Verified75% confidenceFactTime unknown
HyDE (Hypothetical Document Embeddings) is a query rewriting strategy in which a large model first generates a hypothetical answer document, which is then used for retrieval.
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75%
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Long-term
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6/3/2026
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Unverified假设文档嵌入（HyDE）先让LLM根据问题生成假设性回答，再用该假设回答检索真实文档以提升语义匹配精度66% similarUnverified在 Dify 中推荐使用通义千问的 Embedding 模型用于知识库检索等 RAG 功能66% similarUnverifiedThe core idea of this method is that after an LLM provides an answer and explanation, concepts not mentioned in the explanation are removed from the original input, and the model is re-queried with the simplified input.60% similarUnverifiedA practical RAG technique is to first have the model write out a plausible answer and then search in the vicinity of that hypothetical answer to improve recall quality59% similarVerifiedRAG effectively mitigates the two major problems of large language models—knowledge cutoff and hallucination—by forcing the model to extract answers from trusted documents.59% similar
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