AI-Native Flutter Development: Boosting Velocity with Claude Code & Cursor

How do you use AI coding tools like Claude Code and Cursor in a real codebase?

Beyond autocomplete, these tools work best when you give them the right context: relevant files, project conventions, and a clear description of the outcome you want. Agentic workflows let the tool plan, edit across multiple files, and iterate on a task rather than answering one line at a time. This course walks through setting them up and building agentic workflows directly inside a Flutter codebase.

How do you write good prompts for generating code?

Effective code prompts are less about clever wording and more about context engineering: telling the tool what the surrounding code looks like, what patterns your team follows, and what the definition of done is. Being specific about constraints and edge cases gets you far closer to usable output than a vague request. Prompting and context engineering for code is covered as its own unit here.

Can you trust code written by AI, and how do you keep it safe?

AI-generated code should be treated like any contribution from a fast but junior teammate: read it, test it, and never merge it blind. Unit tests, quality checks, and review habits catch the hallucinations and subtle mistakes that AI tends to introduce. Testing, quality, and safe AI code is a dedicated part of this course.

How can a whole development team adopt AI tools instead of just one person?

Scaling AI across a team means sharing prompts, context files, and conventions so everyone gets consistent results, rather than each developer reinventing their own approach. It also involves shifting the team's mindset toward treating AI as a normal part of the workflow. This course covers both the AI-native mindset and the practical steps for rolling these tools out across a Flutter team.

What is an AI-native approach to software development?

Being AI-native means designing your everyday workflow around AI assistance from the start, rather than bolting it on occasionally. It changes how you plan tasks, structure your codebase for context, and divide work between yourself and the tools. The course opens with this mindset and builds toward applying it end to end in a capstone project.

What are quick early wins when starting with AI development tools?

The fastest wins usually come from small, well-scoped tasks: generating boilerplate, writing tests, refactoring a single file, or explaining unfamiliar code. Getting your environment set up correctly first means these wins actually stick instead of fighting configuration. This course pairs environment setup with a set of first wins to build momentum early.