Pharmaceutical labels serve as the authoritative source of approved drug safety information, guiding prescribing, dispensing, monitoring, and patient counseling; however, safety statements are often scattered across warnings, adverse reactions, contraindications, and interaction sections, creating potential internal inconsistencies. Manual cross-checking of all safety-relevant sections is slow, repetitive, and subject to reviewer variability, while keyword searches alone cannot reliably detect semantic equivalence, missing safety concepts, or contradictions expressed with different clinical terminology. To address these challenges, this article proposes a conceptual retrieval-augmented language model system that ingests the full product label, indexes safety-relevant statements, and answers consistency queries by retrieving and comparing authoritative passages. The system integrates a structured label parser, section-aware chunking, semantic indexing, a vector database, retrieval-augmented answer generation, contradiction detection, and a human review interface, with each generated consistency judgment linked to the supporting label passages. Such an approach could accelerate label review, enhance traceability, and help reviewers identify discrepancies that might otherwise remain hidden in lengthy regulatory documents, provided it is carefully grounded, expert-validated, deployed in a privacy-preserving manner, and governed appropriately. By combining semantic retrieval with human adjudication, retrieval-augmented label consistency checking has the potential to become a practical tool for regulatory affairs and pharmacovigilance teams, supporting continuous surveillance of safety information integrity.