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From Prompt Rules to Golden Data Model: Production-Scale Generation of Personalized, Compliant Pharmaceutical Claims

Suyash Mishra, Qiang Li, Subhabrata Adak, Upender Phogat, Srikanth Patil, Satyanarayan Pati · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31519 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

大型域数据模型(LLMs)数据越来越多地用于在制定的行业中生成域特定的商业和医疗内容(例如声明、声明和促销语句)。然而,确保其监管合规性,事实准确性和受众特定的个性化仍然是一个重大挑战。我们报告了一项生产案例研究,询问是否将声明合规性和个性化信号从提示级规则转移到检索时间结构化工作环境中,使用Composer,一个基于1867个产品的黄金数据模型(GDM)构建的部署内部工具。GDM是否将声明合规性和个性化信号整合到检索时间框架中,改善了药物声明的生成,并使用了六个维度质量分数(6Cross)。在六个产品受众区间,背景线

Machine translation (Meta M2M100); the English original follows.

Abstract

Large language models (LLMs) are increasingly used to generate domain-specific commercial & medical content (e.g., claims, statements, and promotional phrases) in regulated industries such as pharmaceuticals. However, ensuring its regulatory compliance, factual accuracy, and audience-specific personalization remains a major challenge. We report a production case study asking whether moving compliance and personalization signal from prompt-level rules into retrieval time structured context improves pharmaceutical claims generation, using Composer, a deployed internal tool built on a Golden Data Model (GDM) of 1,867 pharmaceutical claims spanning four products. The GDM integrates claim metadata, references, audience labels, material type, usage history, Match Text Variant (MTV) links, and a six dimension quality score (6C). Across six product-audience segments, the context pipeline improves intended indication (+29%), intended content type (+62%) and intended target audience (+67%), 6C quality score (+86%), and claim reuse (+34%). Production log analysis over the deployment period shows a 29.5% reduction in weekly API cost. We report this as descriptive deployment evidence. This work is a systems and data architecture contribution: it shows that in a regulated, high-stakes domain, restructuring claim data for retrieval-time filtering is a more scalable lever than expanding prompt-level rules, and documents the engineering path data cleaning, graph linking, and quality scoring.

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Topics: Personalization, Computer science, Software deployment, Context (archaeology), Pipeline (software), Quality (philosophy)

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