And so this is what I have in store for you over the next three weeks.
This is the curriculum ahead.
There's going to be three different types of session.
There's going to be some core sessions that focus on Agentic AI, on pn10, on the core capabilities.
There's going to be some sessions that are purely dedicated to integrations, because integrations is
such a huge part of working with an A10 and delivering business impact.
So that's a whole topic of its own.
And then there's real world projects.
That is the third type of focus area for us.
Okay.
So how does this fit into the three weeks.
All right.
So each of the three weeks I have given a punchy title to describe what we're going to try and achieve.
The first week is called automate.
It's about using Anytime Cloud to make workflows for your business.
Automate is week one.
Week two is accelerate.
This is next level stuff.
This is saying we want to add more juice to our business or our clients.
We want to have voice agents.
We want to use rag.
You may not know what Rag is or you might you certainly probably you've probably heard of it because
it's one of these hyped ideas.
We're going to dig into it in week two as we accelerate.
And that will take us to week three, which I'm calling amplify.
This is something which really allows you to dial up your business or your client's businesses or your
product using multi-agent systems.
We'll use MCP, another hype word.
Uh, we're going to see the reality, the truth, where it's super impactful and where it's probably
a red herring so that we can focus on business value.
And by the end of week three, you will have automated, accelerated and amplified your business.
And each of these three weeks have five days.
Uh, and these are the five days in week one, shown as the day one, two, three, four, five, five
boxes.
And the first two are purple, which means that there are about core skills.
And then we've got some integrations days.
And then we end with a project, a business project that you're gonna love.
The second week here come the five days.
There they are.
It's a purple day.
A yellow day for integrations.
Another purple day when we go into rag, a yellow day with super bass, and then the voice agent that
we deliver at the end of week two and week three.
Week three is starts with a purple as we self-host n810 and we use local models.
Then we have some advanced integrations.
Then we go back to purple for MCP and then Context engineering Subagents hot topics right now in Atlantic
AI.
And then we conclude with the capstone project to amplify your business at the end of week three.
And after all of that, you will be able to to take this celebration moment, get your certificate and
your your, your cup, your chalice and be able to say, I am now an agentic AI builder.
Okay, that's it for our objectives and our course description.
It's time for us to actually get going.
I'm going to start with a little bit of theory stuff, and then we're going to go back to action.
Back to N810.
Back to building some workflows.
So first up just so we're on the same page, I have some sort of basic definitions, some foundational
stuff.
I imagine most of you are very comfortable with this already, but I think it's good to make sure that
we we agree on this, and maybe I'll try and add in some some juicy extras to keep it interesting.
We should start with a definition of an LM, because otherwise I'm just going to be using this assuming
that you know all about this.
So, you know, there's lots of ways to to define what an LM is.
It's basically it's the sort of core model, the data science model that's at the heart of what we call
generative AI.
It's an AI program that it's able to generate text.
What it does is spits out text.
And typically that text is something which is either answering a question, or maybe that text represents
an action that should be taken.
If the question is, what action should I do next?
It's the answer to that question.
It's just generating content.
It's given some sort of an input and it's generating output.
Now all of us are guilty, myself included, of sometimes treating llms large language models in an
almost human like way, giving it human like behaviors.
Thinking of it as if we are making some kind of connection.
Putting motivations and intentions behind what's going on.
A chat that we're having with it.
And it's always important to stay grounded in the reality.
Llms are very powerful, very effective statistical programs.
They are pattern matches on a massive scale.
They have been given tons and tons of data taken from the internet, taken from all sorts of sources.
They've analyzed the patterns.
We call it training.
They have been built so that when they are given an input sequence of some text, they are very effective
at predicting the most likely text to come next.
And if that text is some sort of a question, they are likely to predict a good answer.
If it's if it's a request for what should I do next, then it will predict actions.
It can predict tools to be called.
It can have a conversation.
And all of this is built around really effective pattern matching, really effective statistics.
And we always have to keep that in mind as we work with large language models.
And there's a whole bag of tricks and techniques that we use around large language models to give this
impression that you're in a conversation with them.
There are tricks like this, this approach called memory, for making it seem as if the LLM is able
to remember the conversation, keep, keep the the flow of it.
But it's all done with, with, with clever little techniques.
It is simply a pattern matcher, a statistical engine, that's all it is behind the scenes.
