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@article{202470,
        author = {Dr. Nazirkar S. B. and Barde Sakshi Umesh and Lonkar Monika Dattatray and Bhapkar Sakshi Yuvraj and Dr. Shah Saloni Niranjan},
        title = {RAG Lawyer:A Retrieval Augmented Legal Advisor Using Generative AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {7406-7409},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202470},
        abstract = {RAG-Lawyer integrates Retrieval-Aug-mented Generation (RAG) with domain-specific legal knowledge bases to create a reliable and explainable AI legal advisor. The system retrieves the most relevant le-gal documents—such as statutes, case judgments, and regulatory texts—before generating responses grounded in verifiable sources. By combining a re-trieval module with a fine-tuned generative model, RAG-Lawyer enhances factual accuracy, transparency, and interpretability in legal text generation. Legal judg-ment prediction (LJP) presents a many challenge in ar-tificial intelligence (AI) concept demanding intricate comprehension of legal documents and files law cases nuanced interpretation of statutes, and complex reason-ing over multifaceted case elements prediction Our ap-proach encompasses four key stages:
1) legal knowledge of laws , where we pre-train data in LLM on a vast corpus of legal literature using contras-tive learning models.
2) The Case-law retrieval, employing a graph neural network to analyse relevant concept of statutes of the law cases.
3) Multi-Process reasoning, utilizing a transformer-based architecture with a hierarchical attention mecha-nism to navigate complex legal arguments; and
4) Related Judgment synthesis and where we employ a generative adversarial network to produce known and legally cases Laws.
Despite its potential, challenges remain, including en-suring data reliability, addressing nuanced legal inter-pretations, and maintaining privacy and security of sen-sitive legal information. A RAG legal advisor acts as a powerful augmentation tool for human lawyers, streamlining routine tasks while preserving profes-sional oversight and responsibility. Our concept not only advances the area of relative legal reasoning but also get the transparent and clear system that could serve as a valuable tool for legal professionals. By the gap between AI and legal laws related expertise, Legal Reasoner prove the way for more accurate, consistent, and fair legal decision making processes of laws.},
        keywords = {Retrieval-Augmented Generation (RAG), Legal AI, Information Retrieval, Natural Language Generation, Legal Document Analysis, AI-Powered Legal Advisory, Knowledge-Based Sys-tems, Legal Question Answering, Explainable AI, Legal Informatics, Document Retrieval, AI-Assisted Legal Drafting.Transformer Models, Dense Retrieval, Legal Research Automation},
        month = {May},
        }