-: Hi, I just wanna give you
an inside view of the curriculum.
So this is how we think about the course,
and it's got a lot in here.
So I'm just gonna walk you through my intent,
like what we were trying to do
with each checks of the course.
And this might change by the way,
but we keep it updated every month.
I think we add new content
and sometimes we film the old content
as well as it gets out of date.
So if you spot anything, by the way, just let us know.
We have 150,000 students
and every one of them gives good feedback.
So we appreciate that.
We start with the introduction,
and this is just getting started,
teaches you what is prompt engineering.
We have a few different resources and things here as well.
And then also the GitHub repository for the course.
We try and keep everything in there.
Then we get into the five principles of prompting.
And this is the most important thing in the course.
You'll see these techniques everywhere,
but this is a checklist that you can go through
and once you know how prompting works,
like from this section,
you'll be able to understand most of the other sections.
There's also a section here on how does AI work.
If you're not too interested in this, you can skip it.
That's totally fine.
And then same thing for this section here.
We're going into each feature of ChatGPT,
just giving you a bit of an introduction to them.
And if you haven't seen some of these things,
feel free to watch them,
but you also don't feel compelled to go into it
if you're already an AI expert.
Now, where it starts to get really interesting
is in the standard text model practices.
Typically we call it a standard practice
if it doesn't need code.
And about 30, 40% of the videos don't really need code.
And then we call it a advanced technique
if you need to know how to code
and we'll put the -Coding on the end of each one.
There's also a section here
on GPT-4 and how to use the Playground,
which is needed before the advanced techniques.
And then we go into LangChain.
Love it or hate it, it is the most popular framework
and most of the other frameworks
are in some way inspired by it.
So I think it's really worth learning
and you can just look under the hood
and see how they do certain things
'cause they do follow pretty good best practices
and there's a lot in here.
So again, just dig into the things
that you find interesting.
We also go into LangGraph,
which I think is actually their best product,
but there's a lot more to say about that.
I'll let you dig into that section.
We then do some real-world projects.
So these are actual things that we've done for clients,
specific videos for each type of project.
We also even help you build
your own GPT wrapper if you need as well.
So enjoy that section.
There's a ton in there.
And then we talk about Midjourney
'cause that's the main model
we're using for some of these simpler sections.
We also talk a bit more about image prompting in here.
So quite useful section,
before we get into the harder image generation stuff.
Reason it's harder is just because setting up
Stable Diffusion in the WebUI is a bit of a pain.
There, we do have some links
in this lecture for you to follow.
And then also how to migrate
to Stable Diffusion Excel because most of the content
is based on 1.5, which is still a very popular model
even though it's quite old
because it's the most flexible and trainable.
But a lot of people are starting to switch to Excel.
All this section is still useful for Excel as well.
We're really teaching you the individual techniques
like inpainting, ControlNet, Segment Anything.
And then we have DreamBooth here as well.
That's fine-tuning.
And then we do some image model projects.
So these are real-world projects that we've done.
And then get into evals.
And when you are in the real world
and you're doing prompt optimization
or you're a prompt engineer as your job title,
you're gonna be doing a lot of this.
This is probably 80%
of the work is finding
the right performance metrics and then optimizing to them.
So take a look at that section.
That's my personal favorite section.
We also talk a bit about agent architectures,
which are starting to get more and more important.
AI agents is the next big topic after prompt engineering,
but then we cover
all the other kind of things that we missed.
You can jump ahead to this section by the way
if you want to know a bit more about Claude
or dive into DALL-E or some of the other models.
Like we talk a lot about the Google stuff here.
In particular, Gemini to native image generation
I think is worth looking at.
We cover a few different tools we've tried.
We haven't tried that many tools that we liked,
to be honest.
So this is really only the stuff
that we've found useful and we keep this section updated.
Some of these are some prompt optimization libraries,
which are quite cool.
And then we go a little bit into GitHub Copilot.
So it teaches a little bit
about how to prompt better
when you are using AI for code generation.
And then the conclusion, by the way,
you get a free chapter of our book.
We publish a book through O'Reilly,
so feel free to download that PDF
and see how different the content is.
But we also give you links here
to different sources of inspiration,
things that inspired us when we're creating this course,
'cause there is a lot of really good content out there.
Cool, okay, hopefully that gives you a good understanding
of what we're trying to do
with the course and what's contained inside it.
We do try and keep it updated, so this is subject to change.