AI Glossary

Generative AI

Generative AI refers to models that create new content—text, images, audio, video, or code—conditioned on prompts or other inputs.

Definition

Generative AI refers to models that create new content—text, images, audio, video, or code—conditioned on prompts or other inputs.

Plain English explanation

These systems produce new material instead of only classifying or scoring what already exists.

Technical explanation

Generative models learn distributions over content and sample new instances. Approaches include autoregressive Transformers for text/code and diffusion models for images, among others.

Why it matters

Generative products changed enterprise software roadmaps after 2022–2023. Buyers must distinguish generation from classical classification or rules.

Real-world applications

Drafting and rewriting content
Image and media generation
Synthetic data for testing (with caveats)

Benefits

Accelerates content and design iteration
Enables new UX patterns (chat, copilots)
Can bootstrap data when carefully validated

Limitations

IP, privacy, and brand-risk concerns
Quality variance and hallucinations
Evaluation is harder than for fixed classifiers

Common misconceptions

Generative does not mean unsupervised or ungoverned
Pretty outputs can still be wrong

Large Language Models Diffusion Model

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Enterprise Software Legal

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