How LLMs Actually Work

Modern LLMs are transformer-based models that convert text into token sequences, embed those tokens as high-dimensional vectors, and process them through stacked transformer blocks that use attention mechanisms to share information between tokens. The core architectural components—tokenization, embeddings, positional encoding, multi-head attention, feed-forward networks, and residual connections—are largely shared across different LLM families, with variations in tokenizer choice, model scale, and post-training determining the differences between models. Understanding these transformer mechanisms provides the foundation for comprehending how modern LLMs generate predictions and operate at scale.

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