AI can read your resume.

For context, while 87 % of employers say their AI hiring software prefers simple,

text -based resumes, an explosion of AI design tools actively encourages job seekers to stand out with graphics,

icons, and large images, since it's much easier to sell you a fancy resume template than to tell you boring is best.

Therefore, we should be optimizing our resumes for readability, not appearance.

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,

and high -quality images are one common reason files cross that limit.

All this to say, even if you have all the right qualifications for a role,

none of that matters if the AI software can't even read your resume properly.

So here's what to do.

First, keep the formatting intentionally boring.

Use one column and conventional headings like summary, experience, education, and skills.

And for God's sake, avoid skill bars like it's the plague.

The one caveat is to follow local norms.

For example, in China, headshots are very common on resumes and many employers even ask for a candidate's zodiac sign.

True story.

Second, export your resume as a selectable text PDF, ideally below 2 .5 megabytes.

A quick check is to open the PDF and see if you can highlight and copy the text.

If you can't, the text may be trapped inside an image.

Moving on to rule number two, make your fit obvious.

As a quick reminder, studies found that tailored resumes saw 84 % higher interview rates, but resumes with the most keywords actually performed worse,

which sounds contradictory until you learn the difference between keyword mapping and keyword stuffing.

Keyword mapping is when you use relevant keywords from the job description to describe work you've actually done, which is not new advice.

But AI makes this process much faster.

Keyword stuffing is when job seekers cram in every phrase from the job description, whether or not their experience backs it up.

And AI makes this process much faster as well.

Taking a closer look at the data, we see that across nearly 2 million applications,

untailored resumes had a 3 .09 % interview rate versus 5 .71 % for tailored resumes,

which is also where the 84 % increase from earlier comes from. In

that same study, we also see resumes with the highest keyword coverage receive 21 % fewer interviews compared to those with moderate coverage.

And so in other words, using AI to over -optimize can do more harm than good.

For example, imagine you're applying for a customer success role.

A keyword stuffed bullet might read, delivered customer support, customer service, and customer focused communication to improve the customer experience.

I wish I was kidding, but I legitimately read something like that during my time at Google.

Whereas a thoughtfully mapped bullet would say, reduce customer complaints by 31 % using AI to create an onboarding email sequence from help center documents.

So here's how to use AI to tailor your resume faster without keyword stuffing.

First, give AI the job description and your base resume, ask it to identify the problems the employer needs solved,

extract the skills and keywords that matter most, and only then ask AI to suggest stronger bullet points.

Next, review every suggestion and keep only what you can prove.

Meaning if you can't point to a real experience that supports a keyword, remove it.

So basically, the AI is smart enough to figure out whether you're just adding random keywords for no reason

or including keywords intentionally.

And that completes stage one.

The AI can now process your resume and see why you match the role.

But you still need to convince the human to pass you, which brings us to stage two.

By the way, if you want to get 1 % better at AI every week,

Link down below.

Next up, rule number three, know where AI should stop.

Remember, 59 % of hiring managers like to see candidates using AI, but 28 % reject AI -heavy resumes that show little to no effort,

and the research shows why both can be true.

A randomized experiment from MIT involving nearly nearly half a million job seekers found that using AI to help with spelling,

grammar, and wording increased the probability of getting hired by 8%.

That said, a separate experiment giving applicants access to ChatGPT made their pitches more similar

and reduced evaluator's screening by up to 9%, meaning it became harder to identify who was actually more qualified.

I mean, just imagine if you're the hiring manager and you read, spearheaded cross -functional initiatives to drive operational excellence across 20 resumes,

you'd give up as well.

Put simply, the experiments show that thoughtful AI use is fine, even encouraged, while lazy prompts like make my resume better produce the same

generic BS like results -driven professional with a proven track record of success that hiring managers are sick and tired of reading.

So how do you use AI thoughtfully?

Here's a simple two -step process.

First, focus on brain dumping your raw thoughts for every work experience you have, and just make sure you cover three things for each.

The final result, what you specifically contributed, and how you achieved it.

And don't worry if the notes are a complete mess because the goal at this step is just to capture the facts,

not make them sound good.

For example, your brain dump might look something like this.

