Master the AI-Native Flutter Development Workflow

how do you use claude code and cursor to write flutter code?

You ground the tools in your project's context first, then specify each Flutter feature as a clear prompt rather than expecting the AI to guess your intent. Complex work is planned before any generation begins, and you iterate and steer the output instead of accepting the first draft. This course walks through setting up Claude Code and Cursor and directing them to produce real Flutter code.

how do you review AI-generated code you didn't write yourself?

Treat review as your core job rather than an afterthought: read AI output critically, direct the AI to build test coverage around it, and run user acceptance testing to a high standard before anything ships. Security and performance still need to be enforced by you, since the AI won't own those trade-offs. The engineer stays accountable for quality even when the machine wrote most of the lines.

what does it mean to work as an AI orchestrator instead of writing every line?

Your role shifts from typing code to directing it: you specify features, plan the work, steer the generation, and own the review, testing, and UAT. The AI handles roughly the bulk of the code while you hold responsibility for whether it's correct, secure, and performant. It's a reframing of the senior developer's job around judgment rather than keystrokes.

how do you run multiple AI coding agents at the same time?

You orchestrate several agents in parallel and give them enough structure to work across a large codebase without stepping on each other. For heavier work you provision remote machines as agent farms and run full build and test suites there rather than on your laptop. This covers scaling from a single agent up to coordinated remote agents.

how do you fit AI code generation into a CI pipeline?

AI workflows can be automated in continuous integration so that generation, testing, and checks run as part of your normal pipeline rather than by hand. This pairs with directing the AI to build test coverage and enforcing security and performance standards automatically. The goal is shipping a full AI-native feature through a repeatable, reviewed process.

how do you write good prompts for building app features?

Specify the feature as a clear, concrete prompt and plan complex work before you ask for any generation, so the AI has the context it needs. Then iterate and steer the output, correcting course as it goes rather than expecting one perfect result. Grounding the tools in your existing project context makes those prompts far more reliable.