Enhancing Domain-Specific Performance of Large Language Models through Adaptive Prompt Engineering
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Abstract
Large Language Models (LLMs) have demonstrated impressive general-purpose capabilities; however, achieving high performance in specialized domains continues to be a significant challenge. This paper explores the role of adaptive prompt engineering in optimizing LLMs for domain-specific tasks. By tailoring prompt structures, incorporating contextual metadata, and leveraging iterative refinement techniques, LLMs can generate more accurate, relevant, and context-aware outputs.The paper presents a taxonomy of prompt engineering strategies and evaluates their effectiveness across multiple domains, including law, medicine, and finance. It further examines how instruction-tuned models and structured prompting techniques help bridge gaps in model generalization, enabling higher levels of automation, cross-disciplinary reasoning, and effective human–AI collaboration. Additionally, the study discusses current limitations and future research directions, emphasizing the importance of systematic prompt evaluation, continuous optimization, and human-in-the-loop collaboration to improve the reliability and performance of domain-specific Large Language Model applications.
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