RECAP-Reinforced, Explainable, and Cost-Aware Prompting: A Framework for Understandable Prompt Optimization Based on Cognitive Science
Raghupathi Appala, Maanasa Kotte, Pallavi Tejaswi Kakaraparthi · International Journal For Multidisciplinary Research · 2025
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.