Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations

We study question answering in the domain of radio regulations, a legally sensitive and high-stakes area. We propose a telecom-specific Retrieval-Augmented Generation (RAG) pipeline and introduce, to our knowledge, the first multiple-choice evaluation set for this domain, constructed from authoritative sources using automated filtering and human validation. To assess retrieval quality, we define a domain-specific retrieval metric, under which our retriever achieves approximately 97\% accuracy. Beyond retrieval, our approach consistently improves generation accuracy across all tested models. In particular, while naïvely inserting documents without structured retrieval yields only marginal gains for GPT-4o (less than 1\%), applying our pipeline results in nearly a 12\% relative improvement. These findings demonstrate that carefully targeted grounding provides a simple yet strong baseline and an effective domain-specific solution for regulatory question answering.