Since OpenAI released its GPT-3.5 (Generative Pre-trained Transformer) in late 2022, there has been a surge in utilizing large language models (LLMs) in almost every aspect of our lives, enabling improvements in data analysis, automated content creation, and knowledge management. While techniques like fine-tuning of GPTs require coding expertise, prompt engineering has emerged as a useful skill to guide the output of pre-trained models, enabling a broader access to LLMs without the need of coding expertise. Prompt engineering is often achieved by providing input in the form of a question or specific instructions to generate targeted dataset for natural language inference), DROP (a reading comprehension benchmark requiring discrete reasoning), and Cosmos QA (which focuses on reading comprehension with an emphasis on commonsense reasoning), while offering cost-efficiency and robustness in prompt design and evaluation. Similarly, Bonino and coworkers fine-tuned LLMs for Socratic interactions, demonstrating how this approach guides students toward self-discovery and critical thinking, thereby enhancing the educational utility of models such as GPT-4o. Together, these studies illustrate the versatility and value of SM in advancing LLM performance, spanning applications in education, decision-making, and technical problem-solving.