Shadowing Practice: AI Agents, Clearly Explained - Learn English Speaking with Video
Ders oluşturuluyor...
1
AI AI AI AI AI AI - You know, more agentic.
2
- Agentic capabilities.
3
- AI agent.
4
- Agents.
5
- Agentic workflows.
6
- Agents.
7
- Agents.
8
- Agents.
9
- Agents.
10
- Agents.
11
- Agents.
12
- All right, most explanations of AI agents is either too technical or too basic.
13
This video is meant for people like myself.
14
You have zero technical background, but you use AI tools regularly,
15
and you wanna learn just enough about AI agents In this video, we'll follow a simple one,
16
two, three, learning path by building on concepts you already understand, like ChatGPT, and then moving on to AI workflows,
17
and then finally, AI agents, all the while using examples you will actually encounter in real life.
18
And believe me, I wanna tell you, those intimidating terms you see everywhere, like RAG, RAG, or React, they're a lot simpler than you think.
19
Let's get started.
20
Kicking things off at level one, large language models.
21
Popular AI chatbots like ChatGPT, Google Gemini, and Cloud are applications built on top of large language models, LLMs.
22
And they're fantastic at generating and editing text.
23
Here's a simple visualization.
24
"You, the human, provides an input, and the LLM produces an output based on its training data." For example,
25
if I were to ask ChachiPT to draft an email requesting a coffee chat, my prompt is the input,
26
and the resulting email that's way more polite than I would ever be in real life is the output.
27
So far, so good, right?
28
Simple stuff.
29
But what if I asked ChatGPT when?
30
"my next coffee chat is." Even without seeing the response, both you and I know ChatGPT is gonna fail because it doesn't know that information.
31
It doesn't have access to my calendar.
32
This highlights two key traits of large language models.
33
First, despite being trained on vast amounts of data, they have limited knowledge of proprietary information, like our personal information or internal company data.
34
Second, LLMs are passive.
35
Keep these two traits in mind moving forward.
36
Moving to level two, AI workflows.
37
Let's build on our example.
38
What if I, a human, told the LLM, every time I ask about a personal event,
39
perform a search query and fetch data from my Google Calendar before providing a response.
40
With this logic implemented, the next time I ask, when is my coffee chat with Elon Husky,
41
I'll get the correct answer because the LLM will now first go into my Google Calendar to find that information.
42
Here's where it gets tricky.
43
What if my next follow -up question is, what will the weather be like that day?
44
The LLM will now fail at answering the query
45
because the path we told the LLM to follow is to always search my Google Calendar, which does not have information about the weather.
46
This is a fundamental trait of AI workflows.
47
They can only follow predefined paths set by humans.
48
And if you want to get further.
49
What if I added more steps into the workflow by allowing the LM to access the weather via an API, and then just for fun, use a text to audio model to speak the answer.
50
The weather forecast for seeing Elon Husky is sunny with a chance of being a good boy.
51
Here's the thing.
52
No matter how many steps we add, this is still just an AI workflow.
53
Even if there were hundreds or thousands of steps, if a human is the decision maker, there is no AI agent involvement.
54
Pro tip, retrieval augmented generation or RAG, is a fancy term that's thrown around a lot.
55
In simple terms, RAG is a process that helps AI models look things up before they answer, like accessing my calendar or the weather service.
56
Essentially, RAG is just a type of AI workflow.
57
By the way, I have a free AI toolkit
58
that cuts through the noise and helps you master essential AI tools and workflows.
59
I'll leave a link to that down below.
60
Here's a real world example.
61
Following Helena Liu's amazing tutorial, I created a simple AI workflow using make .com.
62
Here you can see that first, I'm using Google Sheets to do something.
63
Specifically, I'm compiling links to news articles in a Google Sheet, and this is that Google Sheet.
64
Second, I'm using perplexity to summarize those news articles.
65
Then, using Claude and using a prompt that I wrote, I'm asking Claude to draft a LinkedIn and Instagram post.
66
Finally, I can schedule this to run automatically every day at 8 :00 AM.
67
As you can see, this is an AI workflow because it follows a predefined path set by me.
68
Step one, you do this.
69
Step two, you do this.
70
Step three, you do this.
71
And finally, remember to run daily at 8 :00 AM.
72
One last thing, if I test this workflow and I don't like the final outputs, of the LinkedIn post for example,
73
as you can see right here, it's not funny enough.
74
And I'm naturally hilarious, right?
75
I'd have to manually go back and rewrite the prompt for Claude.
76
Okay.
77
And this trial and error iteration is currently being done by me, a human.
78
So keep that in mind moving forward.
79
All right, level three AI agents.
80
Continuing the make .com example, let's break down what I've been doing so far as the human decision maker.
81
With the goal of creating social media posts based off of news articles, I need to do two things.
82
First, reason or think about the best approach.
83
I need to first compile the news articles, then summarize them, then write the final posts.
84
Second, take action using tools.
