Generative AI definition
Generative AI is a category of artificial intelligence that creates new content, such as text, images, code, audio or video, in response to a prompt. It relies on models trained on very large datasets, including large language models and diffusion models, which learn the patterns in that data and produce original outputs that follow them.
How does generative AI work?
Generative models learn the structure of their training data through self-supervised learning, which needs no human labels. A large language model learns by predicting the next token across enormous amounts of text. A diffusion model learns by adding noise to images and practicing how to remove it. After training, these models can produce new sequences or images that resemble, but do not simply copy, what they learned from.
Generation is a sampling process. The model produces a probability distribution over possible next tokens or pixel values and picks from it, with settings such as temperature controlling how adventurous the choice is. That is why the same prompt can give different answers, and why outputs are plausible rather than verified: the model optimizes for likely content, not for truth.
Types of generative AI models
- Large language models: text and code, such as the GPT, Claude and Gemini families and open-weight families such as DeepSeek, Qwen and Llama.
- Diffusion models: images and video, such as Stable Diffusion and similar systems.
- Speech and audio models: text-to-speech, voice cloning and music generation.
- Multimodal models: accept and produce combinations of text, images and audio.
- Code assistants: tools such as GitHub Copilot that generate and explain code inside editors.
Generative AI vs traditional AI
Traditional, or predictive, AI answers narrow questions about existing data: is this transaction fraud, how many units will sell next week. Generative AI produces new artifacts: a draft email, a product image, a code function. Predictive models are usually trained by each company on its own labeled data. Generative models are typically pretrained by a few large providers and then adapted through prompting, retrieval or fine-tuning.
Generative AI use cases and an example
Worked example: a software company's support team uses a generative assistant that reads each incoming ticket, retrieves relevant help articles and past resolutions, and drafts a reply with links to its sources. Agents edit and send the draft rather than writing from scratch. The assistant never sends anything on its own, and every draft is logged so quality can be reviewed.
- Drafting and summarizing emails, reports, meeting notes and contracts.
- Customer support assistants grounded in a knowledge base.
- Code generation, review and test writing.
- Marketing copy, product descriptions and image variations.
- Extracting structured data from unstructured documents.
Risks and responsible deployment
The main risks are hallucinated facts, leakage of sensitive data in prompts, prompt injection through untrusted content, unclear copyright status of some outputs, and running costs that grow with usage. Each has practical mitigations: retrieval-augmented generation for grounding, guardrails and access controls, evaluation suites run before each release, human review for consequential outputs, and spending limits per feature.
Nexzem builds generative AI features on client data with these controls in place from the first prototype, so a successful pilot does not need to be rebuilt to pass security review before launch, and running costs are known before the feature reaches every user.