LEGAL RESEARCHER AI ASSISTANT

Abstract

Digital legal research in Indonesia requires faster, more accurate systems to support practitioners in navigating complex regulatory documents. Identifying relevant statutes and drafting or reviewing legal documents remains time-consuming and error-prone due to scattered laws and inconsistent formats. A multi-agent large language model (LLM) system was developed using GPT‐4.1 and Gemini‐2.5‐pro‐flash within a retrieval-augmented generation (RAG) framework. LangChain and meta‐prompting help structure agent workflows. The system includes three modules: Regulation Retrieval (for finding relevant articles and Pasal to a certain case or issue), Document Drafting (for generating structured contracts), and Legal Review (for highlighting inconsistencies). Law student reviewers confirmed that the Retrieval module consistently cites accurate Pasal and returns relevant statutes. Drafted documents meet basic structural and linguistic standards. This study demonstrates how multi-agent LLM architectures, combined with prompt engineering and RAG, can efficiently support Indonesian legal workflows by automating regulation search, document drafting, and review. These methods can significantly reduce time and effort in legal practice.