📄 中文摘要
检索增强生成（RAG）技术已从单一蓝图演变为多样化的架构生态系统，每种架构都旨在满足特定的性能、可扩展性和准确性需求。选择合适的 RAG 模式对于系统的成功至关重要。本指南详细解析了主要的 RAG 架构，包括它们的工作原理、适用场景、潜在缺陷以及可考虑的替代方案。文章中提到的简单 RAG 模式适合于原型开发或构建最小可行产品（MVP），尤其是在领域定义明确且文档结构清晰的情况下。通过理解这些架构，开发者可以更有效地设计和实施 RAG 系统。
导航 RAG 架构生态：从业者指南
出处: Navigating the RAG Architecture Landscape: A Practitioner’s Guide
发布: 2026年2月11日
📄 English Summary
Navigating the RAG Architecture Landscape: A Practitioner’s Guide
Retrieval-Augmented Generation (RAG) has transformed from a single blueprint into a diverse ecosystem of architectures, each tailored for specific performance, scalability, and accuracy requirements. Selecting the appropriate RAG pattern is critical for system success. This guide provides an in-depth analysis of the major RAG architectures, detailing how they function, when to implement them, their limitations, and alternative options to consider. The Naive RAG model is highlighted as particularly suitable for prototyping or developing a Minimum Viable Product (MVP), especially in well-defined domains with clean, structured documentation. By understanding these architectures, practitioners can design and implement RAG systems more effectively.