Engineering Reliable Retrieval-Augmented Generation for Regulatory and Compliance Systems

Regulatory systems in domains such as healthcare, finance, and legal compliance require more than accurate question answering; they demand reliability, traceability, and stability as policies evolve over time. While Retrieval-Augmented Generation (RAG) improves factual grounding compared to standalone large language models, many deployments remain fragile in production due to retrieval mismatches, unsupported generation, and silent behavioral regressions following document updates. This paper presents a reliability-engineering approach to RAG for regulatory systems. We integrate hybrid densesparse retrieval with cross-encoder reranking, faithfulness guardrails that enforce evidence-backed generation, and continuous regression testing triggered by policy changes. Together, these mechanisms treat RAG as an evolving software system rather than a static inference pipeline. Experimental evaluation on a validated regulatory question-answer set shows that hybrid retrieval improves Recall@5 by up to 18% over dense-only baselines, while faithfulness validation reduces unsupported statements by 67%. A regression framework detects policy-induced answer changes in controlled update scenarios, supporting audit-ready operation. The proposed framework provides a practical blueprint for engineering reliable and auditable RAG systems in compliance-heavy enterprise environments.