Implementation Plan for the Retrieval-Augmented Generation System in the GRANT-AI Trial

Abstract

The GRANT-AI pilot trial investigates the use of artificial intelligence to enhance NIH grant proposal development by integrating expert review with advanced AI feedback mechanisms. This implementation plan focuses on the deployment of a Retrieval-Augmented Generation (RAG) system, which leverages GPT-4o and a curated knowledge base of funded NIH proposals, reviewer critiques, and methodological guidelines. Unlike traditional large language model (LLM) feedback systems, the RAG model performs semantic retrieval to surface domain-specific examples and generates grounded, contextual feedback aligned with NIH review criteria. Expert reviewers initiate feedback by uploading proposal drafts, triggering the RAG engine to retrieve relevant material and guide GPT-4o in composing structured suggestions. The platform incorporates behavioral engagement tools such as progress visualizations and motivational nudges to improve adherence and productivity. Security is ensured through zero data retention, role-based access, and encrypted infrastructure. The system's performance will be evaluated across multiple domains including feedback accuracy, alignment with expert commentary, user satisfaction, and retrieval relevance. By combining AI-driven insights, expert human guidance, and behaviorally informed scaffolding, GRANT-AI seeks to transform the grant writing process into a more efficient, equitable, and evidence-based endeavor.

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