HyDE (Hypothetical Document Embeddings) is a method used to improve the performance of Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs), particularly in handling queries from new or unseen domains.

Background: Many existing embedding retrievers struggle to generalize well to new domains they haven’t encountered before. HyDE addresses this issue by using a unique approach to generate and use hypothetical documents for better information retrieval.

How It Works: When given a query, HyDE first uses a zero-shot prompt to instruct a language model to generate several „fake“ hypothetical documents. These documents are designed to capture relevant textual patterns related to the query. In practice, five hypothetical documents are generated.

Each of these hypothetical documents is then encoded into an embedding vector. These vectors are averaged to create a single, consolidated embedding. This consolidated embedding is then used to identify a neighborhood in the document embedding space. Similar actual documents are retrieved based on vector similarity to this single embedding.

Application: The retrieved documents can then be used in various downstream tasks. For example, they can be fed into a generator model in a RAG pipeline, which uses the retrieved documents to enhance the generation of accurate and contextually relevant responses.


  • Improved Generalization: HyDE helps embedding retrievers perform better in new and unseen domains.
  • Enhanced Retrieval Accuracy: By generating hypothetical documents that capture the essence of the query, HyDE improves the retrieval of relevant documents.
  • Integration with RAG: The retrieved documents can be effectively used in RAG pipelines to boost the quality of generated outputs.

For more details on this method, refer to the paper „Precise Zero-Shot Dense Retrieval without Relevance Labels.

HyDE represents a significant advancement in AI, enhancing the capabilities of retrieval systems and improving the performance of LLMs in generating accurate and contextually grounded responses.

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