HyDE (Hypothetical Document Embeddings) is a technique used in information retrieval to improve search results by generating a “hypothetical” document that represents an ideal answer to a query. Instead of directly matching a user’s query to existing documents, HyDE first uses a language model to create a synthetic document that answers the query. This synthetic document is then converted into an embedding (a numerical vector) and compared to embeddings of real documents in a database. The closest matches are returned as results. The key idea is that the hypothetical document captures the intent and context of the query better than the raw query text, leading to more accurate retrieval.
A common use case for HyDE is when traditional keyword-based or embedding-based search methods struggle to understand the user’s intent. For example, if a user searches for “How to fix a leaky pipe,” keyword matching might return documents containing “leaky” and “pipe” but miss relevant results that use terms like “plumbing repair” or “water leakage.” HyDE addresses this by generating a hypothetical answer, such as a step-by-step guide mentioning tools like wrenches or pipe tape, and then using this generated text to find documents with similar semantic content. This approach is particularly useful for ambiguous or overly broad queries, as the hypothetical document acts as a bridge between the query and the target content.
HyDE is best applied in scenarios where precision matters more than latency, and where the dataset contains dense, context-rich information. For instance, in technical support systems, legal document retrieval, or academic research, users often have complex needs that aren’t easily expressed in simple keywords. However, HyDE adds computational overhead because it requires generating a synthetic document for every query. Developers should consider this trade-off: if your system prioritizes speed (e.g., real-time chat), traditional embeddings might suffice. But if accuracy is critical and you have the resources to run a language model during retrieval, HyDE can significantly improve results. It’s also worth combining HyDE with hybrid search techniques—like filtering results using keywords first, then refining with HyDE—to balance efficiency and effectiveness.