What is the correct way to vibe-code Machine Learning projects?[p]

I'm currently learning Machine Learning through a course, and I want to start building projects alongside it. My main goal right now is simply to build several good ML projects and get familiar with the complete project development process . I want to use AI coding tools such as Cursor, Claude Code, or GitHub Copilot to speed up development, but I'm unsure about the right way to vibe-code an ML project . For example, should I: Give the AI the complete project requirements and let it build the project? First create the architecture/pipeline myself and then let AI implement it? Build the project step-by-step and ask AI to implement each stage? Let AI handle things like data cleaning, EDA, preprocessing, and boilerplate while I focus on the ML decisions? Give AI a detailed specification before starting? Ask AI to review and improve the code after it generates it? Use one long conversation/context for the entire project, or separate prompts for different stages? How should I handle debugging and modifying AI-generated ML code? Basically, what is the best workflow for vibe-coding an ML project from start to finish? I'm not trying to replace learning ML with AI — I'm already studying the concepts separately. I just want to use AI effectively to build projects faster without ending up with a messy or poorly structured project . I'd especially like to hear from people who have built ML projects using Cursor/Claude Code/Copilot: What workflow do you personally follow, and what mistakes should I avoid? Also, please suggest any good communities where I can see how other people are building ML projects and discuss AI-assisted development. Thanks! submitted by /u/TusharKharade_ [link] [留言]