In this lesson, we are going to talk about other interesting use cases for LLM applications.
I especially wanted to tell you about agents.
We have already talked about a, the, the, the most frequent and the most performed tasks of LM applications.
And we saw that the LM application a have many different possibilities.
But we can, you know, group the different tasks by a these categories like assistant to write, assistant
to answer questions, assistant to read assistant to uh for conversations.
We can also classify the types of lamps by a if they are just one application with one user interface,
or if they are a part of a bigger software application.
This is going to be a growing trend.
Trend.
So what we are seeing is that more and more software applications are including an LM app, a, A as
a part of the bigger software application.
So this is this is growing a lot.
And then what I wanted to to tell you about is about the autonomous agents.
So one possibility of the LM application is what we call agents A this has been very popular a during
the last year, but it is important to see that they are still in a in an early development stage.
I so usually they.
Are being used as demos.
But they are not working in a professional a in professional ways in most of the cases.
Why?
Because usually they are or too expensive, or too or too expensive in cost, or too expensive in computational
power.
So the agents are applications that are supposed to solve problems, complex problems.
In an autonomous way so I can give them like a complex task, like, okay, a help me make $1 million
this year and they will start working in different tasks at the same time, in parallel or in a or in
a progressive way in order to fulfill this task.
So the methodology is more a theoretical one than a practical one.
A we have some examples, like for example, we have auto, GPT and other examples, open source examples
even that have got a lot of attention.
But as I'm telling you, not still in the professional way.
So we.
Are waiting and see what is happening with agent development right now.
It's important for you to understand that they are still in a very early development stage.
Okay, so it's interesting to know about them.
It's interesting to practice around them, but you will see that most professionals are not working
with this, uh, with this functionality of the LM applications, the techniques that are working,
the techniques that have proven, uh, to deliver real value are different techniques.
For example, we are going to be focusing in one of them, which is the rack technique, which is the
the the most frequently used in the LM applications.
And if you go to the to the book 100 AI startups, you will see a lot of startups using the rack technique
that you will learn later, but not, uh, the autonomous agent, uh, methodology.
This is more experimental and less real.
So I thought it was important for you to understand this, uh, right.
Right from the beginning.
Before we go further.
Okay.
So in this lesson we have seen other interesting use cases of Elm applications.
And in the next lesson we are starting with LMS.
We are going to to see an introduction to LMS.
Remember LMS are different from LMS applications.
So let's start learning about that.