Generative AI Foundations – Diffusion, Attention, and Creative Systems
Lesson 1: Generative AI as a Production Pipeline
Lesson Objectives
By the end of this lesson, students should be able to:
- Map a generative AI workflow from request to review.
- Name the difference between generation and verification.
- Explain why creative output still needs human judgment.
Lesson Content
Generative AI creates new output from a request, context, learned patterns, and tool settings. The output might be text, an image, audio, code, a video concept, or a mix of media. The pipeline usually includes a prompt, model generation, tool-specific controls, postprocessing, and review.
Generation is not verification. A model can create a believable image of something that never happened or write a confident paragraph with invented details. That is why creative AI needs a review standard just like business writing or code.
Students should learn to ask: What am I generating? What facts or rights are involved? What must be accurate? What is only style? What could mislead someone?
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