Inside LLMs: A Math-Light Guide to How Transformer Models Work

This deep explainer breaks down the core machinery inside modern transformer-based LLMs without heavy math. It walks through tokenization, embeddings, positional encoding, attention mechanisms, feed-forward networks, and the generation loop, showing how text becomes integers, gains meaning, and flows through stacked transformer blocks to produce the next token. By the end, readers can parse modern model cards and papers with confidence.