RAG 数据集 vs 微调数据集准备：不同管道，相同源数据
RAG 需要分块的、检索优化的文本。微调需要输入/输出对。两者都从相同的原始文档开始。以下是如何从单一来源运行并行准备管道。
大多数企业 AI 团队最终构建两条独立的数据管道。两条管道都从相同的原始文档开始。它们共享相同的摄入和清洗阶段。然后分流。
独立运行两条管道意味着重复 60-70% 的工作。更好的方法是共享公共阶段并分支为两个导出路径的统一管道。
为什么通常需要两者
RAG 处理长尾：频繁更新的文档、稀有查询的小众知识。微调处理核心：构成 80% 查询的 200-500 个问题。
共享阶段：摄入、清洗、实体提取
前三个阶段无论输出格式如何都是相同的。运行一次。
分流点
分支 A：RAG 管道
分块、元数据标注、向量嵌入、索引。
分支 B：微调管道
问题生成、答案提取、格式转换、验证。
质量要求不同
RAG 容忍一定噪音（2-5% 噪音率可接受）。微调要求每个示例高准确率（目标低于 0.5% 噪音率）。
统一管道的时间和成本节省
统一管道节省约 33% 的计算时间和 25% 的专家时间。更重要的是，它消除了一致性问题——两个输出保证来自相同的清洁源数据。
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