GPT (Generative Pre-trained Transformer)
GPT is the model architecture developed by OpenAI that underpins ChatGPT, GPT-4, and related systems. It uses the transformer architecture to predict and generate text, and is trained on large volumes of internet text before being fine-tuned for specific applications.
What is GPT?
GPT stands for Generative Pre-trained Transformer. It describes both a training approach and an architecture. Generative means the model produces new text as output. Pre-trained means it was trained on a large general corpus before being adapted to specific tasks. Transformer refers to the underlying neural network architecture — introduced in the 2017 paper "Attention is All You Need" — which processes sequences of text using self-attention, allowing it to capture long-range dependencies in language.
OpenAI released the first GPT model in 2018. GPT-4, released in 2023, is one of the most capable general-purpose language models available, with strong performance across writing, reasoning, code, and document analysis tasks.
How GPT Works
GPT is trained to predict the next token in a sequence — given the preceding text, what word or word fragment comes next? Doing this well at scale, across trillions of examples, forces the model to develop rich internal representations of language, facts, and reasoning patterns. After pre-training, the model is fine-tuned using human feedback (RLHF) to make its outputs more helpful, accurate, and aligned with user intent. At inference time, GPT generates text token by token, each step conditioned on everything that came before.
GPT in Operations
GPT models are the most commonly used foundation for operational AI agents today. Their ability to read and reason about unstructured text — purchase orders, email threads, logistics reports, supplier contracts — makes them well-suited to the document-heavy workflows that slow down manufacturing, wholesale, and logistics operations. The practical question is not whether to use GPT, but how to constrain and connect it: the right prompts, the right retrieval layer, the right human checkpoints. Capability is not the bottleneck. Integration is.