WEBVTT 00:00.080 --> 00:03.050 And with that, we've reached an important milestone. 00:03.080 --> 00:07.940 The first week of our eight week journey is complete, and there's been an awful lot covered. 00:07.970 --> 00:10.490 And congratulations for making it to this point. 00:10.520 --> 00:16.160 At this point, just to recap, you're in a position to describe the history of Transformers and the 00:16.160 --> 00:17.540 shocking last few years. 00:17.540 --> 00:23.210 The use of tokens, tokenization, the importance of context windows and exactly what that means, where 00:23.240 --> 00:27.230 to go to look up API costs, and a lot more like that. 00:27.410 --> 00:32.780 You've really got hands on experience looking at a bunch of different frontier models, both the big 00:32.780 --> 00:37.400 six companies, but also some of the models within them and some of the very latest innovations. 00:37.400 --> 00:42.770 And we've seen how something like count, how many times letter A appears in the sentence is something 00:42.770 --> 00:45.890 surprisingly hard for Llms, even some of the very top ones. 00:45.920 --> 00:50.660 And now, with your understanding of tokenization, you probably have a very good sense as to why. 00:50.720 --> 00:57.590 And then most importantly, you're at this point, hopefully you are confidently able to use the OpenAI 00:57.620 --> 01:02.070 API, Including adding in things like streaming and markdown. 01:02.070 --> 01:07.920 You've built your own tool with the assignment, and you've also used the exercise we did. 01:07.950 --> 01:12.330 You made multiple calls to LMS and you've played around with the system prompts. 01:12.330 --> 01:18.900 You've got a good understanding of how you can use the system prompt for things like setting tone, 01:18.900 --> 01:22.680 character of the response, as well as giving the specific instructions. 01:22.800 --> 01:30.330 And you also understand about using both single shot and Multi-shot prompting as a way to get more accurate, 01:30.330 --> 01:33.930 robust, repeatable results from the LM. 01:34.230 --> 01:42.960 And to boot, you've also added in using the llama API to call the models running on your box directly. 01:42.960 --> 01:45.780 It's not something that we'll be doing, particularly going forwards. 01:45.780 --> 01:47.760 When we get to using open source models. 01:47.760 --> 01:53.340 We'd rather use hugging face code when we can actually really get into the internals and start examining 01:53.340 --> 01:54.900 things like tokens and stuff. 01:54.990 --> 01:59.340 Um, but at any point you can always flip to using the Ulama API. 01:59.370 --> 02:02.730 If you would like to reduce API costs. 02:02.880 --> 02:10.950 So with that, that would be a wrap for a very substantive week, one with a lot of ground covered. 02:11.190 --> 02:16.800 Next week we will be getting into using APIs for all of the frontier models. 02:16.800 --> 02:21.240 So we'll be using OpenAI and Anthropic and Gemini. 02:21.270 --> 02:23.490 You'll do some work a little bit more. 02:23.520 --> 02:30.300 Another step in the direction of agent ization of agentic AI with a little bit more work on agents. 02:30.300 --> 02:37.470 But most importantly, we're going to be building some data science UIs using the fabulous Gradio platform 02:37.470 --> 02:38.430 that I love. 02:38.460 --> 02:44.730 And we'll be doing that, including building a complete multimodal customer support agent that is able 02:44.730 --> 02:51.240 to do things like show pictures and make audio and use tools where it calls into your computer. 02:51.270 --> 02:53.220 So a lot to cover next week. 02:53.220 --> 02:56.670 It's going to be a super exciting week and I can't wait to see you there.