This chapter positions prompt debugging as a critical practice for ensuring safety, fairness, and reliability in high-stakes AI applications. It demonstrates how vague or underspecified prompts in domains such as healthcare, law, education, and employment can lead to harmful, biased, or misleading outputs with real-world consequences. The discussion reframes prompt design as a form of human authorship, where omissions in constraints, audience, or tone allow models to default to statistically dominant, and often biased, assumptions. It examines how these hidden defaults compromise ethical alignment and user trust, particularly in sensitive contexts. The chapter introduces systematic debugging techniques, including iterative testing, CASTROFF-based diagnosis, edge-case persona evaluation, structural decomposition, and self-audit prompting. A structured debugging protocol is presented to guide reproducible, evidence-based prompt refinement. Real-world case studies illustrate how prompt revision improves safety, inclusivity, and contextual appropriateness across professional domains. The chapter further addresses risks in multilingual and cross-cultural prompting, emphasizing the need for localization beyond literal translation. It expands the discussion to institutional prompt governance, highlighting the importance of auditing, documentation, and shared standards. Ultimately, the chapter establishes prompt debugging as an essential discipline for building trustworthy, accountable, and ethically aligned AI systems.