What’s the best way to structure AI-assisted code generation workflows for real-world projects?

[mudasir_qayyum]

I’ve been experimenting with AI coding assistants (like GitHub Copilot and ChatGPT) to speed up development. While they’re great at generating snippets, boilerplate, or even suggesting algorithms, I’m still trying to figure out the most effective way to use them in larger projects without creating technical debt.

A few challenges I’ve run into:

• Code Quality & Readability: Sometimes AI generates working code, but it’s not clean or maintainable.
• Project Structure: For multi-file projects, it’s tricky to guide AI to follow consistent patterns and naming conventions.
• Debugging: AI suggestions are often helpful for bug fixing, but I still need to carefully review everything.
• Security: How do you ensure that AI-generated code doesn’t introduce hidden vulnerabilities?

I’d like to hear from others who are integrating AI into their coding workflows:

• Do you treat AI as a “pair programmer” or more as a quick-reference tool?
• How do you maintain long-term code quality when using AI-generated snippets?
• Any strategies or tools you’ve found useful for managing AI-assisted contributions in collaborative projects?

By the way, I came across an example of AI-built coding tools, like a simple procalculadora.com, which shows how even small utilities can be generated quickly. It made me wonder how we can apply similar approaches effectively in larger, production-level projects.