How Large Language Models Actually Work: Tokens, Context, Scale | Generative AI Ep 6

Is the AI actually thinking? Large language models sound remarkably human, but under the hood they work very differently from the way people write. This episode separates the illusion from the reality. We start with next-word prediction, the clarifying truth: human writing is driven by intent and reasoning, while LLMs use probabilistic next-word prediction in a loop of input text, predict a word, feed it back, and repeat. Then we cover tokens and context, the model's memory: tokenization breaks text into smaller chunks matching the model's internal vocabulary, and the context window determines what text is attended to, sets practical limits, and uses attention to focus, with limits like 4,096 tokens. You'll learn the power of scale, from the 2017 transformer to BERT's 340 million parameters in 2018 to today's trillion-parameter models, and emergent abilities that organically appear as models scale, which some describe as an early, incomplete version of AGI. We cover patterns vs reason and the myths that LLMs are a factual base or prove reason, when in reality they are pattern matchers that mimic training. We finish by probing limits, including why models forget prior conversations instantly without context. What you'll learn: How next-word prediction works Tokens and tokenization The context window and its limits How model scale drives capability Emergent abilities and the AGI debate Pattern matching vs genuine reasoning Chapters: 0:00 Introduction 0:25 Is the AI Actually Thinking? 0:51 Episode Overview 1:10 Next Word Prediction: The Clarifying Truth 2:22 Tokens and Context: The Model's Memory 4:21 The Power of Scale: Massive Model Impacts 5:52 Patterns vs Reason: What It Actually Knows 6:30 Probing Limits: Failure Scenarios 7:45 Final Question: How Will You Change Your Prompting Tomorrow? Cybersecurity Masterclass by Joshua Moses | CyberTrace Foundation.