论文：用 Golden Data Model 检索时上下文替代提示词规则，制药文案合规生成提效
From Prompt Rules to Golden Data Model: Production-Scale Generation of Personalized, Compliant Pharmaceutical Claims
一家药企把提示词规则改成检索时查结构化数据库，合规文案准确率涨了六七成，API 费用还省了近三成，做法有详细工程路径。
arXiv 论文报告制药行业文案生成系统 Composer 的生产案例。该系统基于覆盖 4 个产品、共 1,867 条制药 claims 的 Golden Data Model（GDM），把合规与个性化信号从提示词规则迁移到检索时的结构化上下文。在 6 个产品-受众细分上，意图适应症匹配提升 29%，目标受众匹配提升 67%，6C 质量分提升 86%，claim 复用提升 34%。部署期间的生产日志显示每周 API 成本下降 29.5%。
From Prompt Rules to Golden Data Model: Production-Scale Generation of Personalized, Compliant Pharmaceutical Claims
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.