WEBVTT 00:00:00.000 --> 00:00:02.500 Okay, today we are diving deep into something 00:00:02.500 --> 00:00:06.120 that's absolutely foundational to the AI tools 00:00:06.120 --> 00:00:08.160 changing our world right now. Large language 00:00:08.160 --> 00:00:13.800 models, LLMs. And they truly are the engine behind 00:00:13.800 --> 00:00:16.320 so much of what's called generative AI. You know, 00:00:16.399 --> 00:00:19.472 the AI that isn't just analyzing data but actually... 00:00:19.469 --> 00:00:22.609 Creating new stuff exactly creating and our source 00:00:22.609 --> 00:00:24.750 material for this deep dive It's a really solid 00:00:24.750 --> 00:00:27.429 article from hack science dot education called 00:00:27.429 --> 00:00:29.769 large language models Yeah, it's a good one and 00:00:29.769 --> 00:00:31.789 our mission today is really to cut through the 00:00:31.789 --> 00:00:34.969 noise Pull out the essential concepts and give 00:00:34.969 --> 00:00:38.539 you those genuine aha moments about how these 00:00:38.539 --> 00:00:40.520 seemingly magical tools actually work without 00:00:40.520 --> 00:00:42.100 getting totally lost in the weeds, you know? 00:00:42.159 --> 00:00:43.939 Right. Think of it as getting the core operating 00:00:43.939 --> 00:00:46.359 manual. Just explain clearly. Love that because, 00:00:46.560 --> 00:00:48.219 yeah, sometimes they feel like pure magic. So, 00:00:48.219 --> 00:00:50.679 all right, let's unpack this. At the most fundamental 00:00:50.679 --> 00:00:55.299 level, what are these LLMs? OK. So at their core, 00:00:55.939 --> 00:00:59.100 LLMs represent a pretty significant leap forward 00:00:59.100 --> 00:01:02.399 in AI. They're specifically designed to generate, 00:01:02.399 --> 00:01:06.000 well, human -like text. But the really crucial 00:01:06.000 --> 00:01:08.760 idea here, I think, is that they're built upon 00:01:08.760 --> 00:01:11.400 what are called foundational models. Foundational 00:01:11.400 --> 00:01:13.680 models. Okay, that sounds important. Why? They 00:01:13.680 --> 00:01:15.719 really are. Because these foundational models, 00:01:15.760 --> 00:01:17.780 they're trained on just absolutely massive amounts 00:01:17.780 --> 00:01:20.859 of diverse data. This gives them a broad general 00:01:20.859 --> 00:01:24.000 knowledge base. And this foundation enables something 00:01:24.000 --> 00:01:26.180 powerful called transfer learning. Transfer learning, 00:01:26.200 --> 00:01:27.799 okay. Yeah, so instead of building a completely 00:01:27.799 --> 00:01:30.140 new model for every single task, you can take 00:01:30.140 --> 00:01:32.790 this one foundational model and sort of Adapt 00:01:32.790 --> 00:01:35.909 it for many different things. Ah, okay So one 00:01:35.909 --> 00:01:38.629 giant model train like crazy can then pivot to 00:01:38.629 --> 00:01:41.590 writing a screenplay or summarizing a legal doc 00:01:41.590 --> 00:01:45.129 or maybe even generating computer code that Versatility 00:01:45.129 --> 00:01:47.290 comes from the foundational model and that this 00:01:47.290 --> 00:01:50.290 transfer learning precisely. It's a unified approach 00:01:50.290 --> 00:01:52.569 that allows for incredible flexibility across 00:01:52.569 --> 00:01:55.590 domains And, you know, that deep broad training 00:01:55.590 --> 00:01:57.650 also makes them surprisingly good at spotting 00:01:57.650 --> 00:02:00.030 nuances like grammar issues or inconsistencies. 00:02:00.370 --> 00:02:03.349 The key word really is generative. They create 00:02:03.349 --> 00:02:05.909 new content based on all that knowledge. OK, 00:02:06.030 --> 00:02:08.509 creating new text. But how? I mean, how do they 00:02:08.509 --> 00:02:11.270 take all that training data and actually produce 00:02:11.270 --> 00:02:15.009 coherent, you know, human sounding sentences? 00:02:15.250 --> 00:02:16.949 Well, the core mechanic is actually surprisingly 00:02:16.949 --> 00:02:19.430 simple to grasp, conceptually at least. The scale 00:02:19.430 --> 00:02:24.490 is mind boggling. But the idea is this. LLMs 00:02:24.490 --> 00:02:27.310 work by predicting the probability of the next 00:02:27.310 --> 00:02:29.949 word in a sequence based on the words that came 00:02:29.949 --> 00:02:33.330 before it. They learn these patterns by analyzing 00:02:33.330 --> 00:02:36.449 truly vast amounts of text data during training, 00:02:37.069 --> 00:02:39.289 basically, soaking up books, articles, websites, 00:02:39.830 --> 00:02:42.090 just all sorts of written material. So it's constantly 00:02:42.090 --> 