쉐도잉 연습: The Only AI Tools You Need (12-Minute Guide) - 영상으로 영어 말하기 배우기

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I use around 10 AI tools for 90 % of my work, and each one excels in one specific area.

But figuring out which tool works best for what task usually takes months of trial and error.

So I'll share the one thing each tool does better than alternatives, so you walk away with a clear mental model for when to use what.

I've grouped these tools into four categories across a two -part series.

There's just too much to cover.

This video covers everyday and specialist AI, while part two covers the remaining two categories.

Let's get started.

Kicking things off with everyday AI.

These are your general purpose chatbots, ChatGPT, Gemini, and Claude.

And while they seem interchangeable, their quote -unquote motes, the specific things they do best, have actually become quite distinct.

Starting with the OG, ChatGPT.

While Gemini and Claude are arguably just as capable in raw power, ChatGPT still holds the crown in one area.

It's the most obedient model.

In plain English, ChatGPT drops fewer balls when you hand it a complex checklist.

Other models might be just as smart, but give them a lengthy set of instructions and they'll sometimes skip a step or decide they know better.

If you want proof of this, just ask each model to optimize a rough prompt for itself.

ChatGPT will generate a noticeably longer and more detailed prompt because it knows it can handle the complexity.

And if you run that optimized ChatGPT prompt through both ChatGPT and Gemini, for example, you'll notice two things.

First, ChatGPT thinks longer because it's actually checking every requirement, and it follows each instruction to the letter.

Gemini, on the other hand, often takes shortcuts.

Pro tip, I share the exact prompt optimizer in the Essential Power Prompts template linked below,

but you can test this yourself with something as simple as, optimize this prompt for ChatGPT, insert model number here.

Here's my rough prompt.

Diving into a real world example, I gave both ChatGPT and Gemini the same complex prompt, a hiring rubric with a dozen requirements.

ChatGPT delivered every single one.

Gemini's output looked right at first glance, but when I checked it against my original list, it had quietly dropped a few rules.

That's the key difference.

ChatGPT doesn't decide which instructions matter.

It just follows them.

Here's second, simpler example.

Sometimes when you explicitly tell Gemini to search the web, it just doesn't, which is wild since Gemini and Google search are both Google products, right?

Whereas with ChatGPT, when you enable web search, it performs the web search every single time.

I know this is a small example, but it's downstream from ChatGPT's core superpower.

Obedience means you can trust the behavior you asked for.

So as a rule of thumb, if a task has a lot of moving parts and getting one wrong breaks the whole thing, start with ChatGPT.

Next up, Gemini.

Where ChatGPT wins on obedience, Gemini wins on multi -modality.

In plain English, Gemini is able to process a massive amount of mixed media, video, audio, images, and text natively.

Taking a look at this table, we see that only Gemini can handle all four types of media natively.

It's able to quote -unquote listen to audio and quote -unquote watch videos, while ChatGPT and Claude use roundabout ways to access that information.

What's more, Gemini's massive 1 million token context window means it can handle large video recordings, hour -long audio recordings, full slide decks,

all together that would literally choke other models.

If you watched my latest Gemini video, you'll remember the use case where I screen recorded a messy walkthrough of myself completing a task,

uploading that video onto Gemini, and asking Gemini to turn it into a ready -to -use SOP with perfect formatting,

which is an example of Gemini ingesting video and turning it into text.

Now, let's take that a step further.

Imagine you just finished a weekly meeting.

You have a video recording of the call, a 20 -slide deck, and a photo of a messy whiteboard session.

You can upload all three and ask Gemini to summarize what was discussed, pull out the key decisions, and draft the follow -up email.

Gemini is the only tool that can synthesize all three in one go.

All that said, I have to point out that Gemini's raw reasoning capabilities sometimes feels slightly behind ChatGPT.

But when the task involves video, audio, or massive files, the trade -off is obviously worth it.

Speaking of matching the right tool to the task, today's sponsor HubSpot put together a free guide called the AI Productivity Stack that covers 50

Tools Organized by Use Case.

Here's why I like it.

While this video focuses on my personal favorites, your workflow probably needs something different.

Maybe you're in marketing and need SEO -specific tools, or you manage a team and want to build automated workflows with reliable AI.

This guide breaks down tools across business functions like research, design, and marketing, and for each tool, it shows you the best use case,

key features, pricing, and a step -by -step workflow.

What I found most useful is the decision logic at the end of each section.

So for example, The research category tells you exactly when to use perplexity versus Claude versus Humana based on what you're actually trying to do.

It's a great way to quickly understand what each tool does well.

I'll leave a link to this free guide down below.

Thank you, HubSpot, for sponsoring this video.

Rounding out the Everyday AI category, Claude.

