WEBVTT 00:00.200 --> 00:03.650 Well, day four was an information dense day. 00:03.650 --> 00:09.500 I do hope that you learned some something useful here, and I hope that even those that were already 00:09.530 --> 00:15.440 somewhat familiar with things like tokens and context windows picked up a thing or two and are now able 00:15.440 --> 00:17.420 to more confidently put that into practice. 00:17.420 --> 00:23.150 Certainly this is foundational stuff that we'll be using again and again over the next week or next 00:23.150 --> 00:26.870 weeks as we build on this and apply it to commercial problems. 00:26.870 --> 00:32.900 So what you should now be confident doing is writing code that calls OpenAI and also a llama and using 00:32.900 --> 00:39.260 it to to summarize the summary use case that we worked on, you can now contrast the leading six frontier 00:39.260 --> 00:39.710 models. 00:39.710 --> 00:44.510 In fact, a bit more than that, because we've been exposed to zero one preview as well as GPT four 00:44.540 --> 00:47.750 zero, and to Claude artifacts and things like that. 00:48.020 --> 00:53.270 Um, and in particular, we know that almost all of them are not able to answer the question, how many 00:53.300 --> 00:56.690 A's are there in the the that sentence? 00:56.690 --> 00:59.570 And it's worth pointing out, of course, the reason they struggled with it. 00:59.570 --> 01:05.240 Now, it should be very clear to you it's because that text is tokenized by the time it's sent in to 01:05.270 --> 01:07.880 the model, and all the model knows about Is tokens. 01:07.880 --> 01:13.730 And so from that perspective, counting letters doesn't mean anything to it, because all it sees is 01:13.730 --> 01:18.440 the tokens that are already combined and they don't have the meaning of the letters. 01:18.440 --> 01:23.640 And that's why it's actually a very difficult question for an LLM, but something like zero one preview 01:23.670 --> 01:29.880 that's able to think step by step and reason and understands how things need to be spelt, is able to 01:29.880 --> 01:30.660 do it. 01:30.720 --> 01:33.390 Um, and then perplexity was also able to do it too, wasn't it. 01:33.390 --> 01:38.430 And I suspect that's because it was able to look that up in its resources of knowledge. 01:39.000 --> 01:45.750 Uh, so also now you've built on top of this to understand about the history of Transformers and how 01:45.750 --> 01:46.890 we've got to where we are. 01:46.920 --> 01:52.470 Tokens and what it means to tokenize context windows and how they're not just the input. 01:52.470 --> 01:54.540 It's the whole conversation so far. 01:54.540 --> 02:01.350 And now you know about API costs and where to go to look up the costs of APIs and the context windows 02:01.380 --> 02:04.260 associated with the big models. 02:05.160 --> 02:06.240 Okay. 02:06.270 --> 02:08.790 Next lecture is going to be exciting. 02:08.790 --> 02:10.560 You're going to be coding this time. 02:10.560 --> 02:14.970 You're going to be building some confidence in your coding against the OpenAI API. 02:15.000 --> 02:20.430 We're going to use a bunch of different techniques, and you're going to be implementing a business 02:20.430 --> 02:23.400 solution that is more of a wholesale business solution. 02:23.400 --> 02:28.290 That's going to involve a couple of different calls to LMS, and we're going to get it done in a matter 02:28.290 --> 02:28.860 of minutes. 02:28.860 --> 02:32.610 And it's a great lab, and it will end with exercise for you. 02:32.610 --> 02:39.090 So without further ado, uh, let's wrap up for today and I will see you tomorrow for our Big Week one 02:39.090 --> 02:39.960 project.