Prompt engineering has become a core skill in working with LLMs, yet writing effective prompts is tedious, inconsistent, and often requires multiple attempts. Most prompt development today is done manually, relying on trial and error, which doesn’t scale well, especially in production environments where consistency is crucial. This talk presents a practical approach to optimizing prompts by treating them as evolving tools. The process includes refining prompts based on feedback and ensuring the outputs meet structure and quality requirements, with the help of Pydantic to validate and enforce output standards. This helps create stable, production-ready prompts through continuous and iterative improvement.