WEBVTT 00:00.080 --> 00:03.950 And now we've arrived at an exciting moment in our first week. 00:03.980 --> 00:10.670 The conclusion of the first week is where we get to actually put this into practice and build a commercial 00:10.670 --> 00:11.570 solution. 00:11.570 --> 00:18.170 By the end of today, you will be able to confidently code against the OpenAI API, because you'll have 00:18.170 --> 00:19.430 done it several times. 00:19.430 --> 00:24.650 You'll have used a technique called one shot prompting that we'll talk about streaming markdown, JSON 00:24.650 --> 00:31.010 results, and overall we'll have implemented a business solution literally in minutes. 00:31.010 --> 00:32.750 So what is this business problem? 00:32.750 --> 00:33.650 Well, here it is. 00:33.650 --> 00:40.490 We're going to build an application that is able to generate a marketing brochure about a company. 00:40.490 --> 00:46.400 It's something that could be used for prospective clients, for investors or maybe for recruiting talent. 00:46.580 --> 00:51.740 It's going to be something which will bring together information from multiple sources. 00:52.040 --> 00:57.890 And so it's a bit like the summarization project we did before, except we're we're summarizing and 00:57.890 --> 00:59.030 we're generating. 00:59.090 --> 01:02.420 So it's sort of built on top of some stuff we did before. 01:02.450 --> 01:04.910 Now we're going to use the OpenAI API. 01:04.910 --> 01:09.200 And as before, you'd be able to switch to using Olama if you wanted to. 01:09.230 --> 01:11.960 You're now an expert in that, so I'll leave that up to you. 01:11.990 --> 01:16.130 We're going to use a technique called one shot prompting, which sounds very fancy. 01:16.130 --> 01:21.320 And all it's saying is that in the prompts we send the model, we're going to give an example of what 01:21.320 --> 01:22.100 we're looking for. 01:22.100 --> 01:25.460 We're going to tell it the kind of thing we're expecting it to reply. 01:25.490 --> 01:29.360 And when you do that with one example, it's called one shot prompting. 01:29.360 --> 01:34.490 If you if you ask a question with no examples at all, that is called zero shot prompting. 01:34.490 --> 01:38.330 It's expected just to figure out from the question how to answer one shot. 01:38.330 --> 01:43.550 Prompting is when you give one example, and then if you give multiple examples of what you're asking 01:43.550 --> 01:48.020 and what it should respond in different situations, that's known as multi-shot prompting. 01:48.170 --> 01:50.180 So that's that's all there is to it. 01:50.600 --> 01:56.390 And then we're going to be using things like streaming and formatting to make this, uh, nice, nice 01:56.390 --> 01:58.970 and impressive brochure generator. 01:59.690 --> 02:03.560 So just to remind you of the environment setup, we've done this to death. 02:03.560 --> 02:05.960 You've got an environment that works and it's fabulous. 02:05.960 --> 02:08.810 But just to remind you what you did, you cloned the repo. 02:08.840 --> 02:14.210 You followed the readme to set up your Anaconda environment, maybe a virtual env, and you set up a 02:14.210 --> 02:21.170 key with OpenAI and you put that key, the OpenAI API key, which is src proj. 02:21.560 --> 02:22.250 Blah blah blah. 02:22.280 --> 02:27.860 You put that in a file that was called dot env and it is in your project root directory. 02:27.860 --> 02:30.470 And that is why all of this is working so well. 02:30.530 --> 02:36.230 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 02:36.230 --> 02:38.150 bring up an Anaconda prompt. 02:38.150 --> 02:41.120 If you're on a mac, you bring up a terminal window. 02:41.150 --> 02:45.440 You go to the project root directory LM engineering. 02:45.440 --> 02:51.920 You type the conda, activate lm conda, activate LM to activate the environment. 02:51.920 --> 02:55.940 And then you should see the LMS prefix by your prompt. 02:55.940 --> 02:58.700 If it already says that, then you're already activated. 02:58.910 --> 03:05.330 And once you've done that, you simply type JupyterLab to launch JupyterLab and to be up and running. 03:05.330 --> 03:08.360 And that is where I will see you in the next video.