ModelRefs / HyDE (Hypothetical Document Embeddings) — AI Glossary
HyDE (Hypothetical Document Embeddings) — AI Glossary
A query-expansion technique generating a hypothetical answer document and using its embedding to retrieve real documents.
Overview
HyDE (Gao et al. 2022) prompts the LLM to generate a hypothetical (potentially hallucinated) answer, then embeds that answer rather than the original query. The hypothesis embedding is closer to relevant documents than the short sparse query. Particularly effective for zero-shot dense retrieval where queries are short and retrieval docs are long.
Reference details
| Topic | rag |
|---|---|
| Also known as | hypothetical document embedding |
| Last reviewed | 2026-06-24 |
Related terms
Commonly confused with
A query-expansion technique with a counter-intuitive mechanism: it asks the model to write a plausible answer, then embeds that answer to search with. It works because a hypothetical answer sits closer in embedding space to real answers than a question does. The generated document is deliberately discarded and its factual accuracy does not matter — a point that is easy to miss, since it looks like the model is answering.
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Frequently asked questions
What is HyDE (Hypothetical Document Embeddings)?
A query-expansion technique generating a hypothetical answer document and using its embedding to retrieve real documents.
Is HyDE (Hypothetical Document Embeddings) the same as hypothetical document embedding?
Yes — hypothetical document embedding are common aliases for HyDE (Hypothetical Document Embeddings).
What concepts are related to HyDE (Hypothetical Document Embeddings)?
Closely related concepts include query expansion, dense retrieval, naive rag.