Generative AI: Definition and Examples

Generative AI encompasses all artificial intelligence models designed to produce new content rather than simply analyze or classify existing data. Unlike traditional AI systems that are limited to pattern recognition or prediction, generative AI is capable of creating texts, images, code, music, and even video that did not exist before.

The functioning relies mainly on deep learning architectures such as Transformers (underlying GPT, Claude, Gemini), generative adversarial networks (GANs), or diffusion models (Stable Diffusion, DALL-E). These models are trained on massive datasets and learn to capture statistical structures of language, images, or other modalities to then generate coherent and contextually relevant outputs.

In prompt engineering, generative AI is the central tool with which one interacts. Understanding its capabilities and limitations — hallucinations, biases, context window, sensitivity to instructions — is fundamental to crafting effective prompts. The quality of the output directly depends on the precision, structure, and context provided in the prompt.

Generative AI is transforming many sectors: content writing, software development, graphic design, scientific research, education, and customer service. Its massive adoption since 2022 makes it one of the most disruptive technologies of the decade, with profound implications for productivity, creativity, and work organization.