This article deeply explores the evolutionary path of Prompt Engineering in LLM application development. The author points out that prompts are not necessarily perfect on the first attempt but require continuous iteration based on real-world data and feedback. Using a 'YouTube Summary' product as a case study, the article categorizes Prompt Engineering into six stages: Hardcoded Prototype Phase, Templated CLI Phase, Golden Case Regression Evaluation Phase, Categorized Multi-template Architecture Phase, Request Specification (Spec) Definition Phase, and finally, Automated Prompt/Parameter Optimization Phase. Each stage addresses specific problems and provides concrete engineering actions and code examples, aiming to help engineers evolve LLM applications from 'functional prototypes' to maintainable, evaluable, and evolvable product systems. The article emphasizes the importance of measurable, iterative, and verifiable engineering methods in managing highly sensitive LLM systems and anticipates the possibility of introducing technologies like DSPy for semi-automated optimization.