AI RESEARCH
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
arXiv CS.LG
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ArXi:2603.24580v1 Announce Type: cross Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks. We study the application of RAG to AI governance and policy analysis using the AI Governance and Regulatory Archive (AGORA) corpus, a curated collection of 947 AI policy documents.