This chapter operationalizes the CASTROFF framework by demonstrating how prompts can be diagnosed, repaired, and redesigned in real-world contexts. It reframes prompt failure as a design issue rather than a model limitation, emphasizing the role of structured analysis in improving AI outputs. Using CASTROFF’s eight dimensions, the chapter introduces a systematic approach to identifying weaknesses such as ambiguity, tone mismatch, lack of structure, and misaligned function. It presents applied repair scenarios that illustrate how targeted revisions enhance clarity, usability, and ethical alignment. Comparative analyses of before-and-after outputs highlight how prompt improvements translate into more relevant and audience-appropriate results. The chapter further extends prompt engineering into collaborative practice through team audits and peer feedback, establishing shared standards for evaluation and refinement. Detailed case studies demonstrate full prompt redesign in professional domains, emphasizing measurable improvements in efficiency, structure, and reliability. The integration of audit tools and structured workflows supports scalability and institutional adoption. Throughout, prompting is framed as an iterative, evidence-based design process grounded in communicative intent. Ultimately, the chapter positions CASTROFF as a practical methodology for achieving precise, accountable, and context-sensitive human–AI interaction.