How LLMs Actually Work: A Plain-Language Guide to Transformer Internals

This accessible deep dive explains the core machinery inside modern transformer-based LLMs without heavy mathematics. It covers tokenization, embeddings, positional encoding, multi-head attention, feed-forward networks, and the generation loop, showing how each component contributes to next-token prediction. By the end, readers can parse model cards and research papers with a clear map of which architectural piece does what.