This chapter situates prompt engineering as a transformative shift in human–computer interaction, where natural language replaces code as the primary interface for directing intelligent systems. It defines prompt engineering as a structured design practice that shapes model behavior through explicit articulation of task, tone, role, and constraints. The discussion emphasizes that prompting is not limited to technical experts but constitutes a shared literacy across professions, including healthcare, education, law, and policy. It examines how non-coders increasingly depend on AI outputs shaped by unseen prompts, highlighting risks related to misinterpretation, bias, and loss of nuance. The chapter identifies three enabling forces behind this shift: large-scale data, advanced computational infrastructure, and transformer-based architectures. It traces the historical evolution of AI from rule-based systems to deep learning, demonstrating how control has moved from programming logic to user language. The role of transformers is analyzed as the key innovation enabling context-aware, prompt-driven interaction. Prompting is positioned as both a technical and communicative discipline, requiring clarity, contextual awareness, and ethical consideration. The chapter underscores prompting as a form of professional and civic responsibility that shapes interpretation, trust, and decision-making. Ultimately, it establishes prompt engineering as an essential, interdisciplinary literacy for guiding AI systems effectively and responsibly.