📄 中文摘要
检索增强生成（RAG）是一种强大的技术，旨在使大型语言模型（LLMs）更智能、更真实和更及时。RAG 不仅依赖于模型的训练数据，而是首先检索相关的外部信息，然后基于这些信息生成答案。RAG 不是单一模型，而是一个由多个步骤组成的管道。文章详细解析了 RAG 的核心组件，帮助读者建立清晰的思维模型，理解 RAG 的工作原理及各个组件的重要性。
RAG 组件解析：现代人工智能的构建模块
出处: RAG Components Explained: The Building Blocks of Modern AI
发布: 2026年3月25日
📄 English Summary
RAG Components Explained: The Building Blocks of Modern AI
Retrieval-Augmented Generation (RAG) is a powerful technique designed to enhance the intelligence, factual accuracy, and currency of Large Language Models (LLMs). Instead of solely depending on the training data, RAG retrieves relevant external information first and then generates answers based on that information. RAG is not a single model but a pipeline composed of multiple steps. The article breaks down the core components of RAG in detail, helping readers develop a clear mental model of how RAG operates and the significance of each component.