Retrieval-Augmented Generation for Pharmaceutical Regulatory Intelligence: A Narrative Review
Abstract
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 source
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