RAG 是把信息检索与文本生成结合起来的大模型应用架构，让模型先在外部知识库中检索出相关文档、再把检索结果连同问题一起交给大语言模型生成回答，核心是让生成过程能访问并引用外部权威知识，能力包括查询改写、混合检索、重排序与结果压缩。它解决的是模型不重新训练就无法获取最新与私有知识、事实性错误与幻觉频发的问题，并不解决检索本身召回不到正确内容的问题。
RAG 的核心是检索先于生成，它可以帮助企业实现以下目标：
- 让回答能够引用真实来源。生成结果基于检索到的文档，答案出处可以逐条追溯到原文。检索结果按相关度排序后只取前若干条送入上下文，避免无关内容稀释模型注意力。
- 让知识可以即时更新。业务资料更新进知识库即生效，不需要为新知识重训模型。高阶形态在检索前后各加一道处理——检索前改写查询以对齐术语，检索后重排序并压缩，降低噪声进入生成环节的比例。
- 让私有数据在受控范围内被使用。内部文档通过检索进入生成环节，数据边界由权限体系控制。
- 让检索质量可被单独优化。召回率与重排序效果可以独立评测，不必通过重新训练来改结果。
总之，RAG 可以帮助企业实现大模型从只会聊天到真正能用于业务的转变，让通用语言能力与外部知识库的可信、可更新、可审计特性结合起来，降低事实性错误与幻觉带来的业务风险，在不改动模型的前提下支撑垂直场景落地。
翻译
RAG is an application architecture that combines information retrieval with text generation, letting a model first retrieve relevant documents from an external knowledge base and then hand the retrieved results together with the question to a large language model for answer generation. Its core is enabling the generation process to access and cite authoritative external knowledge, with capabilities covering query rewriting, hybrid retrieval, reranking and result compression. It solves the problems of a model being unable to acquire the latest or private knowledge without retraining and of frequent factual errors and hallucinations, but it does not solve the problem of retrieval itself failing to recall the correct content.
At the heart of RAG is retrieval preceding generation, which can help companies achieve the following:
- Let answers cite real sources. Generation is grounded in retrieved documents, so the origin of each answer can be traced back to the original text.
- Let knowledge be updated immediately. Business materials take effect once added to the knowledge base, with no retraining required for new knowledge.
- Let private data be used within a controlled scope. Internal documents enter generation through retrieval, and the data boundary is governed by the permission system.
- Make retrieval quality independently optimizable. Recall and reranking effectiveness can be evaluated separately, without changing results through retraining.
In short, RAG can help enterprises shift large models from merely chatting to genuinely serving business, combine general language capability with the trustworthy, updatable and auditable properties of external knowledge bases, reduce the business risk from factual errors and hallucinations, and support vertical scenario deployment without modifying the model.
