Quick answer
Generative AI is AI that creates new content, such as text, images, audio, video or code, from a prompt. NIST defines it as models that "generate derived synthetic content." This page lines up 5 sources, from NIST to NVIDIA, with the limit each states, plus what to check before you publish anything. Not legal advice.
Key takeaways
- NIST's glossary defines generative AI as 'the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content.' IBM's wording: AI that can create original content 'in response to a user's prompt or request.'
- The 5 sources state different limits. AWS and IBM name inaccurate output for generative AI, Google Cloud's general AI page says poor training data can lead to biased or inaccurate outputs, NVIDIA's challenges section covers compute, speed, data quality and data licenses, and NIST's glossary entry states none.
- IBM says the same inputs can give slightly or significantly different outputs, so one good test run settles little. Check facts in text; check rights and likeness in images and audio.
- In its January 29, 2025 NewsNet, the US Copyright Office says it concludes that outputs of generative AI can be protected by copyright only where a human author has determined sufficient expressive elements. Not legal advice.
- This site's reading: ask the seller what is generated, who checks it, what happens to your data and what the contract says about ownership. Not legal advice.
What is generative AI?
Generative AI is AI that creates new content, such as text, images, audio, video or code, from a prompt; NIST's glossary calls it models that "generate derived synthetic content." Sources also call it gen AI.
NIST's glossary gives the formal version, which it credits to NIST SP 800-218A: "the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content." It adds, "This can include images, videos, audio, text, and other digital content." IBM words it for a lay reader: AI that can create "original content such as text, images, video, audio or software code" "in response to a user's prompt or request."
A "prompt" is your instruction: a sentence, question or pasted document. In this site's plain words, the contrast the sources draw (AWS: "discriminative or focused on classifying data points"; Google Cloud: a type of AI that "doesn't just analyze data") is with AI that sorts or scores data you already have.
How do five sources define generative AI, and what limit does each state?
They agree on the core idea, creating new content, and differ on what they warn about. NIST is a US standards agency and the other four are companies; each is quoted in the same three slots, from pages read on October 7, 2026. For Google Cloud the page read is its general "What is Artificial Intelligence (AI)?" explainer, which has a short generative AI paragraph; we found no dedicated generative AI explainer (the /learn/ and /discover/ addresses we tried gave 404), so its row rests on less text.
| Source and what it is | Definition, as the page words it | What it says is produced | Limit it states |
|---|---|---|---|
| NIST glossary (US standards agency; definition credited to NIST SP 800-218A) | "the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content" | "images, videos, audio, text, and other digital content" | Not stated in the glossary entry |
| AWS explainer (cloud provider) | "a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music" | "conversations, stories, images, videos, and music" | "generative AI systems can sometimes produce inaccurate or misleading information" |
| IBM explainer (technology vendor) | "artificial intelligence (AI) that can create original content" "in response to a user's prompt or request" | "text, images, video, audio or software code" | "An AI hallucination is a generative AI output that is nonsensical or altogether inaccurate but, all too often, seems entirely plausible." |
| Google Cloud "What is AI" explainer (cloud provider; its generative AI paragraph) | "a type of AI that doesn't just analyze data; it creates new content" | "an AI artist, writer, or even coder" | From the same page, about narrow AI, a category it says includes generative AI models: "poor training data can lead to biased or inaccurate outputs" |
| NVIDIA glossary (chip and software vendor) | "Generative AI enables users to quickly generate new content based on a variety of inputs." | "can include text, images, sounds, animation, 3D models, and other types of data" | Its section "What Are the Challenges of Generative AI?" says "Generative models require high-quality, unbiased data to operate." Errors in generated output: not stated on the page we read. |
One thing is worth reading across the rows. A page listing fewer limits is not safer: NVIDIA's challenges section is about building and training models, and NIST's entry is a glossary definition, so a missing limit there reflects the format, not the safety of the tools.
How is generative AI different from AI, machine learning and LLMs?
Generative AI is one kind of AI, defined by what it produces: new content. Machine learning and LLMs are the other terms people mix it up with. The middle column quotes a source; the last is this site's clarification.
| Term | A sentence from a source | This site's clarification |
|---|---|---|
| AI | Google Cloud: "Artificial intelligence (AI) is a set of technologies that empowers computers to learn, reason, and perform a variety of advanced tasks in ways that used to require human intelligence" | The wide label that covers the rest. |
| Machine learning | Google Cloud: "This is a type of AI where systems learn from data to identify patterns and make predictions or decisions without direct programming." | A way of building AI. |
| Traditional (classifying) ML | AWS: "Traditional machine learning models were discriminative or focused on classifying data points." | Software that sorts or scores data you already have. This page calls that "predictive" for short; the sources' words are "discriminative" and "classifying." |
| Generative AI | Google Cloud: "a type of AI that doesn't just analyze data; it creates new content" | Defined by its output: text, images, audio, video or code. |
| LLM | Google Cloud: "LLMs are large AI models trained on massive datasets of text and code." | A model type mainly used for language (Google Cloud's page adds that LLMs are becoming multimodal). Generative AI is the wider label, because NVIDIA's page lists images, sounds and 3D models too. |
For assistants that take actions, see AI agents for small businesses.
