Authors : Shatrunjay Kumar Singh

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/mr3nyys5

Scribd : https://tinyurl.com/57ch6dd3

DOI : https://doi.org/10.38124/ijisrt/25nov1316

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The external knowledge-based architecture of Retrieval-Augmented Generation (RAG) systems demonstrates strong potential for legal informatics through their ability to connect Large Language Models (LLMs) to external information. The typical design of RAG systems fails to match the common law system because they focus on semantic matching instead of following the legal principles of *stare decisis* and court organization and authority strength. The paper develops a conceptual framework for Precedent-Aware RAG (PA-RAG) which aims to connect these two systems. The system includes two main components: a precedent-aware retriever that uses jurisdictional authority and temporal recency and citation network centrality to rank cases and a legal-reasoning generator that creates structured outputs which can be verified. The research establishes a complete system design and develops specific evaluation criteria for legal applications to direct future system development. The paper evaluates the moral concerns surrounding these systems before presenting a plan for their deployment and testing process to create dependable AI-based legal research tools.

Keywords : Precedent-Aware RAG, Legal Information Retrieval, Case Law Analysis, Stare Decisis, Knowledge Graphs in Law, Legal Artificial Intelligence, Retrieval-Augmented Generation, Large Language Models, Conceptual Framework, AI for Legal Research.

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