WEBVTT

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With introductions, hello world, and a bit of

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basic information behind us, we now are ready

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to tackle the four main AI concepts to

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learn as a developer.

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And this is what will be the main

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part of the rest of this course.

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As I've mentioned before, there's, in my opinion,

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four main AI concepts, those being chat, tool

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calling, structured output, and rack.

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In this video, I will just briefly mention

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what they are, so you are aware, and

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then there will be different sections dedicated for

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going deeper into all four of these.

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So the first one being chat, and you

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might think, well, I'm not building a chatbot

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or anything.

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Chat is just a common word for a

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request response capability.

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It doesn't need to be question and answer,

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it can also be action and response back

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to that action, and so on.

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So you could make a chatbot, but it

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could also just run behind the scenes every

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time some webhook is being triggered in your

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system.

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You need to get some data out from

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a system and paste it into another system.

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So chat is the bare bones of what

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we're going to do here.

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We're going to talk about conversations, where we

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have back and forwards, instructions, and prompt engineering

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in that section.

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The next concept is tool calling, and that's

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the ability to call user code.

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So we can have live interactions with other

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systems, or we can ask for more data.

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So tools can either be informational, meaning I

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need to go up and grab some data,

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like a web search, or it can be

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an action tool that can go in and

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make, for example, some kind of order in

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an invoice system, or in a back end

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of a webshop, or something for that.

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We can make them ourselves.

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We can use the MCP protocol to use

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tools.

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We can use other agents as tools.

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So it's a very, very important concept, so

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it has its own section with a lot

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of details in it.

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The third concept is structured output.

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Some people put that in together with chat.

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I find it so important and one of

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the most used of the features, so I

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put it in its own section.

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So structured output is more about whenever we

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saw hello world, we saw that sometimes it

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said the capital of France is Paris, other

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times it just said the word Paris, and

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so on.

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So we need to steer the model even

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more when we get data back.

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So we often don't just want text or

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markdown back.

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We actually want some structure in it, and

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that is what structured output gives, in that

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it can give it back as JSON.

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There's not much more to it, but it's

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very important that it's being learned, and also

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how you steer structured output.

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The final concept is the biggest one, meaning

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that while it's one of the first that

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came along, being Retrieval Augmented Generation, or RAC

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for short, is that we can give our

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own data.

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So AIs know a lot.

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They can know capital of France.

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They can know every little detail around the

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world, but they cannot talk with your company

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data, your personal data, and so on, without

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us giving it the data up front.

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And that is what RAC is, that we

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begin to take our own data, put them

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into vector stores, do similarity searches on them,

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and so on.

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There's a lot of concept in that area,

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so it's one of the bigger sections, but

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we will go into every single little detail

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so you can be successful with RAC as

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well.

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So that's the four main concepts which we'll

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now dive into.
