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AI & Digital Arts · MS elective · 3 credit hours · 16 sessions

Code & Canvas: Prompting Creativity with AI

This course bridges the gap between language and visual output. It treats “Prompt Engineering” not just as typing words, but as a form of coding (“Prompt Programming”). Students will learn the syntax, parameters, and weights of various AI models. They will also explore lightweight coding (Python/Scripts) to automate prompts and explore the creative possibilities of randomness, iteration, and algorithmic serendipity.

§ 01 By the end you can
  1. Master the syntax of prompt engineering (weights, negative prompts, parameters)
  2. Treat natural language as code to control stylistic output
  3. Utilize Python scripts for batch prompting and iterative generation
  4. Explore "Permutation Prompting" to generate vast variations of a concept
  5. Debug and refine prompts to eliminate hallucinations and artifacts
§ 02 Sixteen sessions

The whole semester.

  1. 01Introduction to Prompt Syntax
  2. 02The Logic of Tokens & Weights
  3. 03Negative Prompting: Subtractive Design
  4. 04Modifiers & Stylistic Keywords
  5. 05The "Seed": Controlling Randomness
  6. 06Prompting for Texture & Materiality
  7. 07Prompting for Lighting & Camera Angles
  8. 08 · MidtermMidterm Examination
  9. 09Advanced Parameters (--chaos, --weird)
  10. 10Permutation Prompts & Arrays
  11. 11Scripting: Automating the Canvas
  12. 12Multi-Prompting & Blending
  13. 13Reverse Engineering Images (Img2Txt)
  14. 14Cross-Model Prompting (MJ vs. SD)
  15. 15The Poetry of Code: Creative Writing
  16. 16Final Project: The Prompt Anthology

Midterm around session 8. Final project briefed at session 8 or 9 and developed in stages until the juries.

§ 03 Final project

Final Project: The Prompt Anthology

How it’s judged

Recommended reading
  1. The Art of Prompt Engineering (Community Wiki/GitBooks)
  2. Natural Language Processing with Python by Steven Bird
  3. DALL-E Prompt Book by Guy Parsons
  4. Words and Pictures by Quentin Blake
  5. Coders at Work by Peter Seibel (for the mindset of debugging)
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