Definition
GPT (Generative Pretrained Transformer) is a type of deep learning model for natural language processing (NLP) that uses a transformer architecture. It is designed to generate human-like text by predicting the next word in a sequence based on context, making it highly effective for tasks like text generation, summarisation, translation, and question answering.
GPT was first introduced by OpenAI in 2018 with GPT-1. Since then, increasingly powerful versions have been released, and GPT models now sit at the core of widely used tools such as ChatGPT, while also inspiring competing systems like Gemini, Claude and DeepSeek.
How GPT works
GPT combines two important ideas in modern AI: generative pre-training and transformers.
- Generative pre-training means the model first learns from vast amounts of unlabelled data using self-supervised learning. It learns patterns in language by predicting the next token in a sequence, gradually improving through error correction.
- The transformer architecture, introduced in 2017, uses attention mechanisms to process entire sequences of text at once. This helps GPT understand context and relationships between words, even when they are far apart in a sentence.
GPT does not understand language like a human. Instead, it breaks text into tokens and uses probability to predict the most likely next token based on previous ones. Its behaviour is shaped by billions or even trillions of internal parameters adjusted during training.
Some newer GPT systems are multimodal, meaning they can process and generate text, images, and audio. Others are designed as reasoning models, using extra computation to analyse problems before responding. GPT-5 introduced a router that selects between faster and slower reasoning models depending on the task.
GPT applications
GPT’s flexibility makes it useful across many natural language processing and automation tasks.
- Content creation, including blogs, emails, product descriptions and creative writing
- Chatbots and virtual assistants for customer service and general conversation
- Language translation and real-time multilingual communication
- Text summarisation and rewriting in different styles
- Data analysis and turning large datasets into insights
- Code generation and programming assistance
- Healthcare support, such as information access and potential use in medical education
Many applications connect to GPT models through APIs, using them as underlying AI infrastructure.
Why GPT is considered a milestone
GPT models have accelerated progress in generative AI by showing how one large, pre-trained model can be adapted to many downstream tasks. Their ability to generate natural-sounding text helps bridge communication between humans and machines, supporting automation, productivity and creative workflows.
At the same time, GPT systems raise challenges around accuracy, bias, intellectual property, privacy and data protection. Because they are trained on large volumes of internet data, outputs can be incorrect, reflect social biases or create legal and ethical concerns.
Key takeaways
- GPT stands for generative pre-trained transformer, a type of large language model.
- It uses self-supervised generative pre-training and the transformer architecture with attention mechanisms.
- GPT predicts tokens based on probability to produce human-like text and, in some versions, images and audio.
- It powers chatbots, content creation, translation, coding support and many other AI applications.
- While powerful, GPT systems come with risks related to accuracy, bias, privacy and intellectual property.