Definition: Generative AI is a class of machine learning models that create new content, such as text, images, code or audio, by learning patterns from large training datasets and producing outputs in response to a prompt.
Most text systems are large language models that predict the next token in a sequence. Image and video systems often use diffusion models that turn random noise into a picture step by step. Both start as foundation models trained on broad data, then get adapted with instructions, retrieval or fine-tuning.
Drafting and summarizing documents, answering questions over internal knowledge, writing and reviewing code, extracting fields from forms, generating marketing variants and powering assistants that take actions through tools.
Predictive models score or classify existing data, such as a fraud probability. Generative models produce new artifacts. Many production systems combine both: a classifier flags a case and a generative model writes the explanation.
Outputs can be fluent but wrong (hallucination), can leak sensitive data, and vary from run to run, so teams need evals, guardrails and human review where stakes are high.
Ryz AI pods build generative AI systems in your cloud and repos, from retrieval to evals, working on your team. See generative AI development.
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