What is Generative AI (GenAI)?

Generative AI (GenAI) is a class of artificial intelligence systems designed to create new content (text, images, audio, video, software code, etc.) based on the data on which they were trained. Unlike discriminative models, which are focused on classification, regression, or prediction, generative models produce outputs that did not previously exist in an exact form, relying on probabilistic modeling of the joint data distribution.

The technological foundation of most modern GenAI systems consists of large neural networks, primarily transformer-based architectures. Training typically occurs in multiple stages: self-supervised pretraining on massive datasets, followed by fine-tuning using supervised data and/or reinforcement learning methods (RLHF, DPO, etc.).

Generative AI is applied to tasks such as automated text generation, image and video synthesis, voice assistant creation, interface design, code generation and auto-completion, and many others. In production environments, such systems require substantial computational resources, carefully optimized training pipelines, efficient fine-tuning techniques (PEFT, LoRA, etc.), as well as continuous quality control of generated outputs, management of hallucination and distortion risks, and ongoing monitoring to prevent model degradation over time in production.