WEBVTT 00:01.010 --> 00:01.580 Well. 00:01.580 --> 00:02.330 Hello there. 00:02.360 --> 00:04.550 Look, I know what you're thinking. 00:04.580 --> 00:07.070 You're thinking I peaked too early. 00:07.100 --> 00:09.950 Last week was an amazing climax. 00:09.950 --> 00:13.430 We built a model that massively outperformed the frontier. 00:13.460 --> 00:15.290 It can't get better than that. 00:15.320 --> 00:17.000 Well, I've got news for you. 00:17.090 --> 00:18.860 It's going to get better than that. 00:18.890 --> 00:22.670 We are actually going to have the best week yet. 00:22.670 --> 00:24.680 And it's just getting started. 00:24.860 --> 00:29.960 So this week, this week it's about reaching the pinnacle. 00:29.960 --> 00:33.770 It's about becoming a master of LM engineering. 00:33.770 --> 00:34.970 And we're going to do it. 00:35.000 --> 00:36.680 We're going to have a blast. 00:36.680 --> 00:38.510 It's going to be epic. 00:38.870 --> 00:44.030 Uh, we are going to build a multi-agent framework. 00:44.030 --> 00:48.290 We're going to build a solution that is going to be really, really awesome. 00:48.290 --> 00:54.740 And yeah, it's going to do something profound and it's going to do it using everything that we've been 00:54.740 --> 00:56.840 building for the last eight weeks. 00:57.140 --> 01:02.750 Uh, for today, though, we're going to be using modal, the serverless platform for AI, so that we 01:02.750 --> 01:09.390 can deploy the same model that we built last week, our proprietary Three specialized alum, we're going 01:09.390 --> 01:18.120 to be able to deploy it behind a serverless API on the cloud, and we're going to build the first agent 01:18.150 --> 01:18.930 of many. 01:18.960 --> 01:24.390 The first of the seven agents that are going to be part of the solution that we are building this week 01:24.510 --> 01:27.150 to solve a juicy business problem. 01:27.150 --> 01:35.550 So without further ado, let's get into week eight, the final week of mastering LLM engineering. 01:35.790 --> 01:40.110 And of course, it wouldn't be the start of a week without this picture. 01:40.110 --> 01:44.640 For the last time, let's look at the journey that you've been on. 01:44.670 --> 01:54.210 You started or seven weeks ago, uh, you first uh, we we spent some time looking at frontier models, 01:54.330 --> 02:00.150 uh, looking at asking questions, asking how many times that A appeared in this sentence, if you can 02:00.150 --> 02:03.540 remember that, uh, in week two, we built UIs. 02:03.540 --> 02:05.850 We experienced gradio for the first time. 02:05.850 --> 02:08.520 We worked with agent ization in a light way. 02:08.520 --> 02:10.080 It's going to get a lot heavier. 02:10.200 --> 02:16.340 Uh, and we played around with Multi-modality in week three, we went open source with hugging face 02:16.340 --> 02:19.760 with tokenization, with with models. 02:20.150 --> 02:26.930 In week four, we built that amazing code generation tool that rewrote Python a, C plus, plus. 02:26.930 --> 02:32.000 And in doing so, we explored how you select the right LLM to solve a business problem. 02:32.060 --> 02:37.760 In week five, we tackled Rag using Lang Chain and built a question answering project. 02:37.790 --> 02:39.890 Maybe you built something bigger as well. 02:39.920 --> 02:41.540 In week six. 02:41.540 --> 02:48.380 Week six was when we started on training and we fine tuned a frontier model and week seven. 02:48.410 --> 02:55.460 Of course, week seven we've just come from was when we beat that frontier model using Cultura fine 02:55.460 --> 03:00.410 tuning to make our own Verticalized specialized LLM. 03:00.890 --> 03:03.650 And I loved it and I hope you did too. 03:03.680 --> 03:10.430 And so this week, week eight, the culmination of everything that's come before is when we have a finale. 03:10.460 --> 03:18.860 We must LLM engineering and we're going to do it by focusing on Agentic AI and building an agent platform. 03:19.520 --> 03:21.350 Let's talk more about that next time.