Unlock the mechanics behind Large Language Models (LLMs) in this foundational session. This masterclass breaks down how LLMs understand, generate, and reason with human language — turning billions of parameters into powerful outputs. You'll explore the architecture behind models like GPT and Claude, and learn how tokenization, embeddings, attention mechanisms, and fine-tuning come together to create magic. What You’ll Learn: Core building blocks of LLMs (transformers, tokens, context windows) How LLMs generate outputs and why prompt design matters Key concepts: embeddings, attention, pre-training vs. fine-tuning Trade-offs: model size, latency, performance Live walkthrough using the Master Playground: test prompts, visualize responses, and debug model behavior Hands-on Focus: You’ll use a custom playground environment to interact with LLMs directly — testing outputs, chaining tasks, and understanding how model parameters affect results.