0:02 In the intersection between probability and chaos 0:05 a new kind of mind takes shape. 0:07 Where once we program explicit rules, 0:10 a model is there to predict the next piece of sequence 0:13 based on the previous information. 0:16 Trained on a massive amount of data, 0:17 why of course! 0:19 Think of it like this, 0:21 If REST APIs 0:22 are function calls 0:23 over HTTP, 0:24 LLMs are conversations 0:26 with a system that predicts and fills each word 0:29 of its answer, based on everything its ever read. 0:33 Prompt is your question, 0:35 your instruction, 0:36 and your starting point. 0:38 But unlike code where your syntax errors break everything, 0:42 prompts are forgiving. 0:43 They are an art form that shapes the AI's response 0:47 without dictating the next steps. 0:49 And just how you will configure 0:51 your development environment before writing code... 0:54 The system message sets the stage! 0:56 It is invisible to the user 0:58 but critical for the AI, 1:00 functioning like both environmental variables and foundational instructions, 1:04 it shapes not HOW your application behaves 1:08 but establishes the foundational rules 1:10 and capabilities 1:11 like a blueprint or prototype 1:13 defining what can and cannot be created within the conversation. 1:18 Each word, part-word, or symbol 1:20 the model processes is a token. 1:23 If strings are your basic unit in code, 1:25 tokens are the fundamental unit in LLMs. 1:30 Roughly four tokens here. 1:32 The economy of language becomes a literal economy when you're paying per token. 1:36 The context window is your model's working memory; 1:40 how much it can see at once. 1:42 Think of it like buffer size or stack memory, 1:44 8k tokens or 32k tokens, 1:48 that's all your model has to work with. 1:50 And here is where it gets interesting, 1:52 Temperature controls randomness! 1:54 At zero is deterministic. 1:56 Always taking the most probable path 1:59 like your sorting algorithm always giving the same result. 2:02 Higher temperature introduces chaos! 2:05 More creative, 2:06 more surprising, 2:07 and more human. 2:08 Just how you might add randomization 2:11 to prevent overfitting in your ML models. 2:14 But... with creativity comes 2:16 hallucinations!!!! 2:18 The model's equivalent of a memory leak 2:20 or an uncaught exception. 2:24 Similar 2:25 even if the output looks right 2:27 and FEEELLSSS right, 2:28 you need to validate what the AI is giving back to you. 2:31 Traditional code handles different data types 2:34 through interfaces and polymorphism, 2:36 multimodal modes do the same 2:39 but seamlessly! 2:40 Text becomes image, 2:41 image becomes description, 2:43 audio becomes transcription, 2:44 all throughout a unified interface. 2:47 It is like your ultimate polymorphic function that just works! 2:52 For us, 2:53 this isn't the technology 2:54 it's a paradigm shift. 2:55 The fundamentals will still remain 2:57 input, process, output. 2:59 Now, you might have decided to add a 3:02 pre-trained LLM into your system. 3:05 What do you need to know? 3:06 How do you get started, right? 3:07 Well, the first thing would be to ask yourself 3:09 about the functionality. 3:11 What do you need it for? 3:12 Is it multimodal? 3:14 Does it speak the language that you need? 3:16 If you have global users, this actually matters a lot 3:19 or... how about does it handle theÂ
inputs that your application processes? 3:24 Then, you have to think about performance, right! 3:27 You got to look at it and be like 3:28 qualitatively, you have to ask yourself does it meets the expectations that you have on it? 3:34 Quantitatively, how does it do on benchmarks 3:37 like GLUE or LMSYS Chatbot Arena 3:41 or even the ones that you've established for yourself 3:43 and then obviously, lastly, the cost! 3:46 Your budget will determine everything!!! 3:49 So now that you have selected a model, 3:51 it's all about optimization 3:53 and think of the next three tiers as levels of complexity. 4:00 Prompt engineering, 4:01 the simplest approach, 4:02 modify how you communicate with the model like 4:04 writing a config file for a human brain. 4:11 RAG (or Retrieval Augmented Generation) 4:13 gives your LLM more context 4:15 by connecting it to external knowledge bases. 4:18 We sort of already do this 4:19 with services when we connect them to databases 4:22 however, we're going to be learning about vectors 4:24 in the future. 4:28 Last but not least 4:29 fine-tuning! 4:30 It's about retraining your model with additional data, 4:33 techniques, and specialized strategy. 4:35 It can actually be the most complex of them all 4:38 but the most rewarding, depending on your needs. 4:40 Now, what makes it so exciting 4:42 are the magical incantations. 4:44 Is about the combination of prompts, 4:46 of training methods, 4:48 and parameters 4:49 that can produce very specialized and remarkable results 4:52 depending on your use cases. 4:54 Managing these components individually 4:56 means writing code for model loading, 4:58 context management, 5:00 prompt templates, 5:00 memory handling, 5:01 and tool integrations. 5:02 That is why LLM frameworks emerged! 5:05 The idea is to give you pre-built tools 5:07 that come from common patterns 5:09 just like how web frameworks give you authentication and database connections 5:13 out of the box. 5:14 I would think of frameworks as a way to build AI agents 5:17 which are essentially orchestratedÂ
loops of LLM calls 5:22 that have memory and tool integration. 5:24 And the core of what are AI agents 5:27 is the same as prompt engineering: 5:29 it's all about the conversation interaction! 5:31 And just remember, 5:33 we already know that the coin might land 5:35 on one side or the other. 5:37 That doesn't change the coin. 5:38 And you know, you already know how to make it dance! 5:42 See you in the next one!
