Types of Generative AI: Models, Outputs and Uses
Group Generative AI two ways: by what it produces and by how the model generates it. Each view answers a different question.
Generative AI can be grouped by the content it produces — text, images, audio, video, code or structured data — and by the model family that generates it, such as transformers, diffusion models, GANs and variational autoencoders. Many current products combine several of these in one system.
Two useful ways to classify Generative AI
A learner usually meets Generative AI through a product: a chat assistant, an image generator or a coding tool. Grouping by output describes what the product does for a user. Grouping by model family describes how the content is produced, which matters when you evaluate quality, cost or training needs.
The two views do not map one to one. Transformers now generate text, code and, in some systems, images or audio. Diffusion models are best known for images but are also used for audio and video. Treat the categories as a vocabulary for discussion, not a rigid taxonomy.
Types of Generative AI by output
| Output | Typical uses | What to check |
|---|---|---|
| Text | Drafting, summarising, answering questions over documents | Factual support, instruction-following and tone |
| Code | Suggesting functions, tests and explanations | Whether it compiles, passes tests and is secure |
| Images | Concept art, product mock-ups, design variations | Prompt adherence, artefacts and usage rights |
| Audio and speech | Narration, voice interfaces, music sketches | Clarity, consent for voices and misuse risk |
| Video | Short clips, animation drafts, explainers | Consistency across frames and disclosure |
| Structured or synthetic data | Test records, form extraction, data augmentation | Schema validity and privacy of the source data |
Transformer-based models
The transformer architecture uses attention to relate each part of an input to the others. Large language models are transformers trained to predict the next token, which lets them generate text and code one step at a time. Multimodal variants accept images or audio alongside text.
Transformers are the basis of most chat assistants and coding tools. Their main application risks are unsupported claims and missed instructions, so production systems typically add retrieval, output checks and human review where errors are costly.
Sources: Vaswani et al.: Attention Is All You Need. Checked 23 October 2026.
Diffusion models
A diffusion model learns to reverse a gradual noising process. At generation time it starts from noise and removes it step by step, guided by a text prompt or another condition. This approach underpins many image generators and is used for audio and video research as well.
Generation takes several steps, so latency and compute cost matter. Evaluate outputs for prompt adherence and visual artefacts, and check the licence and policy terms of the model before commercial use.
Sources: Ho et al.: Denoising Diffusion Probabilistic Models. Checked 23 October 2026.
GANs and variational autoencoders
A generative adversarial network trains two models against each other: a generator creates samples and a discriminator tries to tell them from real data. GANs produced many early high-quality image results and remain useful in some image and data-augmentation tasks, although training can be unstable.
A variational autoencoder compresses data into a smooth latent space and decodes samples from it. VAEs are often used for representation learning and as components inside larger systems; some image diffusion models, for example, operate in a latent space produced by an autoencoder.
Sources: Goodfellow et al.: Generative Adversarial Networks; Kingma and Welling: Auto-Encoding Variational Bayes. Checked 23 October 2026.
Which type should a learner start with?
- For software and business applications, start with text-generating transformers, prompting and retrieval.
- For design or media work, learn how diffusion-based image tools are prompted, edited and evaluated.
- For research or model-building roles, study the training objectives of each family and implement a small example.
- In every case, practise evaluating outputs against a written requirement rather than judging a single impressive sample.
Common questions
What are the main types of Generative AI?
By output: text, code, images, audio, video and structured data. By model family: transformers, diffusion models, GANs and variational autoencoders. Many products combine more than one.
Is ChatGPT a type of Generative AI?
ChatGPT is a product built on transformer-based language models, so it is an example of text-generating Generative AI. Some versions also accept or produce images and audio.
Are GANs still used?
Yes, in some image, data-augmentation and research settings, although diffusion models and transformers are now more common in widely used generation products.
