Everyone can connect an LLM to a Flutter app in a weekend. The real challenge begins when that app reaches real users.

How do you reduce AI response latency? Control token costs? Handle unreliable networks? Reuse previous responses safely? Measure AI quality in production? Build experiences that feel instantaneous despite expensive backend models?

In this fast\-paced talk, I'll share practical engineering patterns used in large\-scale software systems that can be applied to AI\-powered Flutter applications. Drawing from experience building performance\-sensitive products at Microsoft and previously at Oracle, we'll explore how caching, context engineering, observability, and system design impact the success of AI features far more than the choice of model itself.

Attendees will leave with concrete techniques to build AI\-powered Flutter applications that are faster, cheaper, more reliable, and ready for production.

Key takeaway: Building an AI demo is easy. Building a production\-ready AI experience is an engineering challenge.