Prompt engineering serves as the critical interface between human intent and artificial intelligence execution. The efficacy of a large language model’s (LLM) response hinges directly on the clarity, specificity, and strategic design of the input prompt. Many users, regardless of their experience level, frequently encounter common pitfalls that significantly impede optimal AI performance. Identifying and rectifying these pervasive prompt optimization mistakes is paramount for leveraging the full capabilities of