00:02:44.289 thinking, OK, given these words, what's the most 00:02:44.289 --> 00:02:47.050 likely next word to follow? Exactly. And during 00:02:47.050 --> 00:02:49.110 that training process, the model is constantly 00:02:49.110 --> 00:02:51.979 tweaking its internal structure. potentially 00:02:51.979 --> 00:02:54.479 trillions of tiny connections to make those predictions 00:02:54.479 --> 00:02:56.719 more accurate, closer to the patterns it sees 00:02:56.719 --> 00:02:59.360 in real text. So when you ask it to generate 00:02:59.360 --> 00:03:02.060 something, it typically picks the word with the 00:03:02.060 --> 00:03:04.780 highest probability, then uses that word as part 00:03:04.780 --> 00:03:07.120 of the sequence to predict the next one, and 00:03:07.120 --> 00:03:10.659 so on and so on. Word by word, building the response 00:03:10.659 --> 00:03:13.020 based on probability. It's kind of like auto 00:03:13.020 --> 00:03:15.159 -correct on steroids, right? Predicting the best 00:03:15.159 --> 00:03:18.360 next step in a really long chain. Yeah, that's 00:03:18.360 --> 00:03:21.080 not a bad analogy. But. processing that much 00:03:21.080 --> 00:03:23.560 data and making those predictions effectively. 00:03:24.539 --> 00:03:26.900 That requires some serious technical horsepower, 00:03:27.080 --> 00:03:29.599 doesn't it? Oh, absolutely. And the real breakthrough, 00:03:29.719 --> 00:03:32.580 the kind of aha moment here, was the development 00:03:32.580 --> 00:03:35.039 of a specific architecture called the transformer 00:03:35.039 --> 00:03:38.080 architecture. Transformer. Yes. This is what 00:03:38.080 --> 00:03:40.460 fundamentally enabled the creation of these powerful 00:03:40.460 --> 00:03:43.060 foundational models and consequently the LMMs 00:03:43.060 --> 00:03:45.280 built upon them. So that's the secret sauce. 00:03:45.610 --> 00:03:48.509 the transformer. In large part, yeah. It really 00:03:48.509 --> 00:03:51.030 revolutionized how models handle sequences of 00:03:51.030 --> 00:03:53.830 data like text. It allowed them to weigh the 00:03:53.830 --> 00:03:55.969 importance of different words in the input sequence 00:03:55.969 --> 00:03:59.909 much more effectively than previous architectures 00:03:59.909 --> 00:04:02.150 could. Okay, so the transformer lets them read 00:04:02.150 --> 00:04:05.930 and predict effectively at scale. Got it. So 00:04:05.930 --> 00:04:08.289 what tasks can we actually use them for? What 00:04:08.289 --> 00:04:10.810 does this predictive capability really unlock? 00:04:11.210 --> 00:04:13.250 Well, because they are essentially foundational 00:04:13.250 --> 00:04:15.509 models adapted specifically for natural language 00:04:15.509 --> 00:04:19.350 processing or NLP and language generation, their 00:04:19.350 --> 00:04:22.050 capabilities are incredibly broad. The power 00:04:22.050 --> 00:04:25.129 really lies in them being general purpose. They 00:04:25.129 --> 00:04:27.269 can tackle many different tasks without needing 00:04:27.269 --> 00:04:29.870 entirely separate models or, you know, vast amounts 00:04:29.870 --> 00:04:32.509 of specific training data for each individual 00:04:32.509 --> 00:04:34.730 one. Okay, so give us some examples then, maybe 00:04:34.730 --> 00:04:36.449 from the source. What kind of things can they 00:04:36.449 --> 00:04:39.610 actually do? Sure. With the right input, they 00:04:39.610 --> 00:04:42.689 can answer questions, draft documents like essays 00:04:42.689 --> 00:04:45.449 or reports, summarize lengthy texts, translate 00:04:45.449 --> 00:04:47.550 between languages, and even, as you mentioned, 00:04:47.889 --> 00:04:51.149 generate computer code. Generate code. That versatility 00:04:51.149 --> 00:04:53.870 is just kind of mind -blowing. It is. And their 00:04:53.870 --> 00:04:56.370 real -world applicability spans across industries. 00:04:56.910 --> 00:04:59.029 Think about summarizing customer feedback or 00:04:59.029 --> 00:05:02.050 classifying emails, powering sophisticated chatbots, 00:05:02.209 --> 00:05:04.290 drafting marketing copy, assisting with data 00:05:04.290 --> 00:05:08.180 analysis, or even extracting specific like names 00:05:08.180 --> 00:05:10.959 or dates from large documents. They can be applied 00:05:10.959 --> 00:05:13.519 to almost any task that involves text, really. 00:05:13.800 --> 00:05:15.779 They sound incredibly capable, almost like they 00:05:15.779 --> 00:05:18.279 know everything. But they don't, do they? There 00:05:18.279 --> 00:05:20.819 must be limitations. Oh, absolutely. A critical 00:05:20.819 --> 00:05:23.379 limitation to understand is the training cutoff. 