Claude's superpower is producing higher quality first drafts than the other models.

In plain English, that means Claude's first attempt is usually closer to done.

This superpower shows up in two areas.

First, coding.

Here's a fun fact.

The latest version of Gemini beat the older version of Claude in every single benchmark score except for the coding one, which is crazy.

So obviously, Anthropic has figured out something related to coding the others haven't.

And in practice, developers universally agree that Claude writes functional code on the first try more consistently than alternatives.

Here's a real world example.

I needed to bulk export conversations from a customer service platform, but their support team said only developers could do it.

I described the problem and Claude not only gave me step -by -step instructions,

but also wrote a script in Go that worked on the first try.

I don't even know what

Go is, nor can I write code.

Another example, I asked all three models to turn a static image into an interactive chart, and Claude performed the best on the first try.

So basically anything that requires generating working code tends to favor Claude.

Pro tip, when it comes to diagrams, you can ask Claude to generate mermaid code,

which you can then paste directly into tools like Excalidraw to get clean visuals in minutes.

Area two, polishing copy.

Beyond code, Claude produces written drafts that sound human and need fewer revisions.

When you need to tighten an argument or match a specific voice, Claude just gets it.

Put simply, it's exceptionally good at style matching.

Once you share examples of your existing work, it replicates your tone almost perfectly.

When I was in corporate, I'd share previous documents so Claude could replicate that voice across presentations and performance reviews.

And now as a creator, I feed it my existing YouTube scripts to help refine new drafts.

At this point, you might be wondering how I use all three everyday AI tools together.

In a nutshell, ChatGPT or Gemini usually handles the beginning of my work.

Ideation, research, drafting the outline of a presentation.

Clawed that handles the last mile, turning that rough output into something I'm ready to present or publish.

Quick note on Grok, a lot of people ask why I don't use it.

It's actually very simple.

Grok's superpower is its direct access to the Twitter slash X firehose, right?

So it's the best option for people who need to analyze breaking news events in real time.

I never needed that.

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And as a rule of thumb, we should never use tools just for the sake of using tools.

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We should only add them to our toolkit when they solve an actual problem we have.

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Here's a quick recap of the three models and when to use them.

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And if you're wondering whether you need all three,

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the short answer is no. Most people should stick with the paid version of ChatGPT and get really good at it.

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But if you can afford multiple subscriptions and your workflow can take advantage of their individual superpowers, mix and match as needed.

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Fun fact, according to this study on open router data, models from different labs like like ChatGPT and Gemini,

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expand the pie of AI use cases precisely because they excel at different things.

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On to the second category, specialist AI.

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Before diving in, let's clear up a very common misconception.

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Tools like Perplexity are not foundational models.

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Here's a simple visual.

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OpenAI, a frontier AI lab, develops the GPT family of models.

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They also created ChatGPT as the user -friendly app layer.

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Perplexity is different.

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It fine -tunes existing foundational models for speed and accuracy, and is optimized for search.

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Their own sonar model, for example, is just a fine -tuned version of Meta's open -weight llama model.

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So on that note, perplexity's superpower is finding accurate information fast.

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In plain English, the general purpose chatpots are built for reasoning.

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You use them to help you think, brainstorm, or write a draft.

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Perplexity is built for fetching.

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You need a specific fact, and you need it now.

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Starting Starting off with a simple real -life example,

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I used Chachibiti to plan a trip to Japan with my brother because that is a creative task.

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It requires weighing trade -offs, building a narrative, and for that kind of task, I'm happy to wait while the model thinks.

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But when I need grab -and -go information, like whether a specific restaurant is foreigner -friendly because we don't speak Japanese,

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I'd want perplexity to give me accurate and update information within seconds.

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Second example, going back to how I use the three everyday AI tools, let's say Gemini or ChatGPT helps me brainstorm and structure my newsletter.

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Claude produces the final draft.

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Perplexity, in this case, is the search scalpel

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that verifies information like whether Gemini's context window is one million or two million tokens.

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In case you're curious, consumers get one million, enterprises get two million.

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Pro tip, you can use Google style search operators like site, colon reddit .com to narrow your results to a specific source.

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I have an entire video on the most useful Google search operators, so I'll link that down below.

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As a rule of thumb, think of perplexity as a replacement for Google AI mode.

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They're both for fetching information and not as a replacement for general purpose chatbots.

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Actually, let me know if you want an entire video breaking down the AI search apps like perplexity, Google search, Google AI overviews,

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Google AI mode, because they're all made for different things.

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Rounding out specialist AI, Notebook LM's superpower is that it only answers from the sources you give it, meaning it won't make things up.

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Think of it like a walled garden.