And it's somewhat bewildering and astonishing that as a result, purely as a result of the level of
scale that these llms are run at, that they they have this apparent intelligence and it is just a byproduct
of scale.
People like to call it emergent intelligence.
And if this is something that you're still puzzled by, I've got a bunch of videos on YouTube that try
and take you through the journey of coming to grips with how it is that just by virtue of the trillions
of these parameters, these internal weights within a modern LLM, we're able to achieve this illusion
of intelligence.
And as a final point, I'll make here that something that most of you probably are very clear on.
But some people do get, get tripped up by is the distinction between ChatGPT the product and GPT the
model.
GPT is a model, a large language model.
It is the statistical engine.
It is something which takes an input sequence and gives an output sequence.
It's what we would call stateless, which means that every time that you make a call to it, every time
that you invoke GPT, it's completely fresh.
It has no knowledge of what came before, and it's just given a new input and it gives an output that
is the LLM.
The large language model called GPT, uh and ChatGPT is a software product also written by OpenAI.
They built the model The GPT LLM.
And then they thought, you know what?
We're going to make a product that uses this in the most effective way.
That's going to appeal to people.
It's got to have a memory.
It's got to be able to do other things.
In fact, nowadays it can also search the web and do all sorts of stuff.
But this was all built and written with software, with with normal software.
And it uses GPT, the LLM to to add in this apparent intelligence.
So keeping in your mind that separation between software, the ChatGPT the product and the LLM, GPT,
which is the underlying model, the AI which is stateless and which gets an input and gives an output,
that's an important distinction to have in your mind the product versus the model.
And indeed, what we were doing in the instant gratification earlier is we were building a product that
used a model sitting with open router, and we were able to add a little chat functionality on top of
the model.
And that's why it's important to keep keep that difference in your mind.
And if you knew all this already then hang in there.
We'll we'll cover a lot more.
And if you didn't then.
And if you weren't, if you've got questions, you're not entirely sure.
You could also hang in there because we're going to cover so much of this and you're going to this.
This is going to be old hat to you in no time.
The other thing that I just wanted to mention, again, probably people are super familiar with this,
just the concept of an API.
People talk about APIs all the time, and you may be thinking, I've never I've never really been sure
what an API actually is.
And there's good reason for that.
That API is loosely defined.
It's generally you could think of it as simply just a way to connect different applications and services,
different software products, typically using common standards, using using some typical techniques
for connecting together different bits of software in a way that's going to make it relatively easy
to plug them together.
And years ago, it used to be really, really hard.
Even even with APIs, it was still incredibly challenging to connect different software products.
Nowadays, it's much easier because there are such common standards, but it's still always a challenge.
Connecting things is always a challenge, and that's the challenge that N810 has made.
Unbelievably simple.
But that's that's at the heart of APIs connecting different systems.
And while you can use the term API in many contexts, it's most often used to describe connecting different
applications using a web connection, a web interface.
Typically you're using HTTP, the same technology we use to collect web pages.
It's an HTTP connection that you're making the URL, the web page that you're connecting to in order
to make your request to another piece of software.
It's sometimes called the endpoint.
The endpoint is what you hear people calling the URL, which you're not using to collect a web page.
You're using to actually call another piece of software to tell it to do something.
That's that's called the endpoint, an HTTP endpoint, just a web, a web URL that you are calling out
to and the data that you typically exchange using the most common kinds of APIs is this format called
JSON stands for JavaScript Object Notation.
You've probably seen JSON many times before, and if you haven't, then you will get somewhat used to
it on this course.
This is definitely like like whilst it is perhaps code, it is the lowest of the low code that is as
sophisticated as it's going to get.
We are going to get pretty used to looking at JSON if you're not already.
And it's also worth mentioning that typically with APIs you need to have some kind of password to say,
I want to use this API and I am who I say I am, and if you've got my credit card, then you should.
You should build this against me.
And a common way of doing it there are there are different techniques, but a simple and common one
is to use an API key, which is just a password that you have that identifies you to a third party like
open router.
And when you make your call to open router, you pass in the API key as a way of saying, hey, it's
me.
I set up my account on you, you've got my credit card.
Or maybe you don't in the case of Open Router, but you might have my credit card and and this is me
and please do this request for me.
And it responds with the answer.
That's what an API key is all about.
It's a password to use a third party API service.