I compared the replies from sales and support, and there were so many conflicting answers.

So I wrote a refund guide that they can both use, and once both teams used it,

duplicate tickets dropped by 50 % and the customers got consistent answers.

Cool.

Next, upload the job description along with your rough notes

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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本課的詞彙與口語要點

這節口語課以影片“Write a Killer Résumé in the AI Era: 5 New Rules!”為素材。影片中反覆出現的詞有：resume, hiring, keyword, manager, bullet。這段影片共有 163 個句子、2270 個單詞可供跟讀。講話部分時長為 13:08。說話人語速自然，每分鐘約 173 個詞，接近日常對話。只有 79% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
resume 動詞	/ˈɹɛz.ə.meɪ/	恢復 /恢復, 重新開始 /重新開始
hire 動詞	/ˈhaɪɹ/	租借, 出租
bullet 名詞	/ˈbʊlɪt/	子彈 /子彈, 槍子 /槍子
metric 形容詞	/ˈmɛt.ɹɪk/	公制
experiment 名詞	/ɪkˈspɛɹ.ə.mənt/	實驗 /實驗
employer 名詞	/ɪmˈplɔɪə/	僱主, 老闆
tailor 名詞	/ˈteɪlɚ/	裁縫 /裁縫, 裁縫師傅 /裁縫師傅
consistent 形容詞	/kənˈsɪs.tənt/	一貫的 /一貫的
format 名詞	/ˈfɔːɹ.mæt/	格式, 開本 /開本
encourage 動詞	/ɪnˈkʌɹ.ɪd͡ʒ/	鼓勵 /鼓勵, 激勵 /激勵
solve 動詞	/sɒlv/	解決 /解決
complaint 名詞	/kəmˈpleɪnt/	怨言, 抱怨
lazy 形容詞	/ˈleɪ̯zi(ː)/	懶 /懶, 懶惰 /懶惰
suggestion 名詞	/səˈd͡ʒɛst.ʃən/	建議 /建議, 提議 /提議
upload 動詞	/ˈʌpˌloʊd/	上載 /上載, 上傳 /上傳

影片中出現的短語動詞

單詞	釋義
figure out 動詞	弄清楚
break down 動詞	壞了 /壞了, 出故障
check out 動詞	退房
give up 動詞	投案, 自首
go through 動詞	通過 /通過

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	have changed · I've linked · you've actually done
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	may be trapped · being selected · is involved

需要注意的發音

說話人用了 22 次縮略和弱讀形式，例如 you're, can't, you've。請按聽到的簡短形式來說。

“th” 音: thoughtful /ˈθɔːtfəl/, strengthen /ˈstɹɛŋ(k)θən/, thoughtfully /ˈθɔtfəli/
“sh” 和 “zh” 音: suggestion /səˈd͡ʒɛst.ʃən/, intentionally /ɪnˈtɛn(t)ʃənəli/, capture /ˈkæp.(t)ʃɚ/, initiative /ɪˈnɪʃ.ə.tɪv/, polish /ˈpɑ.lɪʃ/
長單詞——注意重音位置: experiment /ɪkˈspɛɹ.ə.mənt/, intentionally /ɪnˈtɛn(t)ʃənəli/, universal /ˌjunəˈvɜɹs(ə)l/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, initiative /ɪˈnɪʃ.ə.tɪv/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: solve /sɒlv/, universal /ˌjunəˈvɜɹs(ə)l/, achievement /əˈt͡ʃiːv.mənt/, initiative /ɪˈnɪʃ.ə.tɪv/, versus /ˈvɝsəs/
詞尾子音 — 要讀清楚，後面不要加母音: bullet /ˈbʊlɪt/, metric /ˈmɛt.ɹɪk/, experiment /ɪkˈspɛɹ.ə.mənt/, consistent /kənˈsɪs.tənt/, format /ˈfɔːɹ.mæt/
/r/ 和 /l/ 的區別: universal /ˌjunəˈvɜɹs(ə)l/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, formal /ˈfɔɹməl/, reliable /ɹɪˈlaɪəbl̩/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 resume, hire, bullet 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: Write a Killer Résumé in the AI Era: 5 New Rules! - 通過影片學習英語口語

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