00:05:23.779 --> 00:05:26.629 This refers to a specific date. The model's knowledge 00:05:26.629 --> 00:05:28.870 base is built only from the data it was trained 00:05:28.870 --> 00:05:31.889 on up until that point in time. So if a model's 00:05:31.889 --> 00:05:36.230 training data ended in, say, late 2023, it won't 00:05:36.230 --> 00:05:38.230 know about anything significant that happened 00:05:38.230 --> 00:05:41.329 in 2024. Precisely. It has zero information about... 00:05:41.310 --> 00:05:44.410 events, discoveries, cultural shifts, anything 00:05:44.410 --> 00:05:46.930 that occurred after that cutoff date. Its understanding 00:05:46.930 --> 00:05:49.709 is basically a snapshot of the world as it existed 00:05:49.709 --> 00:05:51.910 when its training finished. Right. That's why 00:05:51.910 --> 00:05:54.129 you can't ask most public LLMs about the latest 00:05:54.129 --> 00:05:56.069 news or very recent scientific breakthroughs 00:05:56.069 --> 00:05:58.389 and expect an accurate up to the minute answer. 00:05:58.670 --> 00:06:00.750 That's a super important point for anyone using 00:06:00.750 --> 00:06:04.029 them. Definitely. OK, so they're based on foundational 00:06:04.029 --> 00:06:06.470 models. They predict words. They use transformers. 00:06:06.490 --> 00:06:09.230 They have this knowledge cutoff date. Are all 00:06:09.230 --> 00:06:12.050 LLMs fundamentally the same, though? Or are there 00:06:12.050 --> 00:06:14.730 variations? Yeah, the source highlights a few 00:06:14.730 --> 00:06:17.529 distinct types. First, you have the base LLMs 00:06:17.529 --> 00:06:19.430 we've mostly been discussing, the versatile general 00:06:19.430 --> 00:06:21.670 purpose models. They're good at many things, 00:06:22.170 --> 00:06:25.269 but might not be perfectly tuned for very specific 00:06:25.269 --> 00:06:28.329 or highly reliable enterprise tasks without some 00:06:28.329 --> 00:06:30.189 further work. OK, the standard model. Got it. 00:06:30.370 --> 00:06:32.660 Then you have instruction -based LLMs. These 00:06:32.660 --> 00:06:34.959 also use a base model, but they're designed to 00:06:34.959 --> 00:06:37.319 follow explicit instructions given in your prompt 00:06:37.319 --> 00:06:40.319 very, very directly, like write this in the style 00:06:40.319 --> 00:06:42.680 of Shakespeare or extract the key dates from 00:06:42.680 --> 00:06:45.100 this text. You're giving it clear commands. Ah, 00:06:45.100 --> 00:06:46.759 so you're being very specific about how you want 00:06:46.759 --> 00:06:49.160 it to generate the output. Exactly. And then 00:06:49.160 --> 00:06:51.500 there are fine -tuned LLMs. This is where you 00:06:51.500 --> 00:06:54.259 take a base model and you train it further on 00:06:54.259 --> 00:06:56.860 a smaller, highly specific data set, something 00:06:56.860 --> 00:06:59.699 related to a particular task or domain. So like 00:06:59.699 --> 00:07:02.579 you might fine -tune a base model using thousands 00:07:02.579 --> 00:07:05.220 of medical research papers, maybe, or internal 00:07:05.220 --> 00:07:07.980 company documents. Right, exactly. The goal is 00:07:07.980 --> 00:07:10.019 to make it exceptionally accurate or helpful 00:07:10.019 --> 00:07:12.819 for that specific area, improving its performance 00:07:12.819 --> 00:07:15.339 beyond what the general base model could achieve 00:07:15.339 --> 00:07:17.360 on its own. Now this does require additional 00:07:17.360 --> 00:07:19.600 data and training time, of course, but it results 00:07:19.600 --> 00:07:22.220 in a more specialized model. Base, instruction 00:07:22.220 --> 00:07:24.339 -based and fine -tuned. That structure makes 00:07:24.339 --> 00:07:26.959 a lot of sense. Now I've also been hearing quite 00:07:26.959 --> 00:07:29.550 a bit about small language models or SLMs? Are 00:07:29.550 --> 00:07:32.110 they just like smaller versions of these big 00:07:32.110 --> 00:07:34.610 ones? Essentially, yes. SLMs are basically scaled 00:07:34.610 --> 00:07:37.490 down LLMs. They're designed to be more resource 00:07:37.490 --> 00:07:40.470 efficient, easier to deploy, more accessible, 00:07:40.810 --> 00:07:42.829 while still providing many of the benefits you 00:07:42.829 --> 00:07:44.889 get from the larger models. So maybe they're 00:07:44.889 --> 00:07:48.370 faster, cheaper, but maybe less capable than 00:07:48.370 --> 00:07:50.509 the giants like GPT -4. Is that the tradeoff? 