How LLMs actually work: embeddings, inference dynamics and the autoregressive loop, explained
gerardsans · x · 2026-10-04
In a reply thread, the author breaks down "how LLMs work" into three parts: embeddings creation (token configuration shaped by training regimes), inference dynamics (tokenization, positional encoding, pre-fill, layer-to-layer attention and MLP blocks leading to token sampling), and the autoregressive loop. A linked article series provides the full explanation — a solid primer on the Transformer inference pipeline.
Related event: Debate: Co-occurrence Statistics as the Mathematical Foundation of LLMs(3 posts)→
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