Prompt engineering is a crucial skill for optimizing the performance, reliability, and safety of large language models (LLMs) such as GPT-4o, Claude 4, and Gemini 1.5 Pro, which, by 2025, have advanced significantly. Effective prompt engineering involves more than clever wording; it requires a deep understanding of formatting techniques, reasoning scaffolds, role assignments, and potential adversarial exploits to consistently improve model outputs. The practice not only enhances usability but also mitigates security risks by preventing adversarial prompt injections that could bypass LLM guardrails. With models responding differently to various formatting patterns, prompt engineering becomes a critical tool for aligning model behavior with human intent, controlling tone and structure, and ensuring safety. Beyond technical users, prompt engineering is accessible to non-coders like product managers and UX writers, making it a valuable skill across industries. As AI systems become more integrated into products, the ability to craft effective prompts will be as essential as writing clean code, emphasizing the importance of a structured approach to prompt design for achieving reliable and trustworthy AI interactions.