Foundations

What a language model is, what next-token prediction really means, and what LLMs can honestly do

• What Is a Large Language Model? — The one-sentence definition unpacked: a next-token predictor, not a database — and the vocabulary you need before anything else makes sense. (4 lessons, 40 min)
• Next-Token Prediction, Really — The single objective behind everything an LLM does: a probability for every token, a measurable notion of being wrong, and how prediction becomes ability. (4 lessons, 45 min)
• From N-grams to Transformers — Seventy years of language modeling in one arc: counting words, the neural turn, attention, and the scale era — with the dates and papers that mattered. (4 lessons, 45 min)
• Capabilities and Hard Limits — What LLMs do well, what they cannot do by construction, and why the frontier is jagged — testing beats trusting. (4 lessons, 45 min)
• The LLM Lifecycle Map — Pre-training, post-training, inference, adaptation: the map this whole academy is organized around, and who does what at each stage. (3 lessons, 35 min)
• In Production: Where the Meters Run — The domain’s production capstone: cost, latency, reliability, and security as the four meters every later domain reads — and where each one lives in the stack. (3 lessons, 40 min)