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The Retrieval Layer of AI: How RAG and HyDE Improve the Quality of LLM Answers

As large language models become more capable, the biggest determinant of answer quality is no longer generation, it’s retrieval. Two approaches now dominate this space: Retrieval-Augmented Generation (RAG) and Hypothetical Document Embedding (HyDE). While both aim to ground LLM responses in relevant source material, they take fundamentally different paths to get there. Understanding the tradeoffs between RAG vs HyDE is essential for anyone designing reliable AI systems, because the choice directly impacts accuracy, relevance, latency, and user trust. Although GraphRAG is also another option, I will cover this in a separate article.