
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 7 Issue 4
July-August 2025
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RECAP-Reinforced, Explainable, and Cost-Aware Prompting: A Framework for Understandable Prompt Optimization Based on Cognitive Science
Author(s) | Mr. Raghupathi Appala, Ms. Maanasa Kotte, Ms. Pallavi Tejaswi Kakaraparthi |
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Country | India |
Abstract | Maximizing the effectiveness of Large Language Models (LLMs) requires prompt optimization, but existing approaches frequently have limited interpretability, high computational cost, and narrow generalization. We introduce RECAP, a modular, cognitively based framework for explainable and automated prompt engineering. Neurofeedback-based self-scoring, evolutionary prompt graph search, contrastive-symbolic rule induction, Pareto-based cost-accuracy optimization, an interactive debugging interface, and a shared inter-module memory layer are the six main innovations it presents. Without the need for model fine-tuning, RECAP lowers token, latency, and memory overhead while increasing prompt quality and LLM accuracy. It offers a scalable and interpretable substitute for conventional tuning pipelines and can be used in a variety of fields, including conversational AI and search. |
Keywords | Neuro-symbolic Optimization, Self-Scoring, Contrastive Rule Induction, Evolutionary Prompt Graph, Token Efficiency, Cost-Aware Prompting, Cognitive Failure Analysis, Human-in-the-Loop AI, Pareto Optimization. |
Field | Computer > Artificial Intelligence / Simulation / Virtual Reality |
Published In | Volume 7, Issue 4, July-August 2025 |
Published On | 2025-08-03 |
DOI | https://doi.org/10.36948/ijfmr.2025.v07i04.52779 |
Short DOI | https://doi.org/g9vznc |
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E-ISSN 2582-2160

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