Technical and Ethical Perspectives on Building Intelligent Legal Assistants With LLM and RAG Pipelines

Abstract

The rapid advancement of Large Language Models (LLMs) has opened new possibilities for intelligent legal assistants that support research, drafting, case analysis, and decision preparation. This article presents how LLMs combined with Retrieval-Augmented Generation (RAG) can power context aware and reliable legal AI systems. The article outlines technical foundations of such assistants, describing how RAG pipelines integrate legal documents to produce outputs based on reliable sources and reduce hallucinations. Key technical challenges such as constructing legal knowledge bases and managing computational demands are also discussed. Beyond technical design, the article explores the ethical implications of deploying legal chatbots, including bias, unequal access, accountability, and overreliance on automated interpretations. We highlight the requirements for responsible AI in these chatbots, covering explainability, human oversight, and compliance. The article defines a roadmap for developing legal assistants that are accurate, transparent, and aligned with legal and societal norms.

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