85
I need to find and link to those "use perplexity" for real -time summarization, and then "clawed" for copywriting.
86
So, and this is the most important sentence in this entire video, the one massive change that has to happen in order for this AI workflow to become an AI agent,
87
is for me, the human decision maker, to be replaced by an LLM.
88
In other words, the AI agent must reason.
89
What's the most efficient way to compile these news articles?
90
Should I copy and paste each article into a Word document?
91
to compile links to those articles, and then use another tool to fetch the data.
92
Yes, that makes more sense.
93
The AI agent must act, aka do things via tools.
94
Should I use Microsoft Word to compile links?
95
No, inserting links directly into rows is way more efficient.
96
What about Excel?
97
No, the user has already connected their Google account with make .com, so Google Sheets is a better option.
98
Pro tip: because of this,
99
the most common configuration Agents must reason and act, so re -act.
100
Sounds simple once we break it down, right?
101
A third key trait of AI agents is their ability to iterate.
102
Remember when I had to manually rewrite the prompt to make the LinkedIn post funnier?
103
I, the human, probably need to repeat this iterative process a few times to get something I'm happy with, right?
104
An AI agent will be able to do the same thing autonomously.
105
In our example, the AI agent would autonomously add in another LLM to critique its own output.
106
Okay, I've drafted V1 of a LinkedIn post.
107
How do I make sure it's good?
108
Oh, I know.
109
I'll add another step where an LLM will critique the post based on LinkedIn post practices.
110
And let's repeat this until the best practices criteria are all met.
111
And after a few cycles of that, we have the final output.
112
That was a hypothetical example.
113
So let's move on to a real world AI agent example.
114
Andrew Ng is a preeminent figure in AI and he created this demo website that illustrates how an AI agent works.
115
I'll link the full video down below, but when I search for a keyword like skier, Enter.
116
The AI vision agent in the background is first reasoning
117
What a skier looks like a person on skis going really fast in snow for example, right?
118
I'm not sure and then it's acting by I looking at clips in video footage,
119
trying to identify what it thinks a skier is,
120
indexing that clip and then returning that clip to us.
121
Although this might not feel impressive, remember that an AI agent did all that instead of a human reviewing the footage beforehand,
122
manually identifying the skier and adding tags like skier, mountain, ski, snow.
123
The programming is obviously a lot more technical and complicated than what we see in the front end, but That's the point of this demo, right?
124
The average user like myself, wants a simple app that just works without me having to understand what's going on in the backend.
125
Speaking of examples, I'm also building my very own basic AI agent using NAN.
126
So let me know in the comments what type of AI agent you'd like me to make a tutorial on next.
127
To wrap up, here's a simplified visualization of the three levels we covered today.
128
Level one, we provide an input and the LM responds with an output easy.
129
Level two, for AI workflows, we provide an input and tell the LLM to follow a predefined path that may involve in retrieving information from external tools.
130
The key trait here is that the human programs a path for the LLMs to follow.
131
Level three, the AI agent receives a goal, and the LLM performs reasoning to determine how best to achieve the goal,
132
takes action using tools to produce an interim result, observes that interim result, and decides whether iterations are required,
133
and produces a final output that achieves the initial goal.
134
The key trait here is that the LLM is the decision maker in the workflow.
135
If you found this helpful, you might want to learn how to build a prompts database in Notion.
136
- Bye.
137
Have a great one.
📺 Aynı Kanal
B11
How to Get Started with Notion (without losing your mind)
American AccentBusiness EnglishUniversity StudentsWorking Adults
B28
Claude Design is Insanely Easy (even for beginners)
American AccentBusiness EnglishUniversity StudentsWorking Adults
B27
Why Are You a Good Fit for this Role - How to Answer
American AccentBusiness EnglishInterview EnglishUniversity Students
B25
Tell Me About Yourself (The BEST Way to Answer this Interview Question)
American AccentBusiness EnglishInterview EnglishUniversity Students
B23
How I'd Learn AI From Scratch in 2026 (skip the useless 80%)
American AccentBusiness EnglishUniversity StudentsWorking Adults
B21
What is Your Biggest Weakness? (Ace this Interview Question!)
American AccentBusiness EnglishInterview EnglishUniversity Students
B22
Learn 95% of ChatGPT Work in Under 20 Minutes
American AccentBusiness EnglishUniversity StudentsWorking Adults
B21
Tell Me About Yourself - Structure a Strong Answer
American AccentBusiness EnglishInterview EnglishUniversity Students
C11
Google’s AI Course for Beginners (in 10 minutes)!
American AccentBusiness EnglishUniversity StudentsWorking Adults
B27 Job Interview Questions to Prepare For!
American AccentBusiness EnglishInterview EnglishUniversity Students
B2Tell me a time You Handled a Difficult Situation (Interview Question)
American AccentBusiness EnglishInterview EnglishUniversity Students
✨ Video Öner
B23
106 cümleler
Most devs don't understand what agents are
British AccentBusiness EnglishUniversity StudentsWorking Adults
B21
125 cümleler
Why Chatbots Understand You (And Why They Don't) | NLP Explained
Business EnglishUniversity StudentsWorking Adults
B23
23 cümleler
What is an AI Token? | LLM Tokens explained in 2 minutes!
Business EnglishUniversity StudentsWorking Adults
B28
59 cümleler
An ex-OpenAI researcher just deleted language from the LLM...
American AccentBusiness EnglishUniversity StudentsWorking Adults
![Cloud Providers are changing RAPIDLY [REUPLOAD]](https://img.youtube.com/vi/UVz8_JljpGQ/mqdefault.jpg)
B21
55 cümleler
Cloud Providers are changing RAPIDLY [REUPLOAD]
Business EnglishUniversity StudentsWorking Adults
B24
166 cümleler
Can This Robot Be My Personal Assistant
Business EnglishDaily ConversationHigh School Students
B21
307 cümleler
The New AI prompting Method Everyone Is Talking About: Loops (And How To Use Them)
Business EnglishUniversity StudentsWorking Adults
B21
76 cümleler
Nvidia's New Laptops - RTX Spark
Business EnglishUniversity StudentsWorking Adults
B223
26 cümleler
Advantages and Disadvantages of AI
IELTS PrepBusiness EnglishUniversity Students
B21
282 cümleler
What Stanford students actually use ChatGPT for
Business EnglishUniversity StudentsWorking Adults
Bu dersin kelimeleri ve konuşma notları
Bu videoda gölgeleme çalışması için 137 cümle ve 1642 kelime var. Konuşma bölümü 10:04 sürüyor. Konuşmacı dakikada yaklaşık 163 kelimeyle, günlük konuşmaya yakın doğal bir hızda konuşuyor. Kelimelerin yalnızca %77’i İngilizcede en sık kullanılan 3.000 kelime arasında, bu yüzden kelime dağarcığı zorlayıcı.
Bu videodaki önemli kelimeler
Videodan daha az kullanılan 15 kelime, telaffuzu ve anlamıyla:
- output /ˈaʊtpʊt/ (isim) — çıktı. Production; quantity produced, created, or completed.
- trait /tɹeɪt/ (isim) — özellik, vasıf. An identifying characteristic, habit or trend.
- calendar /ˈkæl.ən.də/ (isim) — takvim. Any system by which time is divided into days, weeks, months, and years.
- maker /ˈmeɪk.ɚ/ (isim) — yapan, yapıcı. Someone who makes; a person or thing that makes or produces something.
- clip /klɪp/ (isim) — kıskaç. Something which clips or grasps; a device for attaching one object to another.
- efficient /ɪˈfɪʃənt/ (sıfat) — verimli. Making good, thorough, or careful use of resources; not consuming extra. Especially, making good use of time or energy.
- footage /ˈfʊtɪd͡ʒ/ (isim) — metraj. An amount of film or tape that has been used to record something, or the content of the recording.
- interim /ˈɪntəɹɪm/ (sıfat) — geçici. Transitional.
- critic /ˈkɹɪt.ɪk/ (isim) — eleştirmen. A person who appraises the works of others.
- query /ˈkwɪɹ.i/ (isim) — sorgulama. A question, an inquiry (US), an enquiry (UK).
- vast /væst/ (sıfat) — dev, engin. Very large or wide (literally or figuratively).
- audio /ˈɔ.di.oʊ/ (sıfat) — ses. Focused on audible sound, as opposed to sight.
- complicated /ˈkɑm.plɪˌkeɪ.tɪd/ (sıfat) — karışık, komplike. Difficult or convoluted.
- impressive /ɪmˈpɹɛsɪv/ (sıfat) — etkileyici. Making, or tending to make, a positive impression; having power to impress.
- explanation /ˌɛkspləˈneɪʃən/ (isim) — açıklama. The act or process of explaining.
Duyacağınız deyimsel fiiller
- look at (fiil) — bakmak. To observe or watch.
- wrap up (fiil) — paketlemek, (bir şeyi) sarmak. To cover or enclose (something) by folding and securing a covering entirely around it.
Dikkat edilecek telaffuzlar
Konuşmacı I'm, I'll, doesn't gibi kısaltılmış biçimleri 25 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “sh” ve “zh” sesleri: efficient /ɪˈfɪʃənt/, explanation /ˌɛkspləˈneɪʃən/, essentially /ɪˈsɛnʃəli/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, iteration /ˌɪt.əˈɹeɪ.ʃən/
- Uzun kelimeler — vurguyu doğru yere koyun: manually /ˈmænj(u)əliː/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, explanation /ˌɛkspləˈneɪʃən/, essentially /ɪˈsɛnʃəli/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- 0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; output, trait, calendar gibi kelimelere özellikle dikkat edin.
Bu videodaki dil bilgisi
Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:
| Yapı | Videoda |
|---|---|
| Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli | is meant · being trained · being done |
| Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem | have limited · has already connected · I've drafted |
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.