跟讀練習: The Only AI Tools You Need (12-Minute Guide) - 通過影片學習英語口語

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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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FOR YOU

本課的詞彙與口語要點

這節口語課以影片“The Only AI Tools You Need”為素材。影片中反覆出現的詞有：Chatgpt, Gemini, tool, model, Claude。這段影片共有 155 個句子、2173 個單詞可供跟讀。講話部分時長為 11:54。說話人語速較快，每分鐘約 183 個詞，會出現較多連讀和弱讀。只有 76% 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
everyday 形容詞	/ˈɛvɹiˌdeɪ/	日常的
upload 動詞	/ˈʌpˌloʊd/	上載 /上載, 上傳 /上傳
audio 形容詞	/ˈɔ.di.oʊ/	音訊 /音訊, 伴音
plain 名詞	/pleɪn/	平原, 平野
specialist 名詞	/ˈspɛʃəlɪst/	專家 /專家, 專門家 /專門家
instruction 名詞	/ɪnˈstɹʌkʃən/	教導 /教導
thumb 名詞	/ˈθʌm/	大拇指, 拇指
output 名詞	/ˈaʊtpʊt/	輸出 /輸出
script 名詞	/skɹɪpt/	文章
tune 名詞	/tjuːn/	曲調 /曲調, 曲子
deck 名詞	/ˈdɛk/	甲板
operator 名詞	/ˈɑpəˌɹeɪtɚ/	話務員 /話務員, 接線員 /接線員
versus	/ˈvɝsəs/	對 /對
fewer	/ˈfjuː.ɚ/	較少 /較少, 更少
slide 名詞	/ˈslaɪ̯d/	滑梯

影片中出現的短語動詞

單詞	釋義
break down 動詞	壞了 /壞了, 出故障
figure out 動詞	弄清楚
follow up 動詞	跟進 /跟進
go through 動詞	通過 /通過
point out 動詞	指, 指出
put together 動詞	拼合, 組合 /組合

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
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	was discussed · is optimized · are built
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	I've grouped · have actually become · has figured
定語從句 who / which + 從句 — 補充說明人或事物	one, which is · people who need

需要注意的發音

說話人用了 28 次縮略和弱讀形式，例如 don't, I'll, you're。請按聽到的簡短形式來說。

“th” 音: thumb /ˈθʌm/, synthesize /ˈsɪnθəsaɪz/, walkthrough /ˈwɔːk.θɹuː/
“sh” 和 “zh” 音: specialist /ˈspɛʃəlɪst/, instruction /ɪnˈstɹʌkʃən/, presentation /ˌpɹɛzn̩ˈteɪʃn̩/, publish /ˈpʌblɪʃ/, polish /ˈpɑ.lɪʃ/
長單詞——注意重音位置: operator /ˈɑpəˌɹeɪtɚ/, accuracy /ˈæk.jɚ.ə.si/, developer /dɪˈvɛləpɚ/, consistently /kənˈsɪstəntli/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: everyday /ˈɛvɹiˌdeɪ/, versus /ˈvɝsəs/, developer /dɪˈvɛləpɚ/, dive /daɪv/, solve /sɒlv/
詞尾子音 — 要讀清楚，後面不要加母音: prompt /pɹɑmpt/, upload /ˈʌpˌloʊd/, specialist /ˈspɛʃəlɪst/, output /ˈaʊtpʊt/, script /skɹɪpt/
/r/ 和 /l/ 的區別: replacement /ɹɪˈpleɪsmənt/, replicate /ˈɹɛplɪkeɪt/, reliable /ɹɪˈlaɪəbl̩/, precisely /pɹɪˈsaɪs.li/, arguably /ˈɑɹɡjuəbli/

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 everyday, upload, audio 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: The Only AI Tools You Need (12-Minute Guide) - 通過影片學習英語口語

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