1
00:00:04,330 --> 00:00:11,560
In this lesson, we are going to talk about other interesting use cases for LLM applications.

2
00:00:11,800 --> 00:00:16,180
I especially wanted to tell you about agents.

3
00:00:16,210 --> 00:00:27,250
We have already talked about a, the, the, the most frequent and the most performed tasks of LM applications.

4
00:00:27,250 --> 00:00:34,000
And we saw that the LM application a have many different possibilities.

5
00:00:34,000 --> 00:00:44,620
But we can, you know, group the different tasks by a these categories like assistant to write, assistant

6
00:00:44,620 --> 00:00:50,170
to answer questions, assistant to read assistant to uh for conversations.

7
00:00:50,170 --> 00:01:02,710
We can also classify the types of lamps by a if they are just one application with one user interface,

8
00:01:02,710 --> 00:01:06,190
or if they are a part of a bigger software application.

9
00:01:06,190 --> 00:01:10,270
This is going to be a growing trend.

10
00:01:10,300 --> 00:01:10,960
Trend.

11
00:01:10,960 --> 00:01:24,790
So what we are seeing is that more and more software applications are including an LM app, a, A as

12
00:01:24,790 --> 00:01:28,060
a part of the bigger software application.

13
00:01:28,060 --> 00:01:31,960
So this is this is growing a lot.

14
00:01:33,180 --> 00:01:39,870
And then what I wanted to to tell you about is about the autonomous agents.

15
00:01:39,900 --> 00:01:52,530
So one possibility of the LM application is what we call agents A this has been very popular a during

16
00:01:52,530 --> 00:02:01,320
the last year, but it is important to see that they are still in a in an early development stage.

17
00:02:02,310 --> 00:02:05,880
I so usually they.

18
00:02:06,840 --> 00:02:09,600
Are being used as demos.

19
00:02:10,780 --> 00:02:18,940
But they are not working in a professional a in professional ways in most of the cases.

20
00:02:18,940 --> 00:02:19,540
Why?

21
00:02:19,570 --> 00:02:30,940
Because usually they are or too expensive, or too or too expensive in cost, or too expensive in computational

22
00:02:30,940 --> 00:02:31,870
power.

23
00:02:32,290 --> 00:02:42,310
So the agents are applications that are supposed to solve problems, complex problems.

24
00:02:43,250 --> 00:02:54,980
In an autonomous way so I can give them like a complex task, like, okay, a help me make $1 million

25
00:02:54,980 --> 00:03:04,100
this year and they will start working in different tasks at the same time, in parallel or in a or in

26
00:03:04,100 --> 00:03:08,900
a progressive way in order to fulfill this task.

27
00:03:08,900 --> 00:03:17,270
So the methodology is more a theoretical one than a practical one.

28
00:03:17,570 --> 00:03:27,830
A we have some examples, like for example, we have auto, GPT and other examples, open source examples

29
00:03:27,830 --> 00:03:31,940
even that have got a lot of attention.

30
00:03:32,600 --> 00:03:38,450
But as I'm telling you, not still in the professional way.

31
00:03:38,900 --> 00:03:41,120
So we.

32
00:03:42,250 --> 00:03:49,090
Are waiting and see what is happening with agent development right now.

33
00:03:49,120 --> 00:03:57,130
It's important for you to understand that they are still in a very early development stage.

34
00:03:57,430 --> 00:04:02,230
Okay, so it's interesting to know about them.

35
00:04:02,230 --> 00:04:10,420
It's interesting to practice around them, but you will see that most professionals are not working

36
00:04:10,420 --> 00:04:18,790
with this, uh, with this functionality of the LM applications, the techniques that are working,

37
00:04:19,360 --> 00:04:25,960
the techniques that have proven, uh, to deliver real value are different techniques.

38
00:04:25,960 --> 00:04:31,750
For example, we are going to be focusing in one of them, which is the rack technique, which is the

39
00:04:31,750 --> 00:04:36,790
the the most frequently used in the LM applications.

40
00:04:36,790 --> 00:04:46,150
And if you go to the to the book 100 AI startups, you will see a lot of startups using the rack technique

41
00:04:46,150 --> 00:04:53,710
that you will learn later, but not, uh, the autonomous agent, uh, methodology.

42
00:04:53,710 --> 00:04:58,210
This is more experimental and less real.

43
00:04:58,210 --> 00:05:03,850
So I thought it was important for you to understand this, uh, right.

44
00:05:03,970 --> 00:05:05,290
Right from the beginning.

45
00:05:05,290 --> 00:05:07,390
Before we go further.

46
00:05:07,660 --> 00:05:08,080
Okay.

47
00:05:10,180 --> 00:05:16,570
So in this lesson we have seen other interesting use cases of Elm applications.

48
00:05:16,570 --> 00:05:22,030
And in the next lesson we are starting with LMS.

49
00:05:22,030 --> 00:05:25,840
We are going to to see an introduction to LMS.

50
00:05:25,840 --> 00:05:30,460
Remember LMS are different from LMS applications.

51
00:05:30,460 --> 00:05:33,280
So let's start learning about that.