This lesson we are going to talk about ChatGPT versus Elm applications.
We will learn.
What are the foundational.
And we will see that LLM applications solve the limitations of these foundational models.
So let's talk a little bit about ChatGPT.
So ChatGPT is a private foundational model.
This.
Models are like the brains of artificial intelligence, let's call them that.
But you will see that these brains are a little bit limited.
You can find private foundational models like ChatGPT, and you can find open source foundational models
like lamassu, Falcon, and others we will see later.
In this case, ChatGPT is a private foundational model developed from a company called OpenAI.
And as a private foundational model.
It can be costly.
And you will see that when we start developing LM applications, one of the most important thing we
have to keep in mind is cost.
So ChatGPT can be costly and as it is a private product, it can change at any time.
So OpenAI, the company behind ChatGPT, can decide at any moment that you want to change the way ChatGPT
works.
On the contrary, LM applications are not foundational models.
They are built on top of foundational models.
So we can build an LM application on top of one or on top of several foundational models, and we can
decide if we are going to use a private foundational model like ChatGPT or an open source one.
So LM applications do not depend on an individual company like the case of ChatGPT.
And with LM applications, we can control the cost.
And we can control the changes that are, uh, being made in the application because it's our application.
ChatGPT has some other interesting features.
One of them is what we call context.
We will talk more about, uh, context later, but right now, just think or think about context as
let's say short time memory.
So ChatGPT has a limited context, has a limited short time memory.
And on the contrary, LM applications can have a broader context, can have more short memory.
ChatGPT does not know your company's data.
This is a very important thing.
So ChatGPT doesn't know anything about the data of your company, but an LM application can be trained
on your computer.
And this is important because many companies, they don't want ChatGPT to have anything to do with their
data because they don't trust ChatGPT or ChatGPT security.
So when you are talking with ChatGPT, your conversations are in the cloud.
And when you are talking with an LM application, your conversation can be in the cloud or it can be
private.
So you have more flexibility with LM applications.
ChatGPT has a way to integrate it with external tools, what we call integrations.
So external APIs.
But it has a limited number of integrations, while an LM application has unlimited integrations possible.
And finally, ChatGPT has a limited functionality.
It has a huge, very interesting number of, uh, possibilities, but they are limited.
You cannot do anything you want with ChatGPT.
You are limited to a number of things you can do, but with LM applications you have unlimited functionality.
So your imagination is the limit with LM applications.
Let's learn a little bit more about that.
So in short.
Foundational models like ChatGPT or others like llama two.
Falcon.
Mistral.
Are like the gasoline of like the gasoline for LM applications and LM applications would be like cars,
motorcycles, boats, machines, etc. that run on the gasoline provided by foundational models like
ChatGPT.
So ChatGPT is the gasoline, and LM applications are the cars that are using the gasoline of ChatGPT.
ChatGPT right now is currently the highest quality foundational model, but it is also the most expensive.
Alternatives to ChatGPT are becoming increasingly numerous, higher in quality and less costly.
Foundational models.
They can do.
A lot of things.
You can create conversational conversational agents.
You can summarize documents.
You can generate content.
You can do tasks like sentiment analysis, text classification, code creation, debugging, etc.,
etc. but they have many things they cannot do.
They have many limitations.
And this is where LM applications play a very important role.
Because LM applications solve many limitations of ChatGPT and other foundational models.
What kind of limitations has.
Our foundational model like ChatGPT.
The cost?
The context.
The regulatory challenges.
Data privacy.
Inference.
Latency.
Speed.
Diversity of data sources.
Whenever we are going to use different data sources, we are going to have a problem with ChatGPT.
Lack of reproducibility.
We'll see what it is later.
Hallucinations.
This is fake content made up by ChatGPT evaluation.
Fragility of prompts, outdated knowledge.
So, as I was telling you.
ChatGPT is not as omnipotent as it seems.
It is very powerful, but it is not as omnipotent as it seems.
And L.l.m. applications are the key to use the gasoline.
ChatGPT and other foundational models give you to do really amazing things.
So his LLM application, the ones that are going to create the magic in the artificial intelligence
world.
So in this lesson we have seen we will talk more about that later.
But we have seen a little bit about ChatGPT versus LLM applications.
We saw what are the foundational models.
And the most important thing we saw that LLM applications, LLM apps solve the limitations of the foundational
models in the next lesson, we will tell you how to download the two books included with this bootcamp.
Keys to artificial intelligence.
This is a 300 page book and 100 AI startups.
These are 200 pages book.
Both of us are available in Amazon for more than $50.
Combine, so it's a very good deal for you to have them included in this boot camp.