Unverified50% confidenceFactExact time
RAG的核心工作流程分为五步：接收用户查询、生成检索关键词、调用外部数据源、将检索结果注入提示词上下文、基于最新信息生成回答
1
Sources
50%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
Related Claims
UnverifiedThe working principle of RAG is divided into a retrieval phase (converting the question into a vector to recall relevant content from the knowledge base) and a generation phase (inputting the recalled context together with the question into the model to produce an answer).85% similarVerifiedRAG（检索增强生成）技术架构将文档切片、向量化、存入向量数据库，查询时先检索相关片段再交给模型生成回答78% similarUnverifiedThe core process of Native RAG includes five steps: document chunking, embedding vectorization, storing in a vector database, semantic retrieval, and generating answers.78% similarVerifiedThe complete RAG pipeline is divided into two major phases: the data indexing phase and the query retrieval phase.78% similarVerifiedThe typical workflow of RAG (Retrieval-Augmented Generation) consists of four steps: document chunking, vector embedding, similarity search, and context injection.78% similar
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