How AI Actually Works: LLMs Explained Simply

After my last video, running local AI on a Mac Studio, I got a lot of questions asking: "But how does it actually work?" In this video, we step away from the hardware to break down the "magic" of Large Language Models (LLMs) like ChatGPT and Llama. We’ll explore how AI predicts the next word, why "tokens" matter more than words, and how the Transformer architecture uses "Self-Attention" to understand context. Whether you're a developer or just curious about AI, I’ve worked hard to make this the simplest explanation of LLMs I can. In this video, we cover: - What are Tokens and End-of-Sequence (EOS) markers? - Understanding Embedding Vectors and Features. - The Student Analogy: How Self-Attention (Q, K, V) works. - Feedforward Networks (MLP) and the "fragments of thought." - How Temperature and Top-P change AI responses. 🔔 **Subscribe** for more hands‑on cloud tutorials! *Check out my GitHub page for README files and examples to my videos!* *https://github.com/heavy-metal-cloud/youtube/tree/main/videos* Timestamped Chapters 0:00 - Intro 1:32 - The Embedding Layer: Mapping Meanings 2:13 - Why Words Need Numbers (Vectors) 3:13 - Connecting the Layers: From "The" to "Pizza" 4:01 - Phase 1: Self-Attention - for token collaboration 7:02 - Phase 2: The Feedforward Network (MLP) 8:10 - The Language Model Head & Output 12:57 - Fragments of Thought: The Art Analogy 13:42 - AI Compression