Your AI feature is a chat widget in the corner. Your users can tell.

Most React teams treat AI integration like any other API — fetch a response, render it, done. But LLM-powered features break every assumption we rely on: responses stream unpredictably, latency is measured in seconds not milliseconds, outputs are non-deterministic, and "retry" doesn't give you the same answer twice.

This session covers the production-tested architectural patterns that make AI features feel native to your application — not bolted on as an afterthought.

You'll learn:
- Streaming UI composition with Suspense boundaries and Server Components
- State architecture for conversational context, window limits, and multi-turn interactions
- Why traditional optimistic updates break for probabilistic operations (and what to use instead)
- Graceful degradation strategies: expensive model → cheaper model → no AI
- Production operations: observability, cost management, and testing non-deterministic outputs

Perfect for: React developers shipping AI-powered features, frontend architects evaluating LLM integration patterns, and anyone whose "add AI" ticket turned out to be harder than the demo suggested.

Visual walkthroughs: Real architectural patterns for streaming LLM responses, side-by-side comparisons of bolted-on vs. AI-native approaches, and progressive breakdowns from "it works in the demo" to production-ready architecture.

Walk away with: Streaming UI patterns using Suspense and Server Components, a state management strategy for conversational AI features, error boundary patterns for when AI fails or hallucinates, and a graceful degradation playbook that keeps your app useful even when the model is down.