This is Part 1 of a five-part deep-dive series on the internal mechanics of large language models. This series is written for engineers, ML practitioners, and technically advanced readers. Part 1 covers tokenization and embeddings — the first two stages through which raw text becomes a mathematical object a neural network can reason over. Why Text Must Be Transformed Neural networks are, at their core, mathematical functions that operate on tensors, which are multi-dimensional arrays of floating-point numbers. They cannot accept raw text strings as input. Before a language model can do anything useful, it must convert a sequence of characters into a sequence of numbers. This two-step process of…