What is generative AI?
Generative AI refers to systems that use learned patterns to produce content such as text, images, audio or code. Unlike a classifier that only selects a category, it creates a new output sequence. Fluent or realistic content is not proof of correctness.
From prompt to output
The user's prompt, context and relationships learned during training influence the output. Language models can use probabilities for subsequent text segments, while image systems may use different generation methods. Clear goals, audience, format and constraints can improve relevance. A user requesting a product description, for example, can supply verified features and instruct the model not to invent missing details. A well-written prompt still cannot eliminate errors. Connecting a model to current documents or data sources can support its response, but selecting an unsuitable source or misinterpreting a document remains possible.
Benefits, limitations and human oversight
Generative systems can assist with drafts, ideas, editing and code explanations. Results should be checked, particularly names, dates, numbers and source links. Convincingly presenting nonexistent sources or events is often called hallucination. Confidential information should not be shared without understanding the service's data-handling conditions. Generated content also needs evaluation for other people's rights, bias and context of use. Code that runs is not necessarily secure; review and testing remain necessary. In a healthy workflow, AI provides suggestions while people retain responsibility for purpose, accuracy and publication.