A neural network is a computational system loosely inspired by the structure of biological brains, consisting of interconnected layers of mathematical units called neurons that process and transform data. By adjusting the strength of connections between neurons during training, neural networks learn to recognize patterns, make predictions, and generate outputs from input data without being explicitly programmed with rules for every scenario.

Modern neural networks used in AI image and video generation are deep neural networks, meaning they contain many sequential layers through which data passes and is progressively transformed. Each layer learns to detect increasingly abstract features: early layers might respond to edges and textures, while deeper layers recognize complex structures, objects, and semantic concepts. Convolutional neural networks (CNNs) are particularly suited to processing visual data and have been central to advances in image recognition. Transformer architectures, originally developed for language processing, have been adapted for image and video generation and underpin many of the leading models in use today. The combination of transformer-based text understanding with diffusion-based image synthesis has driven much of the recent progress in text-to-image and text-to-video generation.

Understanding that all AI generation tools are built on neural networks helps contextualize both their capabilities and their limitations. Neural networks learn from patterns in training data and excel at tasks well-represented in that data, while struggling with novel combinations, precise counting, or concepts underrepresented in their training. This understanding helps creators craft prompts that work with the model's learned patterns rather than against them.