An Iterative Self-Reflective Prompt Engineering Framework for Large Language Models

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Large Language Models (LLMs) demonstrate remarkable capabilities across diverse natural language processingtasks; however, their performance is highly sensitive to prompt design. Static prompt engineering approaches often fail toensure consistency, reliability, and reasoning depth across varied tasks and domains. This paper proposes an Iterative Self-Reflective Prompt Engineering Framework that enhances LLM performance through structured self-evaluation and promptrefinement. The framework introduces a feedback-driven loop in which generated responses are analyzed, critiqued, and usedto iteratively optimize the original prompt. By integrating self-reflection mechanisms, the proposed approach improvesaccuracy, coherence, and reasoning quality while reducing hallucinations. Experimental analysis demonstrates that iterativeself-reflection significantly outperforms static prompting across multiple evaluation metrics. The framework provides asystematic and scalable methodology for reliable and trustworthy deployment of LLMs in high-stakes applications.