GPT generative pre-trained transformer

GPT Generative Pre-trained Transformer is a family of autoregressive language models that generate human-like text by predicting the next token in a sequence using transformer neural network architecture. These models undergo unsupervised pre-training on vast corpora of internet text, learning language patterns, grammar, facts, and reasoning abilities without task-specific supervision. The GPT architecture utilizes transformer decoder blocks with self-attention mechanisms that process input sequences in parallel, enabling efficient handling of long-range dependencies and contextual relationships. During pre-training, GPT models learn to predict the next word given previous context, developing emergent capabilities including text completion, question answering, summarization, and code generation. The generative nature allows these models to produce coherent, contextually appropriate text across diverse domains and tasks through prompt engineering and few-shot learning techniques. Modern GPT implementations like GPT-3 and GPT-4 demonstrate sophisticated reasoning, creativity, and knowledge synthesis capabilities that enable enterprise applications ranging from content automation to intelligent customer service. The pre-trained foundation allows fine-tuning for specific domains or tasks while maintaining broad language understanding, making GPT models versatile tools for natural language processing applications.