What GPT Really Is: Tokens, Transformers, and How Language Models Work

GPT, which stands for Generative Pre-trained Transformer, is a neural language model designed to process sequences of tokens and predict what comes next, making it far more complex than a simple chatbot or search engine. The Transformer architecture at its core was introduced in the 2017 paper 'Attention Is All You Need' by Vaswani and colleagues, replacing older recurrent neural network approaches with more efficient attention mechanisms. Before processing any text, GPT converts input into smaller units called tokens using methods like Byte Pair Encoding, which allows rare or technical words to be broken into manageable subword units. These tokens are mapped to numerical identifiers and then transformed into mathematical vectors called embeddings, which the model uses to generate language. The model's central training objective is straightforward: given a sequence of tokens, predict the most statistically likely next token, a process that underpins its ability to write, summarize, and converse naturally.