WEBVTT 00:00.110 --> 00:06.500 So I realized that day one of week one has been a pretty long day, and I assure you that the other, 00:06.500 --> 00:09.500 generally speaking, the other days won't be as long. 00:09.560 --> 00:14.930 We had some foundational work to do to get the environments up and running, and hopefully that you're 00:14.930 --> 00:19.730 happy that we've got there and you're feeling satisfied that we ran our first big project. 00:19.730 --> 00:27.200 As a quick recap of what we got done at the very beginning, seems like an age ago we used to run LMS 00:27.200 --> 00:33.500 open source LMS locally on your box, running them to generate content. 00:33.770 --> 00:41.540 Then we set up the environment, and then we used open AI in the cloud to make a call to frontier models 00:41.540 --> 00:42.560 to GPT four. 00:42.800 --> 00:49.670 Mini was the model we used to generate text there, and obviously we're using here a closed source model 00:49.670 --> 00:53.300 that is maybe 1000 or 10,000 times larger. 00:53.540 --> 00:59.420 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 00:59.420 --> 00:59.750 that. 00:59.750 --> 01:06.500 But what we get back is much richer in quality than using a small local one. 01:06.890 --> 01:12.590 Um, we learned how to distinguish between a system prompt and a user prompt just at a high level. 01:12.590 --> 01:15.710 We'll do a lot more on that, of course, in the coming days. 01:15.770 --> 01:21.860 Uh, system prompt setting the tone, the context of the conversation, the user prompt, which is the 01:21.860 --> 01:23.240 conversation itself. 01:23.270 --> 01:24.730 We used it for the opener. 01:24.760 --> 01:28.030 Later, we'll be using it for many rounds of conversation. 01:28.030 --> 01:35.320 And then most importantly, we applied this to the field of summarization and a critical use case that 01:35.320 --> 01:39.550 comes up so many times it's applicable to many different problems. 01:39.550 --> 01:44.260 It's something that I hope you'll find ways to use this in your day job, in what you do already. 01:44.260 --> 01:49.090 And if not, then certainly you should be able to find personal projects that you could come up with 01:49.090 --> 01:50.440 where you could apply this. 01:50.440 --> 01:53.110 And I'm really excited to see what people come up with. 01:53.530 --> 01:55.480 So that's what we got done. 01:55.480 --> 02:02.950 And would you believe we are already 2.5% through the course on the way to being an LLM engineering 02:03.190 --> 02:07.000 expert, so it's already progress has been made. 02:07.030 --> 02:12.460 Tomorrow we're going to talk about what really is that journey like what are the steps. 02:12.460 --> 02:16.600 So you have a clear sense of what's what's to be done, set you up for success. 02:16.600 --> 02:18.910 And then we'll do some, some, some content. 02:18.910 --> 02:22.870 We'll talk about what are the leading frontier models and the different ways to use them. 02:22.870 --> 02:28.900 And we'll also do some quick lab work, something I promised you that people who would prefer not to 02:28.930 --> 02:31.630 fork out dollars to OpenAI. 02:31.660 --> 02:37.360 I'm going to show you how we could use Olama as an alternative with the same code that we just wrote, 02:37.360 --> 02:43.870 calling Olama running locally instead of calling out to the frontier model on the cloud. 02:43.870 --> 02:46.030 So we'll do that tomorrow too. 02:46.060 --> 02:47.500 Very much looking forward to it. 02:47.500 --> 02:48.670 And I will see you then.