企业 AI 智能体：微调模型 vs RAG 何时使用哪种
企业 AI 智能体该用微调、RAG 还是两者兼用？本指南从 10 个决策维度比较两种方案，解释何时各有胜出，介绍混合模式及数据准备要求。
"该用 RAG 还是微调？「是企业团队构建 AI 智能体时最常问的问题。正确的答案通常是」两者都用，用于不同目的。"
决策框架
| 标准 | RAG | 微调 |
|---|---|---|
| 数据频繁变化 | 最佳 | 差 |
| 输出格式须一致 | 不一致 | 最佳 |
| 需要来源引用 | 内置 | 不可用 |
| 延迟关键 | 增加检索延迟 | 最佳 |
| 领域术语 | 检索但可能误用 | 内化术语 |
| 行为一致性 | 随检索上下文变化 | 一致 |
| 多步智能体工作流 | 每步检索慢 | 快速一致的工具调用 |
混合方案（大多数生产智能体实际使用的）
**微调提供：**领域语言、输出格式一致性、工具调用行为、决策模式
**RAG 提供：**当前事实信息、来源引用、访问控制知识、频繁更新的数据
对于大多数企业智能体部署，答案不是"RAG 还是微调「而是」微调用于行为，RAG 用于知识。"
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