Generative AI
Generative AI learns patterns from existing data, then uses those patterns to create new text, images, audio, video, code or other content.
Generative AI is the part of machine learning that creates content. Instead of only predicting a category or number, it can produce a passage of text, an image, a sound, a video, software code or a mixture of formats.
During training, a generative model learns recurring relationships in its examples. A prompt then conditions what should come next. The model produces a new sample that fits both the prompt and its learned statistical patterns. Different model families do this differently. Language models commonly predict a next token. GANs pit a generator against a judge model called a discriminator. Diffusion probabilistic models are another family used for image synthesis.
The result follows learned statistical patterns, which do not guarantee factual accuracy. That makes the technology useful for creating and transforming content, but it also explains an important limit. A plausible pattern can still produce a false statement, inconsistent reasoning or a fabricated citation. Treat the output as a proposal to inspect, not as evidence that its claims are true.
Some tasks need a new piece of content, not just a label or number.
Learn a distribution, then create one new sample from it.
- 1 · learnTraining exposes the model to many examples so it can learn statistical patterns in the data.
- 2 · promptA person supplies text, an image, audio or another input describing the wanted result.
- 3 · generateThe model uses the prompt and its learned distribution to produce a new content sample.
- 4 · reviewA person or system checks the output because generated content can be inaccurate, inconsistent or harmful.
The model generates a new sample shaped by learned patterns and the prompt.
| Who | What they ask | What it works with |
|---|---|---|
| Marketing team | “Draft three versions of this product description.” | Brief, brand guidance and examples |
| Designer | “Show this chair in a bright reading room.” | Product image and visual prompt |
| Developer | “Write tests for this function.” | Function, requirements and nearby code |
| Video editor | “Turn this storyboard into a rough animated clip.” | Storyboard, timing and style references |
- It can create text, images, audio, video and combinations of these from user input.
- It can transform existing content by summarizing an article or editing an image.
- It can turn one kind of input into another, such as a picture into a written description.
- It can be further trained for specific tasks after its initial pattern learning.
- Statistical plausibility does not guarantee that an output is factually accurate or internally consistent.
- An answer can sound certain and still be wrong, and its reasoning or citations can be made up.
- Training patterns can carry harmful biases into generated outputs.
- Doing well on tests written for people does not prove a system is reliable for a real job; experts in that field still need to check it where it will be used.
Sources used
This explainer is written in original language. The links below support its factual claims.
- docsWhat is Machine Learning?, Google for Developers · read 27 Sept 2026
- docsBackground: What is a Generative Model?, Google for Developers · read 27 Sept 2026
- officialArtificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, National Institute of Standards and Technology · read 27 Sept 2026
- paperGenerative Adversarial Networks, Goodfellow et al. · read 27 Sept 2026
- paperDenoising Diffusion Probabilistic Models, Ho, Jain and Abbeel · read 27 Sept 2026