$ show Qais-Hweidi/ai-assisted-development-guide
This is a collection of 12 lessons on effective AI-assisted software development, derived from real-world experience with tools like Cursor, Windsurf, Cline, and Claude. It is not a software application or data research project—it is pure documentation. The lessons cover project structure, planning, problem decomposition, test writing, commit discipline, and prompt engineering. The content is clear, concise, and immediately applicable. No code or data pipelines are present; the value lies in the reusable practices and mental models it teaches.
The LLM identified 2 files worth highlighting for reuse:
Advocates defining a clear folder structure before writing any code, ensuring both developer and AI share a logical organization. This pattern is universally applicable and reduces confusion as projects scale.
Emphasizes writing tests even for small AI-assisted projects to catch regressions and validate AI-generated changes. The core pattern—test before accepting—is a safe, repeatable practice for any codebase.