Regulatory intelligence depends on the accurate retrieval, interpretation, and synthesis of complex pharmaceutical guidance, product documentation, and evolving compliance expectations. The growing volume and specificity of regulatory text have made manual synthesis increasingly difficult for development, quality, and submission teams. Retrieval-augmented generation emerged as a response to the factual limitations of large language models by linking generated answers to external knowledge sources. This shift was especially relevant to pharmaceutical regulation, where unsupported synthesis can mislead decision-making. Modern RAG systems combine dense retrieval, passage ranking, prompt control, and verification layers to produce answers that are both fluent and traceable. They are now being explored for guideline interpretation, submission preparation, compliance checking, and other regulatory intelligence workflows. Despite these advances, RAG systems remain vulnerable to retrieval errors, ambiguous source language, incomplete document corpora, and residual hallucination. Trust depends not only on answer quality but also on source transparency, auditability, and expert review. Future regulatory RAG systems are likely to combine structured knowledge graphs, real-time guidance monitoring, validated evaluation frameworks, and privacy-preserving deployment. These capabilities may support increasingly autonomous but still accountable regulatory intelligence tools. This narrative review traces the evolution of RAG from a technical solution for hallucination mitigation to a credible framework for pharmaceutical regulatory intelligence. It emphasizes grounding, traceability, evaluation, and governance as the foundations of trustworthy regulatory AI.