Generative AI
Generative AI produces content such as text, images or code. Quality is assessed against its intended use and available source information.
Generate new content for a purpose
Generative AI refers to AI methods that produce content such as text, images, audio or code. Their output depends in part on the input and patterns learned by the model. A language model is one form; not every generative application produces text.
In a fictional example, a system drafts a product description from approved product data. If material information is missing, a plausible addition must not be adopted as a confirmed product feature. The draft is checked against its source information.
Classification instead assigns, for example, a message to a predefined category. That does not necessarily require free-text generation. The appropriate method depends on the intended outcome. Generated content is also not automatically factually correct or ready for publication without further checking.
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Related terms
- Large language modelA large language model processes language using learned patterns. Fluent text proves neither factual accuracy nor access to current business data.
- PromptA prompt is the input to a generative model. A clear task helps but does not replace data connections or access permissions.
- AI hallucinationAn AI hallucination is plausible-looking content that is factually false or invented. Fluent language is not evidence of correctness.
- Model cardA model card documents an AI model’s purpose, foundations, evaluation conditions and known limitations for a particular version.
- AI readinessAI readiness concerns the foundations of an AI project: objectives, usable data, skills, responsibilities and an operating workflow.
- Context engineeringContext engineering prepares information for individual model calls: relevant instructions, messages, sources and current states.
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