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Explainer, how large language models actually work

techMay 10, 2026

How Large Language Models Actually Work: Tokens, Attention, and the Magic Behind the Text lays out how LLMs generate language by predicting the next token using the transformer architecture. It explains tokenization into subword tokens, embedding and positional encodings, and stacked transformer layers where self-attention mixes information across tokens to build contextual embeddings. The post describes training by next-token prediction on massive text corpora using gradient descent and explains inference choices such as greedy decoding, temperature, and nucleus sampling that shape outputs. Knowing these components clarifies why models reflect training data patterns, make context-dependent errors, and how prompt length and model size affect reliability.

Key Highlights

LLMs split text into tokens representing subword pieces, not whole words.
Self-attention computes pairwise weights so each token gains context-dependent representation.
Training uses next-token prediction over massive corpora with gradient descent.
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