In the realm of information search systems and Retrieval-Augmented Generation (RAG) systems, HyDE, or Hypothetical Document Embeddings, is a game-changer. By generating detailed, hypothetical documents based on user queries, HyDE enhances the process of finding information, making it particularly useful for tasks like web searches, question answering, and fact verification. This blog explores what HyDE is, why you should consider using it, its components and implementation, its integration with frameworks like LlamaIndex and LangChain, and whether it's better to implement HyDE independently or utilize existing APIs.