Development of an Agentic RAGPowered Research Assistant for Academia

This system is designed to solve the real-time challenge of finding accurate, structured, and citation-based information without the delays and inconsistencies of manual research. In most real-world scenarios, users—whether students, researchers, or professionals—struggle with information overload, unreliable sources, and the time required to convert raw data into meaningful insights. To address this, the platform integrates an Agentic Retrieval-Augmented Generation (RAG) pipeline accelerated by Groq for high-speed processing and precise retrieval. Users start by registering, logging in, and managing their profiles. From the dashboard, they simply input a topic, after which the system autonomously refines the query and retrieves high-quality content from relevant sources. The agentic backend processes, filters, and summarizes the information to generate a coherent review enriched with accurate citations. This automated pipeline eliminates the need for manual searching, cross-verification, and summarizing, thereby significantly reducing effort and improving reliability. The final output is displayed clearly, with options to export the content for academic or professional use. By combining automated retrieval, intelligent summarization, and user-friendly interaction, the system provides an efficient real-time solution for individuals who need fast, trustworthy, and well-organized reviews.

Keywords: Agentic RAG, Groq Inference, Real-time Retrieval, Query Refinement, Automated Summarization, Citation Generation.