RAG-Based Legal Document Assistant for Automated Legal Document Management and Advice

Legal professionals face a significant challenge in managing vast volumes of legal documents, performing research, and drafting case-specific advice. This paper presents a Retrieval-Augmented Generation (RAG)-based legal document assistant that leverages state-of-the-art Natural Language Processing (NLP) and Machine Learning (ML) techniques to automate document summarization, drafting, and legal advice generation. The assistant aims to streamline legal workflows, enhance productivity, and improve the accuracy of legal document preparation. The system integrates a robust legal research module, which fetches relevant case law and statutes, and an AI-powered chatbot to deliver instant legal advice. Key Words: RAG, Legal Document Summarization, NLP, Legal Assistant, AI-powered Legal Advice, Legal Research