  Large Language Models (LLMs) have shown prominent performance in various
downstream tasks and prompt engineering plays a pivotal role in optimizing
LLMs' performance. This paper, not only as an overview of current prompt
engineering methods, but also aims to highlight the limitation of designing
prompts based on an anthropomorphic assumption that expects LLMs to think like
humans. From our review of 36 representative studies, we demonstrate that a
goal-oriented prompt formulation, which guides LLMs to follow established human
logical thinking, significantly improves the performance of LLMs. Furthermore,
We introduce a novel taxonomy that categorizes goal-oriented prompting methods
into five interconnected stages and we demonstrate the broad applicability of
our framework. With four future directions proposed, we hope to further
emphasize the power and potential of goal-oriented prompt engineering in all
fields.
