If you’ve ever built a RAG system, you’ve likely felt the frustration of the "Mismatch Problem". You ask a perfectly reasonable question, but it returns completely irrelevant documents. Why? Because your retrieval method is searching based upon your question's language . In the vector world, these two things often don't look alike. Eg: Users asking questions to retrieve context from a technical documentation (like company's legal policies) Today, we’re going to master HyDE (Hypothetical Document Embedding) —a technique that flips the script by "hallucinating" the answer before it even touches your database. 📝 What is HyDE? Instead of taking a user's question and searching for it directly, HyDE follows a three-step dance: The Hallucination: It asks an LLM to write a "fake" or hypothetical answer to the user's question in document friendly language (using few-shot prompting). The Embedding: It converts that "fake" answer into a vector.…