And now we've arrived at an exciting moment in our first week.
The conclusion of the first week is where we get to actually put this into practice and build a commercial
solution.
By the end of today, you will be able to confidently code against the OpenAI API, because you'll have
done it several times.
You'll have used a technique called one shot prompting that we'll talk about streaming markdown, JSON
results, and overall we'll have implemented a business solution literally in minutes.
So what is this business problem?
Well, here it is.
We're going to build an application that is able to generate a marketing brochure about a company.
It's something that could be used for prospective clients, for investors or maybe for recruiting talent.
It's going to be something which will bring together information from multiple sources.
And so it's a bit like the summarization project we did before, except we're we're summarizing and
we're generating.
So it's sort of built on top of some stuff we did before.
Now we're going to use the OpenAI API.
And as before, you'd be able to switch to using Olama if you wanted to.
You're now an expert in that, so I'll leave that up to you.
We're going to use a technique called one shot prompting, which sounds very fancy.
And all it's saying is that in the prompts we send the model, we're going to give an example of what
we're looking for.
We're going to tell it the kind of thing we're expecting it to reply.
And when you do that with one example, it's called one shot prompting.
If you if you ask a question with no examples at all, that is called zero shot prompting.
It's expected just to figure out from the question how to answer one shot.
Prompting is when you give one example, and then if you give multiple examples of what you're asking
and what it should respond in different situations, that's known as multi-shot prompting.
So that's that's all there is to it.
And then we're going to be using things like streaming and formatting to make this, uh, nice, nice
and impressive brochure generator.
So just to remind you of the environment setup, we've done this to death.
You've got an environment that works and it's fabulous.
But just to remind you what you did, you cloned the repo.
You followed the readme to set up your Anaconda environment, maybe a virtual env, and you set up a
key with OpenAI and you put that key, the OpenAI API key, which is src proj.
Blah blah blah.
You put that in a file that was called dot env and it is in your project root directory.
And that is why all of this is working so well.
And so what you need to do now in order to get us back to where we were, is if you're on a PC, you
bring up an Anaconda prompt.
If you're on a mac, you bring up a terminal window.
You go to the project root directory LM engineering.
You type the conda, activate lm conda, activate LM to activate the environment.
And then you should see the LMS prefix by your prompt.
If it already says that, then you're already activated.
And once you've done that, you simply type JupyterLab to launch JupyterLab and to be up and running.
And that is where I will see you in the next video.