Prompt engineering has emerged as a foundational paradigm in modern artificial intelligence systems, particularly in large language models (LLMs). Unlike traditional programming approaches that rely on formal syntax and logical operators, prompt engineering enables natural language-based interaction between humans and machines. This study explores prompt engineering as an interdisciplinary phenomenon at the intersection of linguistics, cognitive science, and artificial intelligence. The research adopts a qualitative conceptual analysis methodology to investigate how prompt structures influence model behavior, output accuracy, and semantic alignment with human intent. Findings indicate that prompt engineering functions as a cognitive-linguistic interface that translates human intentions into machine-interpretable instructions. Furthermore, iterative prompt refinement significantly enhances output quality and reduces ambiguity. The study concludes that prompt engineering is not merely a technical skill but a new communication paradigm shaping the future of human–AI interaction systems.