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