Most of us copy a Gemini API call from a blog post and ship it as-is; no error handling, no abstraction, no tests. This course changes that. Across 10 structured lessons, you start with your first Gemini API call and work through a clean provider abstraction, sealed error types, BLoC state management, context management, and testing. Every architectural decision is explained before it is written, and every concept is grounded in real, runnable Flutter code. By the time you reach the finale, you will have a fully tested Flutter app with streaming AI, offline resilience all built on a single, swappable AiProvider interface.
What You'll Build
A fully functional AI-powered notes app built in Flutter featuring streaming responses, a token-aware context manager, a semantic response cache, cost and latency monitoring all running through a single swappable AiProvider interface backed by the Gemini free tier.
Prerequisites
- Working knowledge of Flutter and Dart (widgets, async/await, null safety)
- Familiarity with BLoC or another state management approach
- Basic Supabase knowledge (project setup, tables, RLS) helpful but not required
- No prior AI or LLM integration experience required
What You'll Learn
- Make your first Gemini API call and stream tokens to a Flutter UI
- Design a sealed AiFailure type that handles every failure mode gracefully
- Build an AiProvider interface that lets you swap AI models without rewriting your app
- Manage conversation context and token budgets across multi-turn sessions
- Cache AI responses semantically using cosine similarity and Supabase pgvector
Who This Is For
Flutter developers with working knowledge of the framework who want to integrate AI features properly with clean architecture, real error handling, testable abstractions, and a production monitoring layer.
The problem with most AI integrations
Most Flutter developers copy a Gemini API call from a blog post and ship it as-is with no error handling, no abstraction, no tests. It works in the demo. It breaks in production the first time the network drops, the rate limit hits, or the model refuses a prompt.
Intelligent Flutter fixes that. Across 10 structured episodes, we build a fully tested AI notes app with streaming responses and offline resilience.
What you'll build
A complete AI-powered notes app running on a single, swappable AiProvider interface, so switching models later is a one-file change. You'll design a sealed AiFailure type that handles every failure mode gracefully and build a token-aware conversation context manager.
Why it's different
Every architectural decision is explained before it's written. Every concept is grounded in real, runnable code. And it covers what most AI tutorials skip: testing without a live API and, cost and latency monitoring.
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