AI-Native Development for Flutter Teams: Shipping Faster with Claude Code & Cursor

How do you use AI coding assistants like Claude Code and Cursor in a Flutter project?

AI-native development means treating tools like Claude Code and Cursor as part of your everyday workflow rather than an occasional autocomplete. You feed them the right context about your widgets, state management, and project structure so they can scaffold screens, refactor across files, and handle repetitive Dart boilerplate. This course walks through setting these tools up and applying them to real Flutter workflows.

What makes a good prompt for generating code?

Good code prompts are less about clever wording and more about context engineering: giving the model the relevant files, constraints, conventions, and the actual goal so it doesn't guess. You describe the desired behaviour, point to existing patterns to follow, and specify what not to touch. These prompting and context techniques are the focus of a dedicated unit.

What is agentic development and how does it handle changes across multiple files?

Agentic development lets an AI tool plan and execute a multi-step task on its own, editing several files, running commands, and checking its work before handing back. Instead of pasting snippets one at a time, you give it a higher-level goal and review the resulting change set. The course covers how to drive this safely for larger refactors and features.

How can a development team keep code quality up while using AI to move faster?

Speed only helps if it doesn't create a mess, so teams agree on shared practices: reviewing AI-generated changes, keeping tests in the loop, and defining where the tools are and aren't trusted. Velocity comes from consistent workflows across the team, not from any single person's tricks. A full unit is devoted to team practices, quality, and velocity.

What are the security and cost risks of using AI coding tools?

Key concerns include what code and secrets you send to a model, licensing and provenance of generated code, and the running cost of heavy agentic usage. Responsible use means setting boundaries on sensitive data, verifying output rather than trusting it blindly, and being deliberate about how much compute you spend. These trade-offs are covered under security, cost, and responsible use.