A Retrieval-Augmented Generation Framework for Legal AI Assistance in Indian Domain

Abstract

Legal research is a complex and time-consuming process that requires practitioners to analyze numerous statutes, case laws, judicial precedents, and legal interpretations from multiple sources. Although Large Language Models (LLMs) have enabled conversational legal assistants, they often generate hallucinated responses, which can be misleading and unreliable in legal applications. This paper presents a Retrieval-Augmented Generation (RAG)-based Legal AI Assistant for the Indian legal domain. The proposed framework combines semantic retrieval, document chunking, sentence embeddings, FAISS-based vector indexing, and LLM-based reasoning to generate accurate, citation-backed legal responses. Legal documents are preprocessed, divided into chunks, embedded into vector representations, and indexed using FAISS to enable efficient semantic retrieval. The retrieved legal context is then provided to the LLM, ensuring that responses are grounded in verified legal documents rather than relying solely on the model’s internal knowledge. The framework also provides source citations to improve transparency, explainability, and user trust. The proposed system is designed to assist lawyers, law students, legal professionals, and the general public in accessing reliable legal information. Experimental results demonstrate improved factual consistency, reduced hallucination, and enhanced response reliability compared with conventional LLM-based legal assistants.