Prompts that generate cleaner code are often highly specific and detailed, clearly outlining desired outcomes and constraints. They should incorporate established coding standards (e.g., PEP 8 for Python, Airbnb style guide for JavaScript) and architectural principles like DRY, SOLID, or testability. Providing context about the existing codebase, project goals, and target audience helps the AI understand the environment and tailor its output appropriately. Explicitly asking for readability, maintainability, and efficiency in the generated code also guides the model towards higher quality. Furthermore, requesting specific code patterns or data structures, alongside examples of preferred style, significantly improves clarity. Iterative refinement, where an initial prompt is followed by requests for refactoring or optimization based on specific criteria, is also highly effective. More details: https://cies.xrea.jp/jump/?https://infok.com.ua/