Well, day four was an information dense day.
I do hope that you learned some something useful here, and I hope that even those that were already
somewhat familiar with things like tokens and context windows picked up a thing or two and are now able
to more confidently put that into practice.
Certainly this is foundational stuff that we'll be using again and again over the next week or next
weeks as we build on this and apply it to commercial problems.
So what you should now be confident doing is writing code that calls OpenAI and also a llama and using
it to to summarize the summary use case that we worked on, you can now contrast the leading six frontier
models.
In fact, a bit more than that, because we've been exposed to zero one preview as well as GPT four
zero, and to Claude artifacts and things like that.
Um, and in particular, we know that almost all of them are not able to answer the question, how many
A's are there in the the that sentence?
And it's worth pointing out, of course, the reason they struggled with it.
Now, it should be very clear to you it's because that text is tokenized by the time it's sent in to
the model, and all the model knows about Is tokens.
And so from that perspective, counting letters doesn't mean anything to it, because all it sees is
the tokens that are already combined and they don't have the meaning of the letters.
And that's why it's actually a very difficult question for an LLM, but something like zero one preview
that's able to think step by step and reason and understands how things need to be spelt, is able to
do it.
Um, and then perplexity was also able to do it too, wasn't it.
And I suspect that's because it was able to look that up in its resources of knowledge.
Uh, so also now you've built on top of this to understand about the history of Transformers and how
we've got to where we are.
Tokens and what it means to tokenize context windows and how they're not just the input.
It's the whole conversation so far.
And now you know about API costs and where to go to look up the costs of APIs and the context windows
associated with the big models.
Okay.
Next lecture is going to be exciting.
You're going to be coding this time.
You're going to be building some confidence in your coding against the OpenAI API.
We're going to use a bunch of different techniques, and you're going to be implementing a business
solution that is more of a wholesale business solution.
That's going to involve a couple of different calls to LMS, and we're going to get it done in a matter
of minutes.
And it's a great lab, and it will end with exercise for you.
So without further ado, uh, let's wrap up for today and I will see you tomorrow for our Big Week one
project.