HyDE for RAG: Turnkey Hypothetical Document Embeddings

Standard RAG often fails to retrieve the right documents due to asymmetry in embedding space: the query is encoded into a vector from the question cloud, far from the document cloud. The solution is HyDE (Hypothetical Document Embeddings). An LLM generates a hypothetical answer to the query; its embedding naturally lies close to real documents. Our team implements HyDE turnkey—from model selection to production integration. With over 5 years of experience, we have completed 30+ projects improving RAG, guaranteeing an MRR improvement of 10–15%.

Why standard retrieval falls short

In vanilla RAG, the query embedding lands in a region distinct from that of documents. A hypothetical answer embedding, on the other hand, lands near documents. The difference is significant: on a legal dataset (8,500 docs), standard retrieval yields MRR@5=0.68, while HyDE gives 0.77 (+13% accuracy).

Standard RAG:
Query → Embedding(query) → search → documents

HyDE:
Query → LLM → Hypothetical_Response → Embedding(response) → search → documents

How HyDE works in practice

Consider a legal example. The query "What is the statute of limitations for labor disputes over unpaid wages?" typically searches for documents mentioning deadlines. The LLM generates a hypothetical document like "According to the Labor Code, the statute of limitations for wage-related disputes is 3 months…" The embedding of this document closely matches real code articles. Result: retrieval finds not just documents with the word "deadline", but precisely those describing the deadline—accuracy improves.

Why HyDE gives a 10-15% accuracy boost

The reason lies in embedding space collision. Queries are posed as questions—their vectors concentrate around interrogative constructions. Documents are written declaratively, so their embeddings occupy a different area. HyDE generates text in the style of a document, and its embedding automatically falls into the cluster of real documents. On the 8,500-document legal dataset we measured an MRR@5 increase from 0.68 to 0.77—a 13% improvement.

How HyDE generates hypothetical documents: an example with LangChain

Implementation via LangChain is minimal. The following code shows generation of a hypothetical document and search in Qdrant.

from langchain.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_community.vectorstores import Qdrant

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.3)
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vectorstore = Qdrant.from_existing_collection(
    embeddings=embeddings,
    collection_name="legal_docs",
    url="http://localhost:6333",
)

HYDE_PROMPT = ChatPromptTemplate.from_template("""Write a short excerpt of a document (150-250 words)
that fully answers the following question.
Write as a fragment of an official document, without introductory phrases like "According to the document".

Question: {question}

Hypothetical document:""")

def hyde_retriever(question: str, top_k: int = 5) -> list:
    hypothetical_doc = llm.invoke(
        HYDE_PROMPT.format_messages(question=question)
    ).content
    docs = vectorstore.similarity_search(hypothetical_doc, k=top_k)
    return docs

question = "What is the statute of limitations for labor disputes over unpaid wages?"
docs = hyde_retriever(question)

When to use HyDE

HyDE is especially effective for large corpora (10,000+ documents) in specific domains: law, medicine, technical documentation. You pay 300–500 ms of latency for the extra LLM call. For short queries (numbers, dates), it's better to combine with standard retrieval and pass through a reranker.

Comparison of methods in practice

On the 8,500-document legal dataset with 300 test queries we obtained:

HyDE boosts accuracy, but latency increases. For production we recommend caching and parallel LLM calls.

What's included in our HyDE implementation

• Corpus and query analysis: evaluate applicability of HyDE, collect baseline metrics.
• HyDE integration: set up the hypothetical document generation pipeline with your LLM (GPT-4o, Claude, LLaMA).
• Prompt tuning: 2–3 days of experiments with temperature, style, and response length.
• Testing on your data: measure MRR, NDCG, latency p99.
• Optimization: implement caching, parallel LLM calls, combine with multi-query.
• Documentation and training: handover of code, instructions, and a demo session for your team.
• Support: 2 weeks of post-production monitoring and adjustments.

Implementation timeline

The cost is calculated individually—it depends on corpus size, number of LLM calls, and custom modifications. Contact us: describe your task—we'll propose the optimal solution. If you want to further improve accuracy, order a corpus audit—it will show how effective HyDE is for your specific data.

For more details on HyDE implementation, see LangChain documentation.

Get a consultation: tell us about your challenges—we'll select the right HyDE implementation option.