Most Flutter AI talks focus on connecting a model and displaying responses. The real challenge begins after that.
As AI features move into production, teams quickly encounter new problems: slow response times, rising token costs, inconsistent outputs, excessive context growth, and poor user experience on unreliable networks.
In this session, we'll dive into the engineering patterns used to build scalable AI-powered applications in Flutter. We'll explore how to design efficient prompt and context pipelines, implement intelligent response caching, reduce LLM costs, improve perceived performance, and instrument AI features with meaningful telemetry.
Using real-world architectural patterns inspired by production-scale applications, we'll walk through practical approaches for building responsive and cost-effective AI experiences without sacrificing quality.
Whether you're adding a simple assistant to your app or building an AI-first product, you'll leave with a set of proven techniques that can dramatically improve performance, reliability, and user satisfaction.

What you'll learn

- Design scalable prompt and context management architectures
- Reduce AI response latency through intelligent caching strategies
- Optimize token usage and lower LLM operating costs
- Measure AI feature quality using telemetry and observability
- Build resilient AI experiences that gracefully handle network and service failures