RAG 问题解析：将用户输入拆分为检索与生成简报
英文摘要
A Towards Data Science tutorial by Angela Shi argues that user questions in RAG systems deserve the same careful parsing as documents. The technique splits a raw question into a 'retrieval brief' that specifies what to find and a 'generation brief' that defines how to use the retrieved context. This pre-processing step decouples searching from answer formation, improving both retrieval precision and answer quality. The approach is illustrated for enterprise document intelligence use cases.
中文摘要
Towards Data Science 上 Angela Shi 的教程提出，RAG 系统中的用户问题应受到与文档同等的解析。该方法将原始问题拆分为「检索简报」（指定查找内容）和「生成简报」（定义如何使用检索到的上下文）。这种预处理将搜索与答案生成解耦，从而提升检索精度和答案质量。该方法以企业文档智能场景为例进行了说明。
关键要点
User questions should be parsed to improve RAG pipeline performance
用户问题应予解析以提升 RAG 管道性能
Splits question into retrieval brief and generation brief before execution
执行前将问题拆分为检索简报和生成简报
Decouples retrieval strategy from answer generation logic
将检索策略与答案生成逻辑解耦
Aimed at enterprise document intelligence applications
面向企业文档智能应用