The Sequence Knowledge #502: If You are Doing RAG You Need to Know Hypothetical Document Embeddings
One of the most important methods to enable sematically-rich RAG.
Today we will Discuss:
An introduction to hypothetical document embeddings(HyDE) as a cornerstone of RAG.
The original HyDE paper.
💡 AI Concept of the Day: Understanding Hypothetical Document Embeddings
Continuing with our series about RAG, today we are going to explore a technique that is often lost in broader RAG implementations but its quite effective.
Hypothetical Document Embeddings (HyDE) represents a paradigm shift in the realm of Retrieval-Augmented Generation (RAG), introducing a novel approach to bridging the semantic gap between queries and document corpora. At its core, HyDE leverages the generative capabilities of LLMs to synthesize a hypothetical ideal document that would perfectly answer a given query, prior to initiating the retrieval process. This synthetic document serves as a high-fidelity proxy for the user's intent, effectively recasting the retrieval problem into a more semantically aligned space.