Foundations of Prompt Engineering

This unit introduces core concepts of artificial intelligence, machine learning, and deep learning and traces the origins and role of prompts in modern AI systems. Students will learn how prompts guide output, how to provide data inputs across platforms, and foundational techniques for crafting, testing, and iterating accessible and ethical prompts to prepare for advanced optimization and application topics in later units.

Learning Objectives

• Analyze the differences between artificial intelligence, machine learning, and deep learning, and explain how each paradigm influences prompt behavior and system outputs.
• Demonstrate methods for providing data input and using platform-specific prompt formats by creating and submitting three distinct prompt examples (e.g., plain-text, structured, multimodal) across two platforms.
• Apply step-by-step prompt construction principles — including context, clarity, conciseness, and component selection — to author and refine at least five initial prompts for targeted tasks.
• Evaluate prompt performance through testing and iteration, identify common mistakes and potential bias or accessibility issues, and revise prompts to meet documented ethical and accessibility criteria for at least two use cases.