The Sequence Knowledge #502: If You are Doing RAG You Need to Know Hypothetical Document Embeddings
Was this email forwarded to you? Sign up here The Sequence Knowledge #502: If You are Doing RAG You Need to Know Hypothetical Document EmbeddingsOne of the most important methods to enable sematically-rich RAG.
Today we will Discuss:
💡 AI Concept of the Day: Understanding Hypothetical Document EmbeddingsContinuing 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... Subscribe to TheSequence to unlock the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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