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
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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