Mastering Query Decomposition for RAG Pipelines: A Practical Guide

Last month, I was debugging a RAG system that consistently failed on questions requiring cross-document synthesis. The user asked, "Compare the Q3 revenue growth of Company A against its main competitor mentioned in the 2023 annual report." The system retrieved the revenue for Company A, but it completely ignored the "main competitor" part of the query.

It’s a classic failure mode. When we dump a complex, multi-layered question into a standard vector search, the embedding model often focuses on the most prominent keywords, ignoring the structural requirements of the prompt. We were effectively asking the LLM to perform a "needle-in-a-haystack" search while simultaneously trying to synthesize an answer. It was doomed to fail.

To fix this, I moved toward a query decomposition strategy. Instead of relying on a single retrieval pass, I started breaking down user requests into a series of smaller, atomic queries.

Why Query Decomposition Matters for RAG Pipelines

In most basic implementations, we map a user's input directly to a vector embedding. If the query is complex, the resulting vector represents a "mush" of all the intent, which rarely maps cleanly to a single chunk of data in your vector database.

By using query decomposition, we transform one vague request into a directed graph of sub-tasks. This approach is essential for multi-hop reasoning, where the model needs to gather information from step A to even know what to look for in step B.

When we implemented this, we saw our "retrieval miss" rate drop by roughly 40%. It didn't just help with accuracy; it gave us a clearer audit trail of what the system was actually doing. If the final answer was wrong, I could look at the decomposed sub-queries and see exactly which step in the chain failed.

Implementing the Decomposition Pattern

I started by using a simple LLM prompt to split the user's input. Using LangChain or even raw calls to OpenAI’s gpt-4o, you can define a schema that forces the model to return a list of sub-queries.

Here is a simplified example of how we structure this:

PYTHON
# A conceptual look at our decomposition prompt
def decompose_query(user_input):
    prompt = fCE9178">"""
    Break down the following complex question into 2-3 atomic sub-queries 
    that can be answered independently.
    Question: {user_input}
    Return as a JSON list.
    """
    # Call to LLM here...
    return sub_queries

Once you have the sub-queries, you run them through your existing hybrid search in RAG pipelines workflow. The trick is to keep the context persistent. Each step needs to know what the previous step found.

We first tried a "parallel" approach, where we sent all sub-queries to the vector store at once. That failed because the second query often depended on the output of the first. We switched to a sequential, stateful executor. It added about 300ms to the total latency, but the jump in response quality was worth the trade-off.

Reducing Hallucinations via Multi-Hop Reasoning

Hallucinations in RAG often stem from the model trying to "fill in the blanks" when it lacks enough context. By forcing the system to retrieve specific, smaller pieces of data, you provide the model with a tighter context window.

If you don't decompose, the model gets a massive chunk of potentially irrelevant text and a complex question. It’s a recipe for hallucination. When you provide the model with specific, decomposed findings—e.g., "Fact 1: Company A grew 12%. Fact 2: Company B grew 8%"—the LLM’s job shifts from "searching for truth" to "synthesizing provided facts."

I still recommend pairing this with optimizing RAG retrieval using a cross-encoder. Even with decomposition, you might pull in a noisy document. A reranker acts as the final gatekeeper, ensuring the "atomic" facts you just retrieved are actually relevant to your sub-queries.

The Caveats and Trade-offs

This isn't a silver bullet. There are three things I'm still keeping an eye on:

1. Latency: Each step adds a round-trip to your LLM and vector database. If you have a query that needs four hops, you're looking at a noticeable delay for the end user.
2. Cost: You're calling the LLM multiple times per user request. This will inflate your token usage quickly.
3. Complexity: Debugging a chain of five sub-queries is significantly harder than debugging a single retrieval call. You need robust observability, similar to what you’d build using LLM evaluation pipelines, to monitor if the decomposition itself is hallucinating.

If I were to start over, I’d probably build in a "skip" mechanism. If the initial search for the first sub-query returns high-confidence results that answer the entire user prompt, there’s no reason to execute the remaining sub-queries.

I’m currently experimenting with a dynamic agent that decides whether or not to decompose based on the complexity of the query, rather than forcing every request through the same multi-stage pipeline. It’s still early days, but that feels like the right direction for balancing accuracy with performance.

FAQ

Does query decomposition work for all RAG use cases? No. If your system handles simple, single-intent questions (e.g., "What is our refund policy?"), decomposition is overkill and just adds unnecessary latency. Use it only for complex, multi-part questions.

How do I handle the output of the sub-queries? You should maintain a "context buffer." As each sub-query returns, append the results to a history object. When you finally reach the generation phase, pass the entire buffer to the LLM to synthesize the final answer.

Can I use a smaller model for the decomposition step? Absolutely. You don't need a massive model like gpt-4o to break a sentence into smaller parts. Testing with a smaller, faster model like gpt-4o-mini or a local Llama 3 instance can save you significant costs and time.