Query Decomposition for Complex Questions: How Stepwise LLM Reasoning Improves Search Accuracy

When you ask a question like "Did Microsoft or Google make more money last year, and how did their cloud revenue compare?", most search engines give you a bland list of links. But what if the system could break that question down, answer each part accurately, then stitch the answers together into one clear response? That’s what query decomposition does - and it’s changing how AI understands complex questions.

Why Simple Search Fails on Complex Questions

Most AI systems today use one of two approaches: direct retrieval or query expansion. Direct retrieval just matches your words to documents. Query expansion adds synonyms - like swapping "profit" for "revenue" - hoping it helps. But neither works well when the question has multiple layers.

Take this example: "What caused the 2023 silicon chip shortage, and how did it affect Tesla’s production compared to Ford?" A simple system might pull up articles about chip shortages, then articles about Tesla, then articles about Ford. But it won’t connect the dots. It won’t compare. It won’t explain cause and effect. The result? A messy, incomplete answer.

Studies show traditional systems get only 43.2% of these multi-part questions right. That’s worse than flipping a coin.

How Query Decomposition Works

Query decomposition treats complex questions like puzzles. Instead of answering all at once, it breaks them into smaller, answerable pieces. Think of it like a chef prepping ingredients before cooking - you don’t throw everything into the pot at once.

The most effective method, called ReDI (Reasoning-enhanced Query Decomposition through Interpretation), uses three steps:

1. Decompose: The LLM analyzes your question and splits it into sub-questions. For the Tesla/Ford example, it might generate: "What caused the 2023 silicon chip shortage?" and "How did Tesla’s production change in 2023 compared to Ford’s?"
2. Interpret: Each sub-question gets enriched with context. Instead of just searching for "Tesla production," the system might also look for "Tesla quarterly output," "Tesla factory downtime," or "Tesla supply chain delays." This boosts retrieval by 18.6%.
3. Fuse: Results from all sub-questions are combined into one final answer, weighted by relevance and consistency.

This approach isn’t magic - it’s structured. According to the BRIGHT benchmark, it improves accuracy from 43.2% to 66.9% on complex queries. That’s more than a 50% jump.

Where It Shines: Three Types of Questions

Query decomposition doesn’t help with everything. But it’s a game-changer for three specific kinds of questions:

• Comparative questions: "Which is better, iPhone or Pixel?" or "Did Apple or Samsung sell more phones in 2024?" - These see a 28.4% accuracy boost because the system compares apples to apples (pun intended).
• Causal synthesis: "Why did inflation rise in 2022, and how did it impact small businesses?" - Understanding chains of cause and effect improves by 25.1%.
• Multi-faceted analysis: "What are the environmental, economic, and social impacts of electric vehicles?" - These require pulling together three different data streams. Query decomposition handles them 23.7% better than older methods.

These aren’t theoretical gains. Companies using this in enterprise search report real improvements. One financial firm saw its ability to answer investor questions jump from 49% to 73% accuracy after switching to a decomposition pipeline.

What You Need to Make It Work

You can’t just plug any LLM into a decomposition system and expect magic. Here’s what actually matters:

• Model size: Models under 7 billion parameters (like Mistral-7B) struggle. GPT-4-class models (1.8 trillion parameters) are 42.8% more accurate at decomposition.
• Context window: Long context matters. Mistral-7B-Instruct with a 32K token window generates 37.2% more relevant sub-questions than 8K-window models.
• Cost-performance balance: GPT-4o-mini delivers the best trade-off: $0.00015 per decomposition step. That’s cheaper than most people realize.
• Decomposition threshold: Don’t decompose every question. Simple ones like "What’s the capital of France?" get slower and less accurate. Use confidence scores above 0.75 to decide when to split.

Most teams spend 2-3 weeks learning the basics, and another 2-3 weeks tuning. The Haystack framework makes this easier - 68% of developers get it working in under 3 days using their templates.

The Trade-Off: Speed vs. Accuracy

There’s no free lunch. Query decomposition adds 1,200-1,800 milliseconds to response time. That’s 1.2 to 1.8 seconds. For a chatbot? That’s noticeable. For a business intelligence dashboard? Usually acceptable.

Some users complain about over-decomposition - systems splitting simple questions into unnecessary pieces. One Reddit user spent three weeks calibrating their system to stop decomposing 85% of queries. The fix? A classifier that only triggers decomposition when intent complexity is high.

Mobile apps suffer most. Google’s Dr. Sarah Wong warned at the ACL conference that the extra processing may not be worth it on low-power devices. But for desktop, cloud, or enterprise tools? The delay is a fair price for better answers.

Real-World Adoption

This isn’t just academic. Adoption is growing fast:

• Only 3.1% of enterprise search systems used query decomposition in late 2024.
• By mid-2025, that jumped to 12.4%.
• Gartner predicts 65% will use it by 2027.

Industries leading the charge:

• Financial services: 23.7% adoption - analysts need complex comparisons daily.
• Healthcare: 18.2% - doctors ask multi-part questions about treatments, side effects, and guidelines.
• Government: 15.9% - policy research often involves layered cause-effect analysis.

The tools? Three main paths:

• Academic frameworks: Like ReDI - powerful but hard to implement. Only 32% of new systems use these.
• Commercial platforms: Elastic and Coveo now bundle decomposition features. 41% adoption - easiest for most companies.
• Custom in-house: 27% - used by tech giants like Meta and Microsoft who need full control.

What’s Next?

The field is moving fast. ReDI 2.0, released in September 2025, adds dynamic depth adjustment - meaning it automatically decides how many sub-questions to generate based on how complex the input is. This improved performance on very hard queries (4+ parts) by 31.2%.

Upcoming features:

• Automated threshold calibration: Haystack’s team is building a tool that learns from user feedback to auto-tune decomposition rules.
• Multi-modal support: Google Research announced plans to handle questions that mix text, images, and tables - like "What’s the trend in this chart, and how does it compare to last year?"
• Hardware acceleration: Intel is designing NPUs (Neural Processing Units) optimized for decomposition pipelines, targeting 2027 release.

And integration with knowledge graphs? That’s the next frontier. When decomposition creates intermediate reasoning nodes, it becomes easier to trace how an answer was built - improving transparency and trust.

Is It Right for You?

Ask yourself:

• Do your users ask multi-part, analytical, or comparative questions often?
• Is response speed critical (like in a live chat), or can you afford 1.5 seconds more?
• Do you have enough labeled examples to train a good intent classifier? (At least 500.)
• Are you building for enterprise, or for mobile consumers?

If you answered yes to the first and last, you’re likely a good fit. If you’re building a consumer app with simple queries? Skip it. You’ll slow things down for no gain.

Final Thought

Query decomposition isn’t about making AI smarter. It’s about making it think more like a human. We don’t answer complex questions in one leap. We break them down. We research each piece. We connect the dots.

Now, AI can do the same. And for anyone dealing with messy, layered information - from legal researchers to financial analysts - that’s not just an upgrade. It’s a necessity.