This module dives into the engineering backbone of reliable LLM-powered applications - the API and middleware layer. Learners will understand how to interface effectively with LLM APIs by implementing rate limits, request retries, caching, and token cost control. Emphasis is placed on making LLM calls stable, scalable, and cost-efficient under production-like conditions. Real-world patterns are illustrated through examples in Python or Node.js, and the module concludes with a hands-on demo building a backend service that interacts robustly with the OpenAI API, ensuring consistent performance and predictable costs even under heavy user load.

This module bridges technical design and user experience - showing how the interface directly shapes model effectiveness. Learners will discover how thoughtful UI elements such as clarification prompts, feedback sliders, and reasoning displays turn a static LLM into an adaptive, user-centered system. The lesson explores best UX patterns for chatbots, text generation tools, and intelligent search assistants, highlighting how human-in-the-loop feedback improves both model accuracy and trustworthiness. The demo guides learners through building a minimal React-based frontend that connects to the backend created earlier, visualizes responses dynamically, and incorporates live user feedback for iterative model improvement. This module emphasizes human-centered interaction design and adaptive UI patterns that enable continuous model learning and improved user trust.