Prompt engineering is emerging as a critical skill for developers as AI becomes increasingly integrated into software development, functioning as an ideal pair-programming partner. Understanding how to craft effective prompts is vital for leveraging AI to generate high-quality code, akin to software engineering practices like DRY and SOLID. This involves treating prompts as modular, testable components that can be refined and integrated into larger systems. Structured prompting, including techniques like few-shot learning, chain-of-thought reasoning, and self-consistency checks, enhances the quality of AI-generated outputs by providing clear examples and logical progression. Developers are encouraged to use skeleton prompts and define output schemas to ensure adherence to coding standards and system compatibility. Furthermore, prompt linting and configuration parameters, such as temperature settings, allow developers to validate and tune prompts for optimal performance. By adopting these methodologies, developers can build, test, and maintain prompts with the same rigor as traditional code, resulting in reliable AI interactions and reducing the unpredictability often associated with AI-generated content.