The article compares design to cooking, emphasizing that preparation shapes outcomes. Just as a successful dish requires careful ingredient management, effective AI prompts rely on clarity and structure. The author argues that large language models (LLMs) thrive on straightforward instructions rather than vague niceties. For designers, mastering prompt construction is essential because design demands precision, while AI operates on probabilistic outputs. The author introduces a prompt framework called TC-EBC—Task, Context, Elements, Behavior, Constraints. Each component is crucial for creating effective prompts. For example, a poorly structured prompt for an AI app yields lackluster results, while a TC-EBC formatted prompt delivers clarity and purpose, leading to a better-designed output. The article highlights the importance of removing unnecessary language to improve communication with AI, arguing that a well-crafted prompt acts like a recipe card, delivering concise, actionable guidance. Using real-world examples, the author shows how the TC-EBC structure refines prompts, improving the likelihood of getting desired results. The focus on specificity—defining tasks, context, and constraints—strengthens intent and minimizes ambiguity. This structured approach not only enhances efficiency in generating solutions but also aligns with best practices in prompt engineering across various disciplines.