Fundamentals

Build your AI mental model from zero — structured from easy to hard

First time here? This article tells you what to read, how to read it, and what you can skip—pick a path based on your background.

No formulas, no jargon pile-up—three everyday metaphors to build a first-principles understanding of artificial intelligence.

Three core mechanisms—tokenization, context windows, and probabilistic sampling. Understand these and AI stops feeling like magic.

Five practical techniques—from casual questions to reliable output. No spellbooks—just clarity about what you want.

Retrieval-Augmented Generation end to end: chunking, embedding, retrieval, generation—one diagram and clear steps.

Agents plan steps, call tools, and execute autonomously. Understand the loop and you'll see what Cursor and Devin are doing.

Three ways to make AI more domain-aware—when each fits, what it costs, and where it breaks. One decision table.

Text, images, audio, video—how multimodal AI unifies them, what works today, and what still doesn't.

Ten checks for shipping AI in real products—security, cost, eval, monitoring. None optional.