Generative AI is a type of artificial intelligence that can create new content in response to a request. Depending on the system, that content may be text, images, audio, video, or code. Ask an assistant to turn your notes into a meeting brief or give an image tool a description of a scene, and you are using generative AI.
The practical opportunity is not simply to make more content. It is to turn information into something useful: a first draft, an explanation, a comparison, or a clearer next step. The person using the output still needs to check whether it is accurate and appropriate for the job.
What does generative AI mean?
Generative AI describes systems designed to generate content rather than only classify, retrieve, or predict a label. A traditional system might identify whether a message belongs in a category. A generative system might produce a proposed reply to that message. Some products combine both approaches.
Google Cloud's AI glossary describes generative systems as using trained models to produce new content across formats. The result depends on the model, the information it receives, and the instructions you give it. A confident answer is not the same thing as a verified answer.
How does generative AI work?
At a high level, a generative model learns patterns from training data. When you give it an input, it uses those patterns and the current context to produce an output. A text model, for example, generates a response a piece at a time based on its learned patterns and the information in the conversation. Image and audio systems use different model designs; not every generative tool works in precisely the same way.
Think of the process in four steps:
- Ask for a job. Give the system a request such as “summarize these approved meeting notes.”
- Provide context. Include the relevant information and the format you need, subject to your organization's data rules.
- Review the result. Check the summary against the notes, especially names, figures, commitments, and missing context.
- Refine or act. Correct the output, request a revision, and decide what a person should do next.
Some products can also retrieve permitted information or connect to tools. Those capabilities depend on the product, configuration, and permissions; they should not be assumed from the words “generative AI.”
What are examples of generative AI?
Generative AI can help with several different kinds of work:
- Text: Draft an email, explain a concept, or summarize a document you provide.
- Images: Create a visual concept from a description and iterate on its composition.
- Code: Suggest a small program or explain an unfamiliar function for a developer to review.
- Audio and video: Generate or transform supported media in a tool designed for that purpose.
- Structured work: Turn supplied notes into a checklist or a table of open questions.
These examples show possible applications, not guarantees that every tool supports every format. The right tool is the one suited to the task, the data you can share, and the review you can provide.
What are the types of generative AI?
There is no single universal list of “four types.” One useful way to compare tools is by the kind of output they are designed to produce: text, images, audio, video, or code. Another is by model architecture, such as language models and diffusion models. The categories overlap: one product may support more than one output, and code can be generated by models that also produce text.
For most teams, the more practical question is: What goes in, what comes out, and how will we verify it?
Generative AI vs. AI agents: what's the difference?
Generative AI creates an output such as a draft or summary. An AI agent is designed to pursue a task across one or more steps and may use available tools or information along the way. An agent can use a generative model as part of its work, but generating text alone does not necessarily mean a system is acting as an agent.
For example, an assistant might draft a reply from notes you paste into a chat. An agent with the right available connections might first gather permitted context, prepare a draft, and return it for review. The actual sources and actions depend on the specific system. IBM's comparison describes generative and agentic approaches as related rather than mutually exclusive.
How businesses can use generative AI
Start with a task that has a clear input and an output a person can check. Here are five examples:
| Job | Input | Useful output | Human review |
|---|---|---|---|
| Prepare a meeting | Approved notes and meeting objective | A short brief and open questions | Confirm the facts and the audience. |
| Organize research | A set of permitted source excerpts | A comparison of findings and gaps | Check each important claim against its source. |
| Draft a follow-up | A conversation recap and next step | An editable email draft | Verify the recipient, commitments, and tone. |
| Explain a topic | A question and the reader's context | A plain-language explanation | Check important details against reliable sources. |
| Prepare a checklist | A documented process | A first-pass task list | Confirm the list fits the actual process. |
For a fictional example, imagine a founder preparing for a customer call. They supply approved notes from an earlier conversation and ask for three questions about the customer's current workflow. A helpful response separates what the notes actually say from questions that still need an answer. The founder reviews it before the call; the AI is helping prepare, not claiming to know the customer.
A copyable generative AI brief
Use this prompt with an assistant that can accept the information you are authorized to share:
Help me [specific task] for [audience].
Use this context: [approved notes, source material, or relevant background].
Return [the format and sections you need]. Separate supported facts from assumptions, and flag important information that is missing.
Do not invent names, numbers, quotes, or commitments. Include what I should verify before I use or share the result.
Review the answer against the source material, then make the brief more specific if the output is too general. A good prompt is a starting point for working together, not a substitute for judgment.
What are the limitations and risks?
Generative AI can produce a fluent answer that is wrong, incomplete, outdated, or inappropriate. It may also reflect bias in its data or output, and sensitive information needs appropriate handling. NIST's Generative AI Profile identifies risks that organizations should consider when developing and using these systems.
Before using an output:
- Check important facts against the underlying source, especially figures, quotations, and current claims.
- Protect information by using only tools and connections approved for the material you share.
- Review for fairness and context rather than treating an output as an independent judgment about a person.
- Keep consequential decisions with a person and verify the intended action, audience, and permissions before anything is sent or changed.
- Be transparent about uncertainty. If a source is missing or the answer cannot be checked, say so.
What are the top generative AI tools?
There is no single top three for every use. Compare tools by the task you need to complete, supported inputs and outputs, the quality of their results on your own examples, data handling, cost, and the amount of review required. A writing assistant, image generator, and coding tool may all be useful, but they solve different problems. Test a shortlist on the same small set of tasks rather than choosing from a headline ranking alone.
Bring context and follow-through together
Generative AI is most useful when its output helps move real work forward. Aurora works across available Finta workspace context and supported connections to help prepare the next step. A team might use it to prepare for a relationship conversation, review the context and draft, and decide what to do next. Available sources and actions depend on your setup and permissions.
Start with one useful job, make its context clear, and keep a person responsible for the result.