coding-guidelines

Avoid common LLM coding mistakes during refactoring by enforcing behavioral guidelines that mandate pre-implementation thinking, surgical code changes, and goal-driven execution, ensuring focused edits without unintended side effects.

Best practices for writing clean code with an AI assistant involve explicitly stating assumptions, surfacing tradeoffs, and guiding the AI toward minimal, non-speculative implementations that directly address requirements.

Implement a new feature without unnecessary complexity by defining clear success criteria, focusing on simplicity, and making surgical changes to the existing codebase to maintain efficiency and reduce error-prone code.

You need to apply coding guidelines for bug fixes when modifying or reviewing code to ensure focused edits, prevent unintended side effects, and maintain goal-driven execution throughout the debugging process.

Pre-implementation thinking helps with feature development by encouraging explicit assumption stating and surfacing tradeoffs, which ensures the code is maintainable and directly addresses the requirements.