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You upload your sources and Notebook LM answers questions using only those documents.

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It can't really hallucinate because it has no outside knowledge to draw from.

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Going back to the visual around how perplexity is optimized for search, Notebook LM uses a fine -tuned Google Gemini model that minimizes hallucinations.

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For instance, when I was at Google, before publishing marketing materials, I would upload the final draft alongside the source documents and ask

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Notebook LM if the draft made any claims that contradicted the sources, and it would catch these tiny discrepancies other AI might have missed.

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I use a similar workflow today for my videos.

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Before I start filming, I upload my script

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and all my research into Notebook LM and ask it to flag anything not directly supported by the source material.

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The obvious caveat here is that the output is only as good as the sources we give it.

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So if the sources are incorrect, Notebook LM is going to be confidently incorrect.

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So as a rule of thumb, if the accuracy matters more than creativity and you have source materials to check against, use Notebook LM.

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There are a few more specialist AI tools I use, but didn't make this list because I don't use them every day.

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But to quickly go through them, Gamma for presentations, 11labs for voice cloning, Zapier and NAN for automation, and Excalitral and Napkin AI for quick visuals.

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As a reminder, I'll cover the remaining two categories in part two, so keep an eye out for that.

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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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이 레슨의 어휘와 말하기 포인트

이 말하기 레슨은 영상 “The Only AI Tools You Need”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: Chatgpt, Gemini, tool, model, Claude. 이 영상에는 섀도잉할 문장 155개와 단어 2173개가 있습니다. 말하는 구간의 길이는 11:54입니다. 화자는 분당 약 183단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 76%뿐이라 어휘가 어려운 편입니다.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
prompt 동사	/pɹɑmpt/	부추기다
upload 동사	/ˈʌpˌloʊd/	업로드하다, 올리다
audio 형용사	/ˈɔ.di.oʊ/	오디오
plain 명사	/pleɪn/	평지, 평야
specialist 명사	/ˈspɛʃəlɪst/	전문가, 숙련자
thumb 명사	/ˈθʌm/	엄지, 엄지손가락
output 명사	/ˈaʊtpʊt/	출력
script 명사	/skɹɪpt/	문서, 문장
presentation 명사	/ˌpɹɛzn̩ˈteɪʃn̩/	증정
tune 명사	/tjuːn/	곡, 곡조
deck 명사	/ˈdɛk/	갑판
operator 명사	/ˈɑpəˌɹeɪtɚ/	조작자, 오퍼레이터
versus	/ˈvɝsəs/	대
slide 명사	/ˈslaɪ̯d/	미끄럼틀, 미끄럼대
dive 동사	/daɪv/	잠수하다

영상에 나오는 구동사

단어	뜻
point out 동사	가리키다
pull out 동사	회수(回收)하다
put together 동사	조립하다

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	was discussed · is optimized · are built
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	I've grouped · have actually become · has figured
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	one, which is · people who need

주의할 발음

화자는 don't, I'll, you're 같은 축약형과 약화된 형태를 28번 사용합니다. 들리는 대로 짧게 발음하세요.

“th” 소리: thumb /ˈθʌm/, synthesize /ˈsɪnθəsaɪz/, walkthrough /ˈwɔːk.θɹuː/
“sh”와 “zh” 소리: specialist /ˈspɛʃəlɪst/, instruction /ɪnˈstɹʌkʃən/, presentation /ˌpɹɛzn̩ˈteɪʃn̩/, publish /ˈpʌblɪʃ/, polish /ˈpɑ.lɪʃ/
긴 단어 — 강세 위치에 주의: operator /ˈɑpəˌɹeɪtɚ/, accuracy /ˈæk.jɚ.ə.si/, developer /dɪˈvɛləpɚ/, consistently /kənˈsɪstəntli/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: fewer /ˈfjuː.ɚ/, fetch /fɛt͡ʃ/, format /ˈfɔːɹ.mæt/, functional /ˈfʌŋkt͡ʃənəl/, frontier /ˈfɹʌntɪə/
/v/ — ㅂ(/b/)과 구별하기: everyday /ˈɛvɹiˌdeɪ/, versus /ˈvɝsəs/, developer /dɪˈvɛləpɚ/, dive /daɪv/, solve /sɒlv/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: presentation /ˌpɹɛzn̩ˈteɪʃn̩/, whereas /(h)wɛɹˈæz/, enterprise /ˈɛntəˌpɹaɪz/, analyze /ˈæn.əˌlaɪz/, minimize /ˈmɪn.ɪˌmaɪz/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
자신의 목소리를 녹음해 원본과 비교하고, prompt, upload, audio 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: The Only AI Tools You Need (12-Minute Guide) - 영상으로 영어 말하기 배우기

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