Why Your LLM Gets Lost on Hard Questions
Ask a large language model something simple, like "What’s the capital of France?" - it answers instantly. But ask it something layered, like "Who won the Master’s Tournament the year Justin Bieber was born?" - and suddenly it stumbles. It might guess wrong. It might make up a fact. It might give you a long, confident answer that’s completely wrong.
This isn’t a bug. It’s a fundamental limit. Even the most advanced models like GPT-4o or Claude 3 aren’t magic. When a question needs multiple steps - pulling facts from different places, connecting timelines, comparing events - they often fail because they try to do it all in one go. That’s where self-ask and decomposition prompting come in. These aren’t fancy tricks. They’re structured ways to make the AI think like a human: break the problem down, solve each part, then put it together.
Research shows these techniques boost accuracy on multi-step questions from 68% to over 82%. That’s not a small gain. That’s the difference between guessing and reliably getting it right. And it works even on smaller, cheaper models. You don’t need the most expensive API. You just need to change how you ask.
What Self-Ask Prompting Actually Does
Self-ask prompting forces the model to generate its own follow-up questions before answering. It doesn’t just reason internally. It writes out each step like a person working through a puzzle on paper.
Here’s how it looks in practice:
- Question: Who won the Master’s Tournament the year Justin Bieber was born?
- Follow up: When was Justin Bieber born?
- Intermediate answer: Justin Bieber was born on March 1, 1994.
- Follow up: Who won the Masters Tournament in 1994?
- Intermediate answer: Jose Maria Olazabal won the 1994 Masters Tournament.
- Final answer: Jose Maria Olazabal won the Masters Tournament the year Justin Bieber was born.
Notice the structure. Each step is labeled. Each answer is separate. This isn’t just for show. Studies from arXiv (2025) show that when models write out these steps, they’re 78.9% accurate on this type of question - compared to just 42.3% without it.
The key is the scaffolding. Without clear markers like "Follow up:" and "Intermediate answer:", the model often reverts to guessing. The labels act like training wheels. They force the AI to pause, reflect, and verify before moving on. It’s like telling a student to show their work on a math test - you’re not just checking the answer. You’re checking the thinking.
Decomposition Prompting: Two Ways to Break It Down
Decomposition prompting is the broader category that includes self-ask. But it has two main styles: sequential and concatenated.
Sequential decomposition is like solving a Rubik’s cube one face at a time. You solve the first sub-question, then use that answer to guide the next. This is the most accurate method. According to the same 2025 arXiv study, it improves accuracy by 12.7% over concatenated methods on math problems. Why? Because each step builds on the last. If the first answer is wrong, you catch it before moving on.
Concatenated decomposition is like dumping all the sub-questions at once: "What’s Justin Bieber’s birth year? Who won the Masters in 1994?" The model answers them all together. It’s faster and uses fewer tokens, but it’s riskier. If one sub-answer is off, it can throw off the whole chain. It works better for simpler problems or when speed matters more than perfection.
Most users start with sequential. It’s more reliable. Even if it takes a little longer, you get fewer wrong answers - and that’s usually worth the wait.
Where These Techniques Shine (and Where They Don’t)
Self-ask and decomposition aren’t magic bullets. They’re tools. And like any tool, they work best in the right hands - and for the right jobs.
Best for:
- Multi-hop fact synthesis (e.g., connecting events across time, people, places)
- Math problems with multiple operations
- Legal or medical reasoning that requires checking rules against facts
- Financial analysis: "What was Apple’s stock price when the iPhone 12 launched, and how did it compare to the previous year?"
These are all problems where the answer isn’t stored in one place. You have to piece it together.
Worst for:
- Open-ended creative tasks: "Write a poem about regret."
- Philosophical questions: "Is free will an illusion?"
- Highly abstract reasoning
On these, decomposition can backfire. A 2025 study found accuracy dropped by 9-11% on philosophical questions because the model forced artificial structure where none existed. It created false dichotomies - like splitting "Is AI conscious