Conversational AI UX Integration

This unit explores how to design, prototype, and evaluate chat-centered user experiences that integrate AI capabilities such as autocomplete, next-step suggestions, and plugin-driven continuous learning. Students will learn promptbook practices, the role of data, and rapid ‘spike’ techniques to reduce risk, improve reliability, and build user trust when deploying AI features.

Learning Objectives

• Analyze chat-focused information architectures and UX patterns to identify usability gaps and recommend design improvements for AI-driven autocomplete and suggestion features
• Apply prompt engineering and promptbook techniques to create reproducible prompts and templates that improve model consistency for defined user tasks
• Evaluate plugin-based continuous learning approaches, data usage, and mitigation strategies (including detection of hallucinations and misinformation) for alignment, privacy, and user trust using defined criteria
• Demonstrate rapid prototyping (‘spike’) methods to test AI behaviors—such as autocomplete and next-step flows—using sample datasets and measurable success metrics