HyDE, or Hypothetical Document Embeddings, is an innovative approach to dense retrieval in search engines that enhances information search efficiency and accuracy without relying on labeled data. By utilizing language models like GPT-3 to generate hypothetical documents, HyDE encodes these into embedding vectors, which help identify similar real documents in a corpus through vector similarity, thereby presenting the most relevant search results. This method addresses the challenges of zero-shot learning by offloading the task of modeling relevance to a language model capable of generalizing across various queries and tasks, enabling effective cross-lingual and flexible search applications. Implementing HyDE requires a base embedding model and an LLMChain, with customizable prompts to fine-tune document generation, while the use of the HypotheticalDocumentEmbedder allows for efficient retrieval of relevant information by generating "dummy" embeddings that reserve space for future real documents. The approach is particularly useful in scenarios with limited training data, enhancing the retrieval phase of RAG (Retrieval-Augmented Generation) pipelines by providing more precise context for generating responses.