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The paper introduces LRBTC, a modular LLM and VLM-driven architecture for quality control in pharmaceutical content, addressing the need for scalable and verifiable validation in regulated domains. LRBTC employs a Student-Teacher dual model architecture combined with a human-in-the-loop workflow and waterfall rule filtering. The approach achieves significant improvements on AIReg-Bench (83.0% F1, 97.5% recall) and CSpelling (26.7% accuracy improvement), demonstrating its effectiveness in reducing missed violations and improving content quality.
A dual LLM architecture slashes missed regulatory violations in pharmaceutical content by 5x, outperforming even Gemini 2.5 Pro.
Large language models (LLMs) are increasingly used to create content in regulated domains such as pharmaceuticals, where outputs must be scientifically accurate and legally compliant. Manual quality control (QC) is slow, error prone, and can become a publication bottleneck. We introduce LRBTC, a modular LLM and vision language model (VLM) driven QC architecture covering Language, Regulatory, Brand, Technical, and Content Structure checks. LRBTC combines a Student-Teacher dual model architecture, human in the loop (HITL) workflow with waterfall rule filtering to enable scalable, verifiable content validation and optimization. On AIReg-Bench, our approach achieves 83.0% F1 and 97.5% recall, reducing missed violations by 5x compared with Gemini 2.5 Pro. On CSpelling, it improves mean accuracy by 26.7%. Error analysis further reveals that while current models are strong at detecting misspellings (92.5 recall), they fail to identify complex medical grammatical (25.0 recall) and punctuation (41.7 recall) errors, highlighting a key area for future work. This work provides a practical, plug and play solution for reliable, transparent quality control of content in high stakes, compliance critical industries. We also provide access to our Demo under MIT Licenses.