So I realized that day one of week one has been a pretty long day, and I assure you that the other,
generally speaking, the other days won't be as long.
We had some foundational work to do to get the environments up and running, and hopefully that you're
happy that we've got there and you're feeling satisfied that we ran our first big project.
As a quick recap of what we got done at the very beginning, seems like an age ago we used to run LMS
open source LMS locally on your box, running them to generate content.
Then we set up the environment, and then we used open AI in the cloud to make a call to frontier models
to GPT four.
Mini was the model we used to generate text there, and obviously we're using here a closed source model
that is maybe 1000 or 10,000 times larger.
We pay a small price for that in the form of a fraction of a cent, but we do have to to pay to use
that.
But what we get back is much richer in quality than using a small local one.
Um, we learned how to distinguish between a system prompt and a user prompt just at a high level.
We'll do a lot more on that, of course, in the coming days.
Uh, system prompt setting the tone, the context of the conversation, the user prompt, which is the
conversation itself.
We used it for the opener.
Later, we'll be using it for many rounds of conversation.
And then most importantly, we applied this to the field of summarization and a critical use case that
comes up so many times it's applicable to many different problems.
It's something that I hope you'll find ways to use this in your day job, in what you do already.
And if not, then certainly you should be able to find personal projects that you could come up with
where you could apply this.
And I'm really excited to see what people come up with.
So that's what we got done.
And would you believe we are already 2.5% through the course on the way to being an LLM engineering
expert, so it's already progress has been made.
Tomorrow we're going to talk about what really is that journey like what are the steps.
So you have a clear sense of what's what's to be done, set you up for success.
And then we'll do some, some, some content.
We'll talk about what are the leading frontier models and the different ways to use them.
And we'll also do some quick lab work, something I promised you that people who would prefer not to
fork out dollars to OpenAI.
I'm going to show you how we could use Olama as an alternative with the same code that we just wrote,
calling Olama running locally instead of calling out to the frontier model on the cloud.
So we'll do that tomorrow too.
Very much looking forward to it.
And I will see you then.