With introductions, hello world, and a bit of
basic information behind us, we now are ready
to tackle the four main AI concepts to
learn as a developer.
And this is what will be the main
part of the rest of this course.
As I've mentioned before, there's, in my opinion,
four main AI concepts, those being chat, tool
calling, structured output, and rack.
In this video, I will just briefly mention
what they are, so you are aware, and
then there will be different sections dedicated for
going deeper into all four of these.
So the first one being chat, and you
might think, well, I'm not building a chatbot
or anything.
Chat is just a common word for a
request response capability.
It doesn't need to be question and answer,
it can also be action and response back
to that action, and so on.
So you could make a chatbot, but it
could also just run behind the scenes every
time some webhook is being triggered in your
system.
You need to get some data out from
a system and paste it into another system.
So chat is the bare bones of what
we're going to do here.
We're going to talk about conversations, where we
have back and forwards, instructions, and prompt engineering
in that section.
The next concept is tool calling, and that's
the ability to call user code.
So we can have live interactions with other
systems, or we can ask for more data.
So tools can either be informational, meaning I
need to go up and grab some data,
like a web search, or it can be
an action tool that can go in and
make, for example, some kind of order in
an invoice system, or in a back end
of a webshop, or something for that.
We can make them ourselves.
We can use the MCP protocol to use
tools.
We can use other agents as tools.
So it's a very, very important concept, so
it has its own section with a lot
of details in it.
The third concept is structured output.
Some people put that in together with chat.
I find it so important and one of
the most used of the features, so I
put it in its own section.
So structured output is more about whenever we
saw hello world, we saw that sometimes it
said the capital of France is Paris, other
times it just said the word Paris, and
so on.
So we need to steer the model even
more when we get data back.
So we often don't just want text or
markdown back.
We actually want some structure in it, and
that is what structured output gives, in that
it can give it back as JSON.
There's not much more to it, but it's
very important that it's being learned, and also
how you steer structured output.
The final concept is the biggest one, meaning
that while it's one of the first that
came along, being Retrieval Augmented Generation, or RAC
for short, is that we can give our
own data.
So AIs know a lot.
They can know capital of France.
They can know every little detail around the
world, but they cannot talk with your company
data, your personal data, and so on, without
us giving it the data up front.
And that is what RAC is, that we
begin to take our own data, put them
into vector stores, do similarity searches on them,
and so on.
There's a lot of concept in that area,
so it's one of the bigger sections, but
we will go into every single little detail
so you can be successful with RAC as
well.
So that's the four main concepts which we'll
now dive into.