Abstract
This paper presents a pioneering exploration into prompt engineering, a critical aspect of interactions with Large Language Models (LLMs) in engineering contexts. We delve into the nuanced strategies and methodologies of prompt engineering, demonstrating its profound impact on the performance and reliability of LLMs like GPT - 4. We explored a range of techniques, from simple, clear instructions to more complex few - shot learning prompts and evaluated their effectiveness in various scenarios. The findings highlight the significant potential of prompt engineering in enhancing the precision, efficiency, and applicability of LLMs in engineering, setting a foundation for future advancements in human - AI collaboration.
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