Unverified50% confidenceFactExact time
RAG uses a retriever at question time to find the most relevant fragments in the document library and stuff them into the prompt, and the model answers based on the fragments; the document library is a copy of the source files, chopped up and left in place, with no write-back.
1
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50%
Confidence
Long-term
Relevance
10/14/2026
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Related Claims
UnverifiedIn RAG architecture, when a user asks a question, the system first retrieves the most relevant document fragments from a pre-built knowledge index, then feeds these fragments as context into a large language model, which synthesizes them into a final answer76% similarVerifiedRAG通过将文档切分为语义块后经嵌入模型转化为高维向量存入向量数据库，查询时通过近似最近邻（ANN）搜索找到语义最相近的文档片段74% similarVerifiedRAG addresses the limitation of large models' knowledge cutoff dates by splitting private documents into text chunks, converting them into vector representations stored in a vector database, and injecting retrieved relevant content into the Prompt.71% similar
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