What is Generative AI?
Generative AI refers to machine learning systems that learn patterns from data and generate new content -- text, code, images, audio -- that did not exist before. Understanding the category before diving into specific tools is essential for any practitioner.
Definition
Generative AI models are trained to model a probability distribution over data. At inference time, they sample from that distribution to produce novel outputs that resemble -- but are not copies of -- their training data.
Key Model Families
Large Language Models (LLMs) for text and code. Diffusion models for images and video. Multimodal models that cross modalities. Each uses different architectures but shares the generative training paradigm.
How it Differs from Traditional AI
Traditional ML classifies or predicts from fixed categories. Generative AI produces open-ended outputs. This makes it far more flexible but also harder to evaluate, constrain, and guarantee correctness for.
Where Value is Created
Generative AI creates value by accelerating human tasks that previously required scarce expertise: writing, coding, analysis, summarisation, planning. The economic impact is in throughput and access, not replacement of judgment.
The Generative AI Landscape
This programme focuses on LLMs and code generation tools. Complete the AI Foundations track first, then choose your tool track based on your role and the technology your organisation has adopted.