Congratulations are definitely in order.
Yesterday was a mammoth first day on this course and you got through it.
You set up your environment and we're off to the races.
So as I recap, what you can now do is use a llama to run models locally.
You can call OpenAI's API to run a frontier model.
You understand, at least at a high level, the difference between a system and a user prompt.
And you've built a summarization use case, which is an important commercial application.
Today we're going to talk a bit about what are the steps to get to being an LLM engineer, set you up
for success, and then talk more about frontier models.
That is the plan.
Now, let me start by just emphasizing that this course, this eight week course is a practical course.
First and foremost we're going to cover theory.
We're going to cover the foundational information that you need.
But we're always going to do it in a practical context.
I'm a big believer in the best way to learn is by doing.
And that's what we'll be actually doing.
And we're going to be putting it to good use.
We're going to be building commercial projects as we go.
And I'll often be asking you to try and think about how you could apply what you've learned to your
day job and try and build little prototypes to bring that to life.
So that is the flavour of the course.
There are three different aspects to LM engineering that you'll be picking up as we go.
The first of them is just understanding the models that are out there, the wide range of different
types of LM and what they're capable of, whether we're talking about open source versions, the closed
source paid versions, ones that are multi-modal, that can generate images or audio, the different
architectures of LMS, and importantly, how you pick which LM you should be working with in the first
place.
We'll be looking at all of the tools of the trade, things like the ubiquitous hugging face, the super
impressive glue code in Lange chain, the fabulous gradio that you will see.
I'm a big fan of weights and biases.
Super helpful and then modal for deploying it to production.
And we'll be looking at techniques different ways that you can apply this.
The field of AI to solve business problems from using API's Rag which is such a hot topic, fine tuning
and then at the end a full Agentic AI solution.
The idea of this course is that I've planned it so that it will apply almost no matter what your level
of experience.
You may find initially that some of the first few weeks are too simplistic for you, or the first few
days at least.
And there I would say hang in there, use it as a way to just sort of reinforce some of the of the foundational
information that we'll talk a bit more detail about things like tokens than you've done before, perhaps.
So there'll be stuff to pick up, but you can go through it quickly and then make the projects your
own.
So use it as a way to build deeper versions of what we do and then prepare for harder, fun projects
coming up later.
If it feels too challenging, then please, please don't worry.
Take your time with the practicals.
Take your time with the exercises.
Work your way through them.
There are some extra guides that I'll talk about in week one that would help give you a sort of firmer
footing if you need it.
And please, please, please ask for help.
I am here, I respond quickly.
You can always reach out to me, either as I say on the platform or through email or through LinkedIn.
Details are in the GitHub repo.
Reach out to me.
Get help.
That is what I'm here for.
And then if it feels just right, then excellent.
Keep going.
So the prerequisite is beginner to intermediate level Python.
And if you have intermediate level Python you're going to find it easiest and you'll get the most out
of it.
And if you look for example at this line of code right here, which is just a random line from within
a function.
If you basically know what that's probably doing, then you're in great shape if you know exactly what
it's doing and if you know in fact why, it's not perhaps the most optimal way of doing it, then you're
more than more than, well set that you're advanced, and that's great if you don't know what this does
and you're not familiar with a world with a word like yield or set or the dot get, then there is a
special notebook, a special Jupyter Lab in week one for you, which is a guide to to Python at this
level.
And as you go through that notebook, I will take you through each of the stepping stones until we get
to a point where a line like this should make sense.
And of course, you can also use ChatGPT and Claude.
The the the genies are really good at explaining code.
And indeed, if you put something like this in there, they would tell you exactly what it does and
why and step you through it too.
And they're probably just as good as, as my, my notebook.
So either way, that should give you what you need to do.
And at any point, if you don't understand some code, you can always use ChatGPT.
So to get the most out of this course, there are a few things that I would ask you.
First of all, follow along as I do my coding.
So when I'm when I'm going through in the labs and I'm executing cells, then either at the same time
or afterwards go through and do it yourself.
And if you hit snafus, which you might do for various reasons, then have a crack at trying to to sort
out why.
Do some debugging.
That's a great way to learn and complete the exercises, and then put your code examples up on GitHub.
It's actually a great way as well.
If you're new to this space, and you're trying to build up something of a kind of resume to show that
you've built some of this experience, the best kind of resume you can have is a GitHub repo, because
people will look at it and will see the sorts of things you've worked on.
And of course, you don't want to put exactly the projects we do.
You want to make them your own.
You want to figure out, okay, how can I apply this to my business area or to a personal project that
I'm working on to make it something that's similar, slightly different?
Take on it for new business value.
That is a great way to get the most out of this course, and then look to share your code.
If you're happy with it and you and you're okay with that.
I've got instructions about how you can submit a pull request, which means that I can see your code.
I can give you feedback on it if you'd like, and I can also then republish it so that other students
taking the course will see your examples and we can all share in it together.
And of course stick at it.
Hang on in there.
This course gets better and better and better, I assure you.
And I you know, there's there's going to be so many projects, so much commercial application.
And I definitely encourage you to stay the course.
And the thing I didn't put down here one more time is that getting the most out of the course.
Also, please reach out to me if I can help at any point.