#HyDE
Hypothetical Document Embeddings (HyDE) improve retrieval by generating a hypothetical answer and using its embedding to find relevant documents. Posts cover HyDE implementation, integration with RAG pipelines, and how HyDE improves retrieval quality for ambiguous or complex queries.
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Embedding a question and embedding an answer often produce different vectors. HyDE generates a hypothetical answer to the question, embeds that, and retrieves on it. Retrieval quality goes up disproportionately.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.