Editorial review prepared: September 2026. Generative AI changes quickly, so product-specific features should be checked against current official documentation.
Generative AI refers to AI systems designed to produce new outputs—such as text, images, audio, video, software code, or structured data—based on patterns learned during training and the instructions or inputs provided at use time.
How generative AI works
Different generative systems use different model families. Language and code generation commonly rely on Transformer-based models. Image, audio, and video systems may use diffusion models, Transformers, or other architectures. The model does not simply retrieve a stored copy of every output; it generates a new result from learned statistical representations and the current input.
Common forms of generative AI
- Text generation: drafting, rewriting, summarization, translation, question answering, and conversational assistance.
- Image generation and editing: creating or modifying visual content from text, images, or other inputs.
- Audio and speech: speech synthesis, transcription support, music generation, and audio transformation.
- Video generation and editing: creating or modifying moving-image content from multimodal inputs.
- Code generation: producing, explaining, translating, or debugging software code.
Where generative AI can be useful
- Brainstorming and first-draft creation.
- Summarizing and reorganizing large amounts of information.
- Creating prototypes and alternative versions of content.
- Assisting developers with documentation and code.
- Supporting accessibility through translation, transcription, and format conversion.
- Helping researchers and professionals explore ideas when outputs are independently checked.
Important limitations
Generative AI can produce impressive results, but fluency is not proof of correctness. Systems can generate fabricated facts, unsafe suggestions, misleading citations, biased outputs, or content that is unsuitable for a particular context. High-impact decisions should not rely on generated content without appropriate human review and authoritative evidence.
Risk management and responsible use
NIST’s Generative AI Profile identifies risk management as an ongoing process across the design, development, deployment, and use of generative AI systems. Practical safeguards can include human review, source verification, access controls, testing, monitoring, documentation, and clear limits on how generated outputs are used.
Generative AI vs. traditional predictive AI
The distinction is not simply “traditional AI predicts while generative AI creates.” Many generative systems are themselves predictive models, and non-generative AI can also support complex decision-making or perception. The useful distinction is that generative systems are specifically designed to produce new content or representations as outputs.
A simple example: generation is probabilistic
Suppose a user asks a text model to draft three product descriptions. The system does not retrieve three fixed descriptions from a database. It generates sequences from learned statistical patterns, the prompt, and any context supplied at use time. A different prompt, model setting, or source context can produce a different result. That flexibility is useful for drafting and exploration, but it is also why generated material should be checked before publication.
How to evaluate a generated result
- Accuracy: verify factual claims against reliable evidence rather than trusting fluent wording.
- Relevance: confirm that the output actually answers the requested task and audience.
- Provenance: check whether citations, quotations, statistics, images, or other referenced material can be traced to genuine sources.
- Bias and safety: consider whether the result introduces stereotypes, unsafe instructions, or inappropriate assumptions.
- Privacy: avoid entering confidential or personal information unless the service and organizational policy permit that use.
- Human review: use qualified review for high-impact domains such as health, law, finance, employment, education, security, or public safety.
Generative AI and intellectual property
Generated content can raise questions about copyright, trademarks, licensing, attribution, and similarity to existing material. The correct treatment depends on the content, jurisdiction, service terms, and intended use. For publishing workflows, it is safer to review originality, source rights, and required permissions rather than assuming that machine-generated material is automatically free of third-party rights.
A practical workflow for responsible use
A useful workflow is to define the task, provide only necessary context, generate a draft, verify important claims, review the output for privacy and rights issues, and then edit the result before publication or operational use. NIST’s Generative AI Profile treats risk management as a lifecycle activity rather than a one-time check, which is a helpful way to think about organizational use of generative systems.
Related KAWverse guides
- Large Language Models (LLMs): How They Work, Uses & Limits
- Prompt Engineering: Practical Guide to Better AI Prompts
- ChatGPT: What It Is, What It Can Do & Limitations
- Google Bard Is Now Gemini: What Changed & How It Works
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