Generative AI Tools and Prompting

This unit examines a range of specialized generative models (audio, image, code, video, and face synthesis) and how prompt design influences their outputs, strengths, limitations, and future directions. Students explore practical use cases, ethical and bias-related concerns, and model-specific prompting strategies to prepare for applied integration in the final unit.

Learning Objectives

• Analyze the functional differences, output modalities, and prompt sensitivity of generative models such as MuseNet, BigGAN, Codex, RunwayML, DeepArt, Midjourney, Google Bard, and DeepFaceLab
• Apply model-specific prompting techniques to produce and document targeted outputs, demonstrating how prompt variations alter results across at least three different generative systems
• Evaluate ethical, cultural, and bias-related issues inherent to each model and propose concrete mitigation strategies for responsible use
• Demonstrate selection and justification of an appropriate generative model and prompt approach for a real-world scenario, considering capabilities, limitations, and downstream impacts