How LLMs Actually Work

Original: How LLMs work

A practical walkthrough of transformer-based LLM internals, from tokens and embeddings to attention, FFNs, and next-token generation.

The article explains how modern LLMs convert text into token IDs, embeddings, and position-aware vectors before passing them through stacked transformer blocks. It covers attention, multi-head attention, KV cache, GQA, feed-forward networks, MoE, residual streams, normalization, and decoding. Its goal is educational: helping readers understand the common architecture behind many current model families and read model cards or papers more confidently.

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