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Generative AI Demonstration

This additional page (genai.html) showcases how prompt iteration steers a generative model. We begin with a minimal prompt, add constraints across a few steps, and stop when the output meets our goals. The final prompt you approved appears in a text box below for transparency.

Prompt → Output (Iterative Refinement)

Step 1 — Simple prompt

Explain what generative AI is.
Model output:

Generative AI refers to systems that can create new content—such as text, images, audio, or code—by learning patterns from existing data. These models don’t just retrieve information; they synthesize novel outputs based on a prompt.

Step 2 — Add audience & structure

Explain what generative AI is for a college audience and include three bullet points of common capabilities.
Model output:

Generative AI is a branch of machine learning where models are trained to produce new data similar to what they learned from. For college projects, it can accelerate drafting, brainstorming, and prototyping.

  • Text generation (summaries, explanations, creative writing)
  • Code generation (snippets, refactors, documentation)
  • Media synthesis (images, audio, design concepts)

Step 3 — Add tone & constraints

Explain what generative AI is for a college audience, with three bullet points of capabilities, in a clear, non‑technical tone, and keep it under 120 words.
Model output:

Generative AI is software that makes new content after learning patterns from lots of examples. Instead of copying, it creates original text, images, or code based on your prompt.

  • Write and summarize: essays, emails, study guides.
  • Help with coding: examples, comments, quick fixes.
  • Create visuals/audio: mockups, diagrams, outlines.

Step 4 — Add an example & safety note

Same as before, but also include one concrete example for students and a brief responsible‑use note