📄 Abstract
Large Language Models (LLMs) demonstrate remarkable capabilities across diverse natural language processing tasks; however, their performance is highly sensitive to prompt design. Static prompt engineering approaches often fail to ensure consistency, reliability, and reasoning depth across varied tasks and domains. This paper proposes an Iterative SelfReflective Prompt Engineering Framework that enhances LLM performance through structured self-evaluation and prompt refinement. The framework introduces a feedback-driven loop in which generated responses are analyzed, critiqued, and used to iteratively optimize the original prompt. By integrating self-reflection mechanisms, the proposed approach improves accuracy, coherence, and reasoning quality while reducing hallucinations. Experimental analysis demonstrates that iterative self-reflection significantly outperforms static prompting across multiple evaluation metrics. The framework provides a systematic and scalable methodology for reliable and trustworthy deployment of LLMs in high-stakes applications.
🏷️ Keywords
📚 How to Cite:
Deepika Bansal , AN ITERATIVE SELF-REFLECTIVE PROMPT ENGINEERING FRAMEWORK FOR LARGE LANGUAGE MODELS , Volume 12 , Issue 7, July 2026, EPRA International Journal of Multidisciplinary Research (IJMR) , Pages: 724 - 729 ,