Expanding Horizons in Prompt Engineering: Techniques, Frameworks, and Challenges

Prompt engineering is a critical method for guiding large language models (LLMs) to perform diverse tasks effectively. This paper reviews prompt engineering techniques, including example-driven approaches, logic steps, and modular frameworks, highlighting their adaptability and limitations. It emphasizes the need for robust evaluation metrics, addressing gaps in reasoning quality and task adherence. By proposing novel evaluation approaches such as token-level entropy, this study advances understanding of prompt effectiveness, paving the way for more reliable, ethical, and domain-specific LLM applications.