International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 4 (July-August 2025) Submit your research before last 3 days of August to publish your research paper in the issue of July-August.

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

Share this