📄 中文摘要
RAG（检索增强生成）架构已成为构建需要领域特定知识的AI应用的主流模式。Perplexity AI每月处理超过1亿个查询，而GitHub Copilot则参考整个代码库以建议上下文相关的代码。这种架构的优势在于其成本效益，通常比微调便宜10到100倍，并且能够与任何大型语言模型（LLM）兼容。此外，当数据发生变化时，RAG架构能够即时更新，确保AI应用始终基于最新的信息进行工作。
RAG架构：构建了解您数据的AI应用
出处: RAG Architecture: Building AI Apps That Know Your Data" platform
发布: 2026年3月25日
📄 English Summary
RAG Architecture: Building AI Apps That Know Your Data" platform
Retrieval-Augmented Generation (RAG) architecture has emerged as the dominant pattern for building AI applications that require domain-specific knowledge. Perplexity AI processes over 100 million queries per month, while GitHub Copilot references entire codebases to suggest context-aware code. The advantages of RAG include its cost-effectiveness, being 10 to 100 times cheaper than fine-tuning, and compatibility with any large language model (LLM). Additionally, RAG allows for instant updates when data changes, ensuring that AI applications operate based on the most current information.