Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

Explores best practices for human-AI collaboration in software development using vibe coding tools. Key risks identified include garbage-in-garbage-out prompting, poor prompt quality burning through model limits, and AI tendency to over-engineer solutions. Using a RAG system over news articles as a practical example, the author demonstrates a workflow: define clear requirements with test queries, generate architecture before code, validate and stress-test the design with edge cases, have the AI self-critique, and push back on unnecessary complexity. The central principle is a human-in-the-loop cycle where AI accelerates but humans remain the final arbiter on trade-offs, maintainability, and production readiness.

Vibe Coding with AI: Best Practices for Human-AI Collaboration in Software Development

<p>Feels like the article is outdated, that how I thought and worked a year ago, but now half of the things (if not more) are not valid anymore, you don’t need to write detailed prompt or ask create architectural plan first, all AI agents have plan mode and do it by default</p>
