Applied Generative AI Tools & Data

This unit develops hands-on skills for using generative AI tools (e.g., Copilot, GitHub Copilot, Tabnine) and managing data to produce reliable, targeted outputs. Students will practice prompt engineering, create visual and written assets, configure and test plugins/agents, and evaluate model limitations (including hallucinations), preparing them for advanced agent design and deployment in later units.

Learning Objectives

• Analyze the capabilities and limitations of popular generative AI tools (Copilot, GitHub Copilot, Tabnine) and compare their suitability for workplace tasks such as coding, customer service, and HR support
• Apply data-management and data-preparation techniques to clean, version, and curate datasets that improve model output relevance and reduce hallucinations
• Demonstrate prompt-engineering strategies by producing, iterating, and evaluating prompts to generate accurate text, visual assets, and presentation slides for specified use cases
• Create and test a simple Copilot plugin or conversational agent, and evaluate its performance using user-centered testing criteria, localization, SEO considerations, and responsible-AI checks