Where does a small business meet generative AI?
This site's reading: a typical place is a drafting step inside tools you may already use, such as a suggested email reply or a first social post. Guides: AI email responder for small business and AI social media content generator.
For how many businesses use AI at all, see What Do Businesses Use AI for?. That page's Census-based study counts businesses that used AI in a business function and has no item for generative AI, so this page quotes no usage figure.
What should you check before using generated text, images or audio?
Check four things before anything generated reaches a customer: the facts, who owns it, whether it fakes a person, and what you claim about it. This is this site's checklist, not legal advice.
- Facts. AWS says generative AI systems "can sometimes produce inaccurate or misleading information," and IBM says "the same inputs can result in slightly or significantly different outputs," so one good test settles little. Check every price, date, name and quote against your own records. Google Cloud's "it's always wise for developers to review and validate AI-generated code" is about code, but the habit fits text.
- Ownership. In NewsNet issue 1060 (January 29, 2025), releasing Part 2 of its AI report, the US Copyright Office says it "concludes that the outputs of generative AI can be protected by copyright only where a human author has determined sufficient expressive elements." Such elements can include a human making "creative arrangements or modifications of the output, but not the mere provision of prompts." The Office also confirms that using AI to assist, or including AI material in a larger human work, "does not bar copyrightability." This is about copyright protection only, not infringement. Not legal advice.
- Fake people. The FTC's final rule, announced August 14, 2024, addresses reviews and testimonials that misrepresent that they are by someone who does not exist, "such as AI-generated fake reviews," or who did not have actual experience with the business, and it prohibits businesses from creating or selling such reviews. This site's advice: do not use a generator to write customer reviews. IBM defines deepfakes as "AI-generated or AI-manipulated images, video or audio"; a cloned voice or face of a real person is a separate risk.
- Claims. On September 25, 2024, announcing five enforcement actions (Operation AI Comply), then-FTC Chair Lina M. Khan said that "the FTC's enforcement actions make clear that there is no AI exemption from the laws on the books." That is an announcement of actions, not a ruling on any of them; check what you say your product or result does.
What should you ask a seller of a generative AI tool?
Ask these six questions in writing. They are this site's own.
- Which parts of the output are generated, and which come from my own records?
- Does it show a draft for approval before sending anything to a customer?
- Is my text, photo or recording used to customize or improve the model? AWS raises a related concern: "Data privacy and security concerns arise if proprietary data is used to customize generative AI models."
- Can it write reviews or testimonials, or copy a real person's voice or face?
- What does the contract say about who owns the output?
- What accuracy claims do you make, and what test supports them?
How was this page made?
All pages were read on October 7, 2026 from outside the United States. This page was drafted with Claude, an AI model made by Anthropic. Neither Anthropic nor OpenAI is a source here; ChatGPT appears only inside a Google Cloud quotation.
Next step: run the four checks on one generated draft you already use.
FAQ
What is generative AI in simple terms?
It is AI that produces new content, such as text, images, audio, video or code, when you give it a prompt. IBM describes it as AI that can create original content 'in response to a user's prompt or request,' and NIST's glossary defines it as models that 'generate derived synthetic content.'
Is ChatGPT a generative AI?
Yes, by Google Cloud's description: 'Tools like DALL-E for images and ChatGPT for text are prime examples.' That is one vendor's description.
What is the difference between traditional AI and generative AI?
AWS says traditional machine learning models 'were discriminative or focused on classifying data points,' while generative models create new content. Google Cloud calls generative AI 'a type of AI that doesn't just analyze data; it creates new content.' Both are vendor descriptions.
Is generative AI the same as an LLM?
Not as the sources use the words. Google Cloud defines LLMs as 'large AI models trained on massive datasets of text and code,' while NVIDIA's page says inputs and outputs of generative models 'can include text, images, sounds, animation, 3D models, and other types of data.' This site's reading: an LLM is one kind of model mainly used for text, and generative AI is the wider label.
Can I use generative AI output in my marketing?
This page cannot say for your case. The US Copyright Office ties copyright protection to a human author's sufficient expressive elements, the FTC's fake-review rule addresses reviews by someone who does not exist, such as AI-generated fake reviews, and AWS and IBM warn about inaccurate output. Check facts, rights and claims first. Not legal advice.