Hello everyone and welcome.
In this video we will learn about evolution of large language models.
We'll learn how large language models evolve from being a model that helps with prediction, to adding
technology advancements to the model, and how it can do more than just prediction.
So let's dive deeper into evolution of large language models.
So what are the LLM capabilities, limitations, and path to MCP?
Large language models like ChatGPT represent a significant breakthrough in early artificial intelligence.
These systems excel at numerous complex tasks like including code generation, text summarization,
and even image creation.
There were centrality has made them valuable across many industries and applications.
However, this technology had some initial limitation.
Despite their impressive capabilities, there are notable constraints on Lem.
Consider the first version of ChatGPT.
If you asked it to send an email on your behalf, it would simply respond saying, I cannot perform
that action.
This highlights a fundamental limitation of traditional large language model.
What are the core functions of Lem?
It's a sophisticated prediction engine.
They analyze patterns in the text and predict what word or phrase should come next in the sequence.
So, for example, if you say I want to play.
The model uses its training data to determine what is the most likely next ward going to be.
It can be games, music, or anything that it is trained on.
So the next token prediction is essentially the mechanism that powers all llms.
LMS.
So why does it matter?
Recognizing this, limitations help us understand why technologies like Model Context Protocol presents
the next evolutionary step for LM.
While LM excels at understanding language generation, they needed additional capabilities to interact
with external systems and perform real world tasks.
So now let's go over the first evolution of LM, which was.
That is retrieval augmented generation.
It's a technique that enhances large language models by giving them access to external information or
sources that it is not trained on.
The core problem that Rags solves is llms are trained on certain specific data.
They cannot use information that is outside the training data to analyze and answer your questions,
something like whether or company specific data is something that it's not trained on.
So this creates a significant limitations on the accurate and up to date specialized information that
can help.
So how Rag works.
It has the first step of query processing.
When you ask question the system, analyze what information might be needed.
Then we have information retrieval.
It searches through external database documents or knowledge sources.
Then there is context integration.
The retrieved information is fed to the along with the original question, and then generates an answer
using both its trading knowledge and the information that was retrieved.
So I have covered Rag in a different section where we would take a deep dive into this concept.
However, I briefly went over Rag here so that we understand how the Llms are evolving over time.
So now that we had Rag, there were still challenges and we wanted to extend the LLM capabilities.
The next thing that was born is AI agent.
So what is AI agent?
AI agent is a system that combines large language models with external tools.
You can think of AI agents as system that combines LLM with external tools to perform autonomous and
complex tasks.
This was a significant leap beyond expeditions that LLM were able to work with external entities to
solve more complex problem at an enterprise level.
You can think of agents as tools that can integrate with application programming interface that allows
them Interact with external services and systems.
These tools extend the LMS capability far beyond text generation, enabling them to perform real world
actions and access necessary information.
So now let's take a real world example of LM using agents, and also learn about some of the challenges
that comes with it.
So here is a real world example perplexity.
That's the search engine that defines and demonstrates the evolution very effectively.
So when you chat with perplexity you're not using just the LM.
You are using a system that LM also does internet search to retrieve the information that you need for
the search that you perform on the website.
LL itself cannot browse the internet, but the integration tools make it possible.
There are also challenges in this area.
While tool integration works well.
Building a comprehensive AI assistant becomes increasingly complex.
Imagine creating a system that can search internet, read emails, summarize documents all in a very
cohesive workflow.
Each additional tool requires careful integration and coordination.
There are also scaling problems.
Current approaches require custom integration for each tool, creating maintenance burden and inconsistent
user experience.
This is how life has evolved over time.
However, there is more room to evolve and that is where MCP can help us with some complexity and scaling
problems.
So let's take a deep dive into some other concepts before we learn about MCP.
Thank you and I'll see you in the next video.