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This paper introduces a Retrieval-Augmented Generation (RAG) system tailored for navigating the complexities of global AI regulations across 68 jurisdictions. The system employs type-specific chunking, conditional retrieval routing based on entity detection, and priority-based re-ranking to handle the heterogeneity of legal documents. Evaluations on 50 queries demonstrate strong performance, achieving 0.87 faithfulness and 0.84 relevancy, showcasing the effectiveness of domain-specific retrieval strategies in this context.
Forget searching through endless legal documents – a new RAG system achieves 87% faithfulness and 84% relevancy in answering complex, multi-jurisdictional AI regulation questions.
Navigating AI regulation across jurisdictions is increasingly difficult for policymakers, legal professionals, and researchers. To address this, we present a multi-jurisdictional Retrieval-Augmented Generation system for global AI regulation. Our corpus includes 242 documents across 68 jurisdictions, ranging from formal legislation like the EU AI Act to unstructured policy documents such as national AI strategies. The system makes three technical contributions: type-specific chunking that preserve legal structure across heterogenous documents; conditional retrieval routing with entity detection and metadata for legal citations; and priority-based re-ranking to boost enacted legislation over policy and secondary sources. Evaluation of 50 queries reveals strong performance across both single-entity and multi-jurisdictional questions, achieving 0.87 average faithfulness and 0.84 average answer relevancy. Single-entity queries achieve 0.86 average faithfulness and 0.92 average answer relevancy, while multi-jurisdictional comparison queries achieve 0.88 average faithfulness and 0.75 average answer relevancy. These findings highlight the effectiveness of domain-specific retrieval strategies for navigating complex, heterogenous regulatory corpora.
