As AI agents move from demos to production, prompt engineering has become a critical discipline. This post outlines a methodology that starts with clear role definition, then encapsulates reusable skills, and finally enforces output quality through structured evaluation. The approach is framework-agnostic and can be applied to Claude, GPT, or open-source models. Key takeaways include the importance of separating static role context from dynamic skill logic, and using iterative testing to refine prompts. For teams building customer-facing agents, this methodology offers a practical path to more consistent and reliable outputs.
A structured prompt engineering methodology for AI agents, focusing on role definition, skill encapsulation, and output quality control.