THE ART AND SCIENCE OF PROMPT ENGINEERING: OPTIMIZING INTERACTIONS WITH LARGE LANGUAGE MODELS
Keywords:
Prompt Engineering, Large Language Models, Chain-of-Thought Implementation, Context Integration, Response Format ControlAbstract
This comprehensive article explores the evolving landscape of prompt engineering in Large Language Models (LLMs), examining the fundamental principles and advanced methodologies that enhance human-AI interaction. The article investigates key components, including context integration, instruction-based architectures, and iterative refinement processes, demonstrating significant improvements in model performance across diverse applications. Through extensive analysis of enterprise implementations, the article reveals the effectiveness of structured approaches in optimizing response quality, reducing computational resources, and improving task completion rates. The article highlight the critical role of systematic prompt construction in achieving superior results across technical domains, while establishing a framework for future developments in AI interaction methodologies.
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