When calling an LLM or running a complex ML model, the app can feel unresponsive. I’m currently using Provider, but I’m struggling with managing the "Loading" and "Error" states for multiple AI requests. Would switching to BLoC or Riverpod make it easier to handle these asynchronous AI workflows?
3 answers
For AI apps, Riverpod is excellent because of FutureProvider and StreamProvider. They have built-in .when() methods that force you to handle the loading, error, and data states explicitly. Since AI responses can take time or fail due to network issues, this structure is a lifesaver. BLoC is also great if you prefer a more rigid, event-driven architecture, which helps in tracking exactly what the AI is doing (e.g., AnalyzingImageState, FetchingResponseState). Both are superior to basic Provider for complex async logic because they help decouple the "thinking" logic from your UI widgets.
Does Riverpod handle the case where a user navigates away from a screen while an AI request is still pending? I don't want to waste API tokens on a response the user won't see.
I use BLoC because the "Events" make it very clear to my team when a user has clicked 'Generate' versus when the model has naturally finished its task.
BLoC’s predictability is hard to beat, Diana. For enterprise apps where you need to log every AI interaction for audit trails, it’s probably the best choice.
Patrick, that’s one of Riverpod’s biggest strengths! When a provider is no longer being "listened" to (like when a user leaves the screen), it can be set to autoDispose. This will automatically cancel any active network requests or stream listeners. If you're using the http package, you can pass a CancellationToken to ensure the server-side work is also stopped where possible. This is a crucial "SEO-friendly" tip for cost management in production-grade AI mobile applications.