00:07:50.730 --> 00:07:53.449 Well, they are generally smaller, yes, and require 00:07:53.449 --> 00:07:55.930 fewer computational resources, lower cost to 00:07:55.930 --> 00:07:58.699 train and operate. And techniques like knowledge 00:07:58.699 --> 00:08:00.660 distillation, where you train a smaller model 00:08:00.660 --> 00:08:03.279 to mimic a larger one and transfer learning, 00:08:03.759 --> 00:08:06.560 help them perform pretty well on tasks like analysis, 00:08:06.939 --> 00:08:09.579 translation, summarization. But here's where 00:08:09.579 --> 00:08:12.240 it gets really interesting, and this is a significant 00:08:12.240 --> 00:08:15.759 aha from the source material. The success of 00:08:15.759 --> 00:08:18.019 some SLMs, like the PHY series from Microsoft, 00:08:18.060 --> 00:08:20.500 for example, isn't just about being smaller. 00:08:20.980 --> 00:08:24.319 It's about strategic data selection. Strategic 00:08:24.319 --> 00:08:26.199 data, you mean they don't just use all the data 00:08:26.199 --> 00:08:28.759 they can find? No. And this kind of flipped a 00:08:28.759 --> 00:08:31.180 lot of conventional thinking on its head. Instead 00:08:31.180 --> 00:08:33.759 of just prioritizing sheer quantity of training 00:08:33.759 --> 00:08:37.159 data, they focus intensely on quality. Quality 00:08:37.159 --> 00:08:39.720 over quantity. Exactly. This involved using things 00:08:39.720 --> 00:08:43.019 like textbook quality educational data, carefully 00:08:43.019 --> 00:08:45.919 curated synthetic data sets, and really select 00:08:45.919 --> 00:08:48.240 web data. So it's like teaching a student with, 00:08:48.240 --> 00:08:50.320 I don't know, high quality textbooks instead 00:08:50.320 --> 00:08:52.379 of just letting them browse the entire internet 00:08:52.379 --> 00:08:54.909 randomly. That's a great analogy, actually. This 00:08:54.909 --> 00:08:57.529 approach focused on building common sense reasoning 00:08:57.529 --> 00:08:59.850 and a solid general knowledge foundation from 00:08:59.850 --> 00:09:02.250 these highly curated sources. And the surprising 00:09:02.250 --> 00:09:05.090 part is that, in some cases, SLMs trained this 00:09:05.090 --> 00:09:07.750 way have been shown to match or even surpass 00:09:07.750 --> 00:09:10.450 the performance of much, much larger models on 00:09:10.450 --> 00:09:13.750 certain specific tasks. Wow. OK, quality over 00:09:13.750 --> 00:09:15.990 quantity really can make a difference, even at 00:09:15.990 --> 00:09:19.769 this scale. That's fascinating. All right, shifting 00:09:19.769 --> 00:09:22.740 gears just a bit. How do developers and companies 00:09:22.740 --> 00:09:25.120 actually use these models? Are they all locked 00:09:25.120 --> 00:09:26.980 away by big tech or is there a way for others 00:09:26.980 --> 00:09:29.039 to build on them? Yeah, the source differentiates 00:09:29.039 --> 00:09:31.919 between open source and commercial LLMs. You 00:09:31.919 --> 00:09:34.500 have companies like Microsoft with Azure OpenAI 00:09:34.500 --> 00:09:37.419 and OpenAI itself offering powerful commercial 00:09:37.419 --> 00:09:39.740 models. These are often designed for stability 00:09:39.740 --> 00:09:42.580 and enterprise use. Thousands of businesses reportedly 00:09:42.580 --> 00:09:44.620 build applications and services right on top 00:09:44.620 --> 00:09:46.460 of these platforms. So that's how many companies 00:09:46.460 --> 00:09:49.320 get access to the sort of leading edge models. 00:09:49.659 --> 00:09:52.820 Exactly. But there's also a really growing ecosystem 00:09:52.820 --> 00:09:56.440 of open source models that you can, in theory, 00:09:56.779 --> 00:09:59.080 download and run yourself, assuming you have 00:09:59.080 --> 00:10:01.899 the right hardware and technical know -how. And 00:10:01.899 --> 00:10:04.080 interestingly, the source notes that the fundamental 00:10:04.080 --> 00:10:06.159 concepts and how you actually interact with these 00:10:06.159 --> 00:10:09.000 models, often through APIs or software development 00:10:09.000 --> 00:10:11.899 kits, SDKs are quite comparable across different 00:10:11.899 --> 00:10:13.799 providers, whether they're commercial or open 00:10:13.799 --> 00:10:16.419 source. The basic ideas are similar. Right. Okay, 00:10:16.419 --> 00:10:19.860 speaking of interacting, how do we, as users, 00:10:20.120 --> 00:10:23.379 tell these models what we want them to do? What's 00:10:23.379 --> 00:10:25.990 the mechanism? That's done through prompts. A 00:10:25.990 --> 00:10:29.309 prompt is simply your input text, usually just 00:10:29.309 --> 00:10:30.970 in natural language, describing the task you 00:10:30.970 --> 00:10:32.750 want the model to perform. And the output you 00:10:32.750 --> 00:10:35.009 get back is also text. So if I wanted to write 00:10:35.009 --> 00:10:37.070 an email, I just type, like, write an email asking 00:10:37.070 --> 00:10:39.570 my boss for a raise. That's simple. Pretty much, 00:10:39.610 --> 00:10:41.649 yeah. That's the power of the natural language 00:10:41.649 --> 00:10:45.210 interface. It feels intuitive. Now, the art and 00:10:45.210 --> 00:10:46.870 science of designing and crafting those prompts 00:10:46.870 --> 00:10:49.429 effectively is what's called prompt engineering. 00:10:49.789 --> 00:10:51.750 Prompt engineering. OK, that sounds a bit like 00:10:51.750 --> 00:10:54.230 programming, but using words instead of code. 00:10:54.460 --> 00:10:56.500 It's often described exactly like that, yeah. 00:10:56.840 --> 00:11:00.480 A new paradigm in programming the model. It involves 00:11:00.480 --> 00:11:02.360 understanding the model's capabilities, how it 00:11:02.360 --> 00:11:04.720 was trained, and how it tends to respond to different 00:11:04.720 --> 00:11:08.259 phrasing, different kinds of instructions. Effective 00:11:08.259 --> 00:11:10.159 prompt engineering is really crucial because 00:11:10.159 --> 00:11:13.220 a well -crafted prompt dramatically increases 00:11:13.220 --> 00:11:15.220 the likelihood of getting useful and relevant 00:11:15.220 --> 00:11:17.980 output back. So it's not just about asking a 00:11:17.980 --> 00:11:20.679 question. It's about structuring your request 00:11:20.679 --> 00:11:23.639 in a way the model understands best. Giving it 00:11:23.639 --> 00:11:26.399 context, maybe? Precisely. Prompts define the 00:11:26.399 --> 00:11:28.659 task, but can also include specific instructions, 00:11:29.019 --> 00:11:31.100 the content you want processed, maybe examples 00:11:31.100 --> 00:11:34.080 of the desired output style, or even cues to 00:11:34.080 --> 00:11:36.620 guide the model. And it's very much an iterative 00:11:36.620 --> 00:11:38.659 process. Iterative. Yeah, you send a prompt, 00:11:38.840 --> 00:11:40.500 see what you get back, and then you refine your 00:11:40.500 --> 00:11:42.519 prompt based on that output to get closer and 00:11:42.519 --> 00:11:44.899 closer to what you actually want. Trial and error. 00:11:44.990 --> 00:11:47.149 Trial and error, to get it just right, makes 00:11:47.149 --> 00:11:50.610 total sense. Okay. Now, when the model is processing 00:11:50.610 --> 00:11:53.429 that prompt and generating text, what's the basic 00:11:53.429 --> 00:11:55.870 unit it's actually working with internally? Is 00:11:55.870 --> 00:11:59.490 it letters? Words? Good question. Those units 00:11:59.490 --> 00:12:02.470 are called tokens. Tokens are the fundamental 00:12:02.470 --> 00:12:05.490 pieces of text that LLMs process. They can be 00:12:05.490 --> 00:12:08.529 whole words, sometimes parts of words, or even 00:12:08.529 --> 00:12:11.049 punctuation marks. And tokens have practical 00:12:11.049 --> 00:12:14.090 implications beyond just being the internal unit. 00:12:14.269 --> 00:12:17.649 Crucially, yes. Tokens directly affect computational 00:12:17.649 --> 00:12:20.929 costs. When you use a model through an API, for 00:12:20.929 --> 00:12:23.490 instance, you're often charged based on the number 00:12:23.490 --> 00:12:25.950 of tokens processed that includes the tokens 00:12:25.950 --> 00:12:28.850 in your input prompt plus the tokens in the generated 00:12:28.850 --> 00:12:31.870 response. Ah, okay. So longer prompts and longer 00:12:31.870 --> 00:12:34.370 answers can actually be more expensive. Exactly. 00:12:34.789 --> 00:12:36.450 And managing tokens is also really important 00:12:36.450 --> 00:12:38.710 because the LLMs have a maximum limit on the 00:12:38.710 --> 00:12:40.850 number of tokens they can handle at any one time. 00:12:41.389 --> 00:12:44.029 This limit relates to their context window. The 00:12:44.029 --> 00:12:45.850 context window, what's that? Yeah, the context 00:12:45.850 --> 00:12:48.389 window is basically the amount of text, the sequence 00:12:48.389 --> 00:12:50.889 of tokens that the model can see or consider 00:12:50.889 --> 00:12:52.669 when it's making its prediction for the next 00:12:52.669 --> 00:12:55.149 token. So it doesn't process the entire internet 00:12:55.149 --> 00:12:57.809 simultaneously when it's answering me. It focuses 00:12:57.809 --> 00:13:00.149 on a specific window of text around where it's 00:13:00.149 --> 00:13:03.750 working. Right, exactly. The context window helps 00:13:03.750 --> 00:13:06.590 the model understand the relationships and dependencies 00:13:06.590 --> 00:13:09.129 within that immediate text sequence, which leads 00:13:09.129 --> 00:13:12.639 to more accurate and coherent predictions. The 00:13:12.639 --> 00:13:15.379 maximum size of this window varies significantly 00:13:15.379 --> 00:13:17.500 between different models, by the way. It's another 00:13:17.500 --> 00:13:20.259 constraint to be mindful of, especially because, 00:13:20.399 --> 00:13:22.679 as the source notes, in practical applications 00:13:22.679 --> 00:13:25.600 like RRag, the usable length of the context window 00:13:25.600 --> 00:13:28.360 for your input is often shorter. You need to 00:13:28.360 --> 00:13:30.080 leave space for the generated output as well. 00:13:30.440 --> 00:13:32.440 Ah, that makes perfect sense. You need room for 00:13:32.440 --> 00:13:35.200 the answer within that limited window. OK, so 00:13:35.200 --> 00:13:37.710 we've talked. prompts, tokens, context window. 00:13:37.889 --> 00:13:40.169 How does the model actually understand the meaning 00:13:40.169 --> 00:13:42.870 of the words within that window? It's not just 00:13:42.870 --> 00:13:44.889 matching patterns, is it? Not just patterns, 00:13:44.889 --> 00:13:47.690 no. That's where embeddings come in. Embeddings 00:13:47.690 --> 00:13:49.610 are these fascinating machine learning tools 00:13:49.610 --> 00:13:52.330 used to represent text inputs, could be words, 00:13:52.529 --> 00:13:55.370 phrases, even whole documents as numerical vectors. 00:13:55.529 --> 00:13:58.129 Numerical vectors, like lists of numbers. How 00:13:58.129 --> 00:14:00.830 does that help with meaning? Well, these vectors 00:14:00.830 --> 00:14:03.029 are designed to capture semantic similarities 00:14:03.029 --> 00:14:05.629 within a vector space. Think of it like this. 00:14:06.519 --> 00:14:09.740 Imagine a giant, multi -dimensional map, where 00:14:09.740 --> 00:14:12.679 every word or piece of text has a specific location, 00:14:12.960 --> 00:14:16.000 a point. Words or texts with similar meanings 00:14:16.000 --> 00:14:19.100 are located closer together on that map. So, 00:14:19.340 --> 00:14:22.659 king might be close to queen on this map, and 00:14:22.659 --> 00:14:25.419 maybe the vector distance between king and man 00:14:25.419 --> 00:14:27.679 is similar to the distance between queen and 00:14:27.679 --> 00:14:29.860 woman, those kinds of relationships. Exactly. 00:14:30.039 --> 00:14:32.100 That kind of semantic relationship can be captured 00:14:32.100 --> 00:14:34.480 mathematically. The closer the numerical vectors 00:14:34.480 --> 00:14:36.840 are in this high dimensional space, the more 00:14:36.840 --> 00:14:39.190 related their meaning is considered to be. These 00:14:39.190 --> 00:14:41.429 vectors, which are just lists of floating point 00:14:41.429 --> 00:14:43.710 numbers, are learned during the model's training 00:14:43.710 --> 00:14:46.230 and act as a way for it to understand the semantic 00:14:46.230 --> 00:14:49.049 essence of the text. The source mentions their 00:14:49.049 --> 00:14:51.730 particular relevance in retrieval augmented generation, 00:14:51.809 --> 00:14:54.669 RAG, techniques. Embeddings allow you to represent 00:14:54.669 --> 00:14:57.230 external documents as vectors and then perform 00:14:57.230 --> 00:14:59.970 semantic search searching based on meaning, not 00:14:59.970 --> 00:15:02.129 just keywords to find relevant information before 00:15:02.129 --> 00:15:04.509 feeding it to the LLM to generate an answer. 00:15:04.809 --> 00:15:06.730 That's a really helpful way to visualize how 00:15:06.730 --> 00:15:09.850 the model sees meaning. OK, so we use prompts. 00:15:09.970 --> 00:15:12.450 It works with tokens within a context window. 00:15:12.830 --> 00:15:15.610 It understands meaning through embeddings. Can 00:15:15.610 --> 00:15:18.250 we influence the style or maybe the randomness 00:15:18.250 --> 00:15:20.769 of the output beyond just tweaking the prompt? 00:15:21.269 --> 00:15:23.929 Absolutely. That's controlled by model configuration 00:15:23.929 --> 00:15:26.330 parameters. These are settings you can often 00:15:26.330 --> 00:15:28.309 adjust that influence how the model generates 00:15:28.309 --> 00:15:31.409 text and, importantly, its level of randomness 00:15:31.409 --> 00:15:33.990 or predictability. Randomness. You mean like 00:15:33.990 --> 00:15:36.230 how creative or surprising it is? Precisely. 00:15:36.809 --> 00:15:39.129 Parameters like temperature and top are the main 00:15:39.129 --> 00:15:42.029 ones that steer this randomness. A lower top 00:15:42.029 --> 00:15:43.950 value, for instance, means the model is more 00:15:43.950 --> 00:15:46.330 conservative. It only considers a very small 00:15:46.330 --> 00:15:49.370 set of the most probable next words. This usually 00:15:49.370 --> 00:15:52.070 results in more predictable focus text. OK. A 00:15:52.070 --> 00:15:53.769 higher top, on the other hand, lets it consider 00:15:53.769 --> 00:15:56.769 a wider pool of less probable words, which can 00:15:56.769 --> 00:15:58.789 potentially lead to more diverse or creative 00:15:58.789 --> 00:16:01.519 outputs. Interesting. And temperature. Temperature 00:16:01.519 --> 00:16:04.100 works similarly. Higher temperature equals more 00:16:04.100 --> 00:16:07.399 randomness, lower equals less. The source notes 00:16:07.399 --> 00:16:09.779 that while temperature can technically go higher, 00:16:10.299 --> 00:16:13.720 values above, say, 1 .2 tend to produce pretty 00:16:13.720 --> 00:16:16.909 nonsensical text. It suggests the 0 .8 to 1 .2 00:16:16.909 --> 00:16:19.610 range might be good for more creative tasks. 00:16:19.950 --> 00:16:22.009 OK, got it. Temperature and top are like the 00:16:22.009 --> 00:16:24.490 creativity sliders. What about those penalty 00:16:24.490 --> 00:16:26.769 parameters mentioned? Frequency and presence 00:16:26.769 --> 00:16:29.950 penalty. Right, so frequency penalty discourages 00:16:29.950 --> 00:16:32.629 the model from repeating the exact same token 00:16:32.629 --> 00:16:35.990 based on how often that specific token has already 00:16:35.990 --> 00:16:38.409 appeared in the generated text. It basically 00:16:38.409 --> 00:16:40.490 helps prevent it from saying the same word over 00:16:40.490 --> 00:16:43.179 and over again. OK, avoids repetition. Yeah. 00:16:43.580 --> 00:16:46.039 And presence penalty is broader. It discourages 00:16:46.039 --> 00:16:48.000 the model from repeating any token that has appeared 00:16:48.000 --> 00:16:50.419 so far in the output, regardless of frequency. 00:16:51.000 --> 00:16:53.139 This tends to encourage it to introduce new concepts 00:16:53.139 --> 00:16:56.159 or move on to different topics. So a high presence 00:16:56.159 --> 00:16:58.500 penalty really pushes the model to explore new 00:16:58.500 --> 00:17:01.299 ground within its response. So one stops repeating 00:17:01.299 --> 00:17:04.180 the exact same word, the other pushes for new 00:17:04.180 --> 00:17:07.039 ideas or topics. Roughly. Yeah, that's a good 00:17:07.039 --> 00:17:09.079 way to think about it. And then there's legit 00:17:09.079 --> 00:17:12.119 bias, which is a pretty powerful tool. It lets 00:17:12.119 --> 00:17:14.359 you directly steer the likelihood of specific 00:17:14.359 --> 00:17:16.440 tokens appearing in the output. How does that 00:17:16.440 --> 00:17:19.079 work? You can effectively ban certain undesirable 00:17:19.079 --> 00:17:21.319 tokens by giving them a very low score, like 00:17:21.319 --> 00:17:25.059 an age of 100. Or you can make desired tokens 00:17:25.059 --> 00:17:27.460 almost guaranteed to appear by giving them a 00:17:27.460 --> 00:17:30.299 very high score, like 100. Smaller adjustments 00:17:30.299 --> 00:17:32.700 just nudge the probability up or down a bit. 00:17:32.900 --> 00:17:34.839 That sounds like you could really force it to 00:17:34.839 --> 00:17:36.960 say certain things or completely avoid others. 00:17:37.200 --> 00:17:39.940 You can, but the source rightly cautions that 00:17:39.940 --> 00:17:43.880 it requires understanding how words map to specific 00:17:43.880 --> 00:17:47.259 tokens, which isn't always obvious. And excessive 00:17:47.259 --> 00:17:50.559 or inappropriate use can easily lead to nonsensical, 00:17:50.859 --> 00:17:53.680 repetitive, or even biased outputs. It's a precise 00:17:53.680 --> 00:17:55.880 tool that needs careful handling. Power, but 00:17:55.880 --> 00:17:57.140 you definitely need to know what you're doing. 00:17:57.390 --> 00:17:59.690 Fascinating how much control you can actually 00:17:59.690 --> 00:18:02.549 have over the output, though. It really is. And 00:18:02.549 --> 00:18:04.029 finally, just to round things out, the source 00:18:04.029 --> 00:18:06.470 briefly mentions a couple more related concepts. 00:18:07.450 --> 00:18:11.029 Model adaptation, which is the broader idea of 00:18:11.029 --> 00:18:13.609 customizing models for specific use cases like 00:18:13.609 --> 00:18:15.349 the fine tuning we discussed earlier. Right. 00:18:15.890 --> 00:18:19.049 And the really intriguing idea of emergent behavior. 00:18:19.309 --> 00:18:22.109 Emergent behavior. What's that? Yes. These are 00:18:22.109 --> 00:18:24.349 capabilities or behaviors that the model starts 00:18:24.349 --> 00:18:28.269 to exhibit. were not explicitly programmed or 00:18:28.269 --> 00:18:30.410 even anticipated by the developers during training. 00:18:30.930 --> 00:18:34.130 They seemed to sort of emerge from the sheer 00:18:34.130 --> 00:18:36.210 scale and complexity of the training process 00:18:36.210 --> 00:18:38.670 itself. Wait, things the model can just suddenly 00:18:38.670 --> 00:18:40.890 do that nobody actually designed it to do? That's 00:18:40.890 --> 00:18:42.950 incredible and maybe a little spooky. It is. 00:18:42.950 --> 00:18:45.250 It really highlights that we're still exploring 00:18:45.250 --> 00:18:48.109 the full potential and maybe even the fundamental 00:18:48.109 --> 00:18:51.089 nature of these incredibly complex models. Wow. 00:18:51.319 --> 00:18:53.140 Okay, so let's try and wrap this deep dive up. 00:18:53.279 --> 00:18:55.099 We've journeyed through the fundamentals of large 00:18:55.099 --> 00:18:57.380 language models, starting with how they're built 00:18:57.380 --> 00:19:00.599 on foundational models, enabling generative AI 00:19:00.599 --> 00:19:03.359 and transfer learning. Their core mechanism of 00:19:03.359 --> 00:19:05.539 predicting the next word based on probability. 00:19:05.880 --> 00:19:07.880 Right, the revolutionary transformer architecture 00:19:07.880 --> 00:19:10.359 that makes it all possible. Their vast capabilities 00:19:10.359 --> 00:19:13.539 from answering questions to writing actual code. 00:19:14.019 --> 00:19:15.940 The crucial limitation of the training cutoff 00:19:15.940 --> 00:19:18.259 date, remembering they don't know recent events. 00:19:18.380 --> 00:19:21.200 Definitely. The different types base, instruction 00:19:21.200 --> 00:19:24.079 -based, fine -tuned, and the surprising power 00:19:24.079 --> 00:19:26.720 of small language models, especially when driven 00:19:26.720 --> 00:19:29.539 by strategic, high -quality data selection. Yeah, 00:19:29.740 --> 00:19:32.319 that quality over quantity point is key. Plus 00:19:32.319 --> 00:19:34.579 how we access them through open source and commercial 00:19:34.579 --> 00:19:37.259 options. And how we interact using prompts, the 00:19:37.259 --> 00:19:39.200 whole art and science of prompt engineering. 00:19:39.619 --> 00:19:41.759 Exactly. The fundamental units they work with 00:19:41.759 --> 00:19:44.559 tokens and how they impact cost, and the limits, 00:19:44.799 --> 00:19:47.240 like the context window. how they grasp meaning 00:19:47.240 --> 00:19:50.380 through embeddings, those vector maps. And how 00:19:50.380 --> 00:19:52.920 we can steer their output using those configuration 00:19:52.920 --> 00:19:55.319 parameters like temperature, top, and the penalties, 00:19:55.759 --> 00:19:58.180 all operating within the context window. Phew. 00:19:58.779 --> 00:20:00.599 Understanding these building blocks really does 00:20:00.599 --> 00:20:03.319 seem key to making sense of this current wave 00:20:03.319 --> 00:20:06.660 of generative AI and figuring out how to use 00:20:06.660 --> 00:20:09.400 it effectively. Absolutely. It demystifies it 00:20:09.400 --> 00:20:11.569 somewhat, doesn't it? It really does. Hopefully 00:20:11.569 --> 00:20:14.089 this deep dive has pulled back the curtain a 00:20:14.089 --> 00:20:17.589 bit and given you listening some valuable aha 00:20:17.589 --> 00:20:20.509 moments about the core technology powering so 00:20:20.509 --> 00:20:22.210 much of what you're seeing and probably using 00:20:22.210 --> 00:20:24.650 today. So maybe a final thought to leave you 00:20:24.650 --> 00:20:27.049 with. Considering those emergent behaviors we 00:20:27.049 --> 00:20:29.750 just touched on and that surprising success of 00:20:29.750 --> 00:20:33.730 quality data over sheer quantity in SLMs. What 00:20:33.730 --> 00:20:35.750 unexpected applications or capabilities might 00:20:35.750 --> 00:20:37.849 we see next? Things emerging from these models 00:20:37.849 --> 00:20:39.549 that aren't even being specifically designed 00:20:39.549 --> 00:20:42.009 for right now. That's a great question. What 00:20:42.009 --> 00:20:44.170 stands out to you most from everything we've 00:20:44.170 --> 00:20:45.630 discussed today? Something to think about.