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Generative AI turned every knowledge worker into a supervisor of software that writes, designs, codes and reasons. Learn to work with — and build on top of — the models reshaping every industry.
What is Generative AI?
Generative AI refers to models that create new content: text, code, images, audio, video. The current wave is dominated by large language models (LLMs) like GPT-4, Claude, and Gemini for text, and diffusion models like Stable Diffusion and Midjourney for images. Prompt engineering, retrieval-augmented generation (RAG), and agent frameworks are the working vocabulary.
Why learn Generative AI?
Every product roadmap in 2026 has a generative-AI line item. Teams that can move past 'generate a paragraph' into structured RAG, evaluation, and agent design ship features their competitors can't. Even non-builders benefit — the leverage is enormous.
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Generative AI — frequently asked questions
Generative AI is a subset of AI focused on producing new content (text, code, images, audio). Traditional AI often focuses on classification, prediction, or optimisation — deciding rather than creating.
For most jobs, no. The largest and most useful models are consumed as APIs. Training only comes in when you have proprietary data and a specific fine-tuning need — most GenAI engineering work is above the model, not inside it.
It's increasingly a component of many roles rather than a stand-alone job. Being strong at prompting + evaluation + basic Python is a very hirable combination.
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