Pratique du Shadowing: AI Agents, Clearly Explained - Apprendre l'anglais à l'oral avec la vidéo
Création de la leçon...
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AI AI AI AI AI AI - You know, more agentic.
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- Agentic capabilities.
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- AI agent.
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- Agents.
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- Agentic workflows.
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- Agents.
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- Agents.
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- Agents.
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- Agents.
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- Agents.
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- Agents.
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- All right, most explanations of AI agents is either too technical or too basic.
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This video is meant for people like myself.
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You have zero technical background, but you use AI tools regularly,
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and you wanna learn just enough about AI agents In this video, we'll follow a simple one,
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two, three, learning path by building on concepts you already understand, like ChatGPT, and then moving on to AI workflows,
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and then finally, AI agents, all the while using examples you will actually encounter in real life.
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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.
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Let's get started.
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Kicking things off at level one, large language models.
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Popular AI chatbots like ChatGPT, Google Gemini, and Cloud are applications built on top of large language models, LLMs.
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And they're fantastic at generating and editing text.
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Here's a simple visualization.
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"You, the human, provides an input, and the LLM produces an output based on its training data." For example,
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if I were to ask ChachiPT to draft an email requesting a coffee chat, my prompt is the input,
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and the resulting email that's way more polite than I would ever be in real life is the output.
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So far, so good, right?
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Simple stuff.
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But what if I asked ChatGPT when?
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"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.
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It doesn't have access to my calendar.
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This highlights two key traits of large language models.
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First, despite being trained on vast amounts of data, they have limited knowledge of proprietary information, like our personal information or internal company data.
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Second, LLMs are passive.
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Keep these two traits in mind moving forward.
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Moving to level two, AI workflows.
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Let's build on our example.
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What if I, a human, told the LLM, every time I ask about a personal event,
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perform a search query and fetch data from my Google Calendar before providing a response.
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With this logic implemented, the next time I ask, when is my coffee chat with Elon Husky,
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I'll get the correct answer because the LLM will now first go into my Google Calendar to find that information.
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Here's where it gets tricky.
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What if my next follow -up question is, what will the weather be like that day?
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The LLM will now fail at answering the query
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because the path we told the LLM to follow is to always search my Google Calendar, which does not have information about the weather.
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This is a fundamental trait of AI workflows.
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They can only follow predefined paths set by humans.
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And if you want to get further.
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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.
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The weather forecast for seeing Elon Husky is sunny with a chance of being a good boy.
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Here's the thing.
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No matter how many steps we add, this is still just an AI workflow.
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Even if there were hundreds or thousands of steps, if a human is the decision maker, there is no AI agent involvement.
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Pro tip, retrieval augmented generation or RAG, is a fancy term that's thrown around a lot.
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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.
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Essentially, RAG is just a type of AI workflow.
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By the way, I have a free AI toolkit
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that cuts through the noise and helps you master essential AI tools and workflows.
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I'll leave a link to that down below.
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Here's a real world example.
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Following Helena Liu's amazing tutorial, I created a simple AI workflow using make .com.
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Here you can see that first, I'm using Google Sheets to do something.
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Specifically, I'm compiling links to news articles in a Google Sheet, and this is that Google Sheet.
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Second, I'm using perplexity to summarize those news articles.
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Then, using Claude and using a prompt that I wrote, I'm asking Claude to draft a LinkedIn and Instagram post.
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Finally, I can schedule this to run automatically every day at 8 :00 AM.
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As you can see, this is an AI workflow because it follows a predefined path set by me.
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Step one, you do this.
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Step two, you do this.
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Step three, you do this.
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And finally, remember to run daily at 8 :00 AM.
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One last thing, if I test this workflow and I don't like the final outputs, of the LinkedIn post for example,
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as you can see right here, it's not funny enough.
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And I'm naturally hilarious, right?
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I'd have to manually go back and rewrite the prompt for Claude.
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Okay.
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And this trial and error iteration is currently being done by me, a human.
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So keep that in mind moving forward.
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All right, level three AI agents.
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Continuing the make .com example, let's break down what I've been doing so far as the human decision maker.
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With the goal of creating social media posts based off of news articles, I need to do two things.
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First, reason or think about the best approach.
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I need to first compile the news articles, then summarize them, then write the final posts.
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Second, take action using tools.
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I need to find and link to those "use perplexity" for real -time summarization, and then "clawed" for copywriting.
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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,
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is for me, the human decision maker, to be replaced by an LLM.
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In other words, the AI agent must reason.
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What's the most efficient way to compile these news articles?
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Should I copy and paste each article into a Word document?
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to compile links to those articles, and then use another tool to fetch the data.
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Yes, that makes more sense.
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The AI agent must act, aka do things via tools.
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Should I use Microsoft Word to compile links?
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No, inserting links directly into rows is way more efficient.
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What about Excel?
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No, the user has already connected their Google account with make .com, so Google Sheets is a better option.
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Pro tip: because of this,
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the most common configuration Agents must reason and act, so re -act.
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Sounds simple once we break it down, right?
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A third key trait of AI agents is their ability to iterate.
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Remember when I had to manually rewrite the prompt to make the LinkedIn post funnier?
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I, the human, probably need to repeat this iterative process a few times to get something I'm happy with, right?
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An AI agent will be able to do the same thing autonomously.
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In our example, the AI agent would autonomously add in another LLM to critique its own output.
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Okay, I've drafted V1 of a LinkedIn post.
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How do I make sure it's good?
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Oh, I know.
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I'll add another step where an LLM will critique the post based on LinkedIn post practices.
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And let's repeat this until the best practices criteria are all met.
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And after a few cycles of that, we have the final output.
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That was a hypothetical example.
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So let's move on to a real world AI agent example.
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Andrew Ng is a preeminent figure in AI and he created this demo website that illustrates how an AI agent works.
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I'll link the full video down below, but when I search for a keyword like skier, Enter.
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The AI vision agent in the background is first reasoning
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What a skier looks like a person on skis going really fast in snow for example, right?
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I'm not sure and then it's acting by I looking at clips in video footage,
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trying to identify what it thinks a skier is,
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indexing that clip and then returning that clip to us.
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Although this might not feel impressive, remember that an AI agent did all that instead of a human reviewing the footage beforehand,
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manually identifying the skier and adding tags like skier, mountain, ski, snow.
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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?
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The average user like myself, wants a simple app that just works without me having to understand what's going on in the backend.
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Speaking of examples, I'm also building my very own basic AI agent using NAN.
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So let me know in the comments what type of AI agent you'd like me to make a tutorial on next.
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To wrap up, here's a simplified visualization of the three levels we covered today.
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Level one, we provide an input and the LM responds with an output easy.
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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.
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The key trait here is that the human programs a path for the LLMs to follow.
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Level three, the AI agent receives a goal, and the LLM performs reasoning to determine how best to achieve the goal,
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takes action using tools to produce an interim result, observes that interim result, and decides whether iterations are required,
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and produces a final output that achieves the initial goal.
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The key trait here is that the LLM is the decision maker in the workflow.
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If you found this helpful, you might want to learn how to build a prompts database in Notion.
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- Bye.
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Have a great one.
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Vocabulaire et conseils d’expression pour cette leçon
Cette vidéo contient 137 phrases et 1642 mots à répéter en shadowing. La partie parlée dure 10:04. Le locuteur parle à un rythme naturel d’environ 163 mots par minute, proche d’une conversation courante. Seuls 77 % 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 moins courants de la vidéo, avec leur prononciation et leur sens :
- output /ˈaʊtpʊt/ (nom) — sortie. Production; quantity produced, created, or completed.
- trait /tɹeɪt/ (nom) — trait. An identifying characteristic, habit or trend.
- calendar /ˈkæl.ən.də/ (nom) — calendrier. Any system by which time is divided into days, weeks, months, and years.
- prompt /pɹɑmpt/ (verbe) — inciter. To lead (someone) toward what they should say or do.
- maker /ˈmeɪk.ɚ/ (nom) — faiseur, fabricant. Someone who makes; a person or thing that makes or produces something.
- clip /klɪp/ (nom) — pince. Something which clips or grasps; a device for attaching one object to another.
- manually /ˈmænj(u)əliː/ (adverbe) — manuellement, à la main. By hand.
- efficient /ɪˈfɪʃənt/ (adjectif) — efficace. Making good, thorough, or careful use of resources; not consuming extra. Especially, making good use of time or energy.
- footage /ˈfʊtɪd͡ʒ/ (nom) — pellicule enregistrée, métrage cinématographique. An amount of film or tape that has been used to record something, or the content of the recording.
- demo /ˈdɛm.oʊ/ (nom) — démo, démonstration. A demonstration or visual explanation.
- critic /ˈkɹɪt.ɪk/ (nom) — critique. A person who appraises the works of others.
- tutorial /ˌtjuːˈtɔːɹɪəl/ (nom) — didacticiel, tutoriel. A self-paced learning exercise; a lesson prepared so that a student can learn at their own speed or convenience.
- fetch /fɛt͡ʃ/ (verbe) — ramener, rapporter. To retrieve; to bear towards; to go and get.
- query /ˈkwɪɹ.i/ (nom) — question. A question, an inquiry (US), an enquiry (UK).
- vast /væst/ (adjectif) — vaste, immense. Very large or wide (literally or figuratively).
Les verbes à particule que vous entendrez
- break down (verbe) — tomber en panne. To stop functioning.
- go back (verbe) — rentrer, retourner. To return to a place or state after having been there at a previous time.
- look at (verbe) — regarder. To observe or watch.
- think about /ˈθɪŋk əˌbaʊt/ (verbe) — penser à, réfléchir à. To ponder.
- wrap up (verbe) — emballer. To cover or enclose (something) by folding and securing a covering entirely around it.
Prononciation à surveiller
Le locuteur utilise 25 contractions et formes réduites, comme I'm, I'll, doesn't. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: efficient /ɪˈfɪʃənt/, explanation /ˌɛkspləˈneɪʃən/, essentially /ɪˈsɛnʃəli/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, iteration /ˌɪt.əˈɹeɪ.ʃən/
- Mots longs — placez bien l’accent: manually /ˈmænj(u)əliː/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, explanation /ˌɛkspləˈneɪʃən/, essentially /ɪˈsɛnʃəli/
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 output, trait, calendar.
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | is meant · being trained · being done |
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | have limited · has already connected · I've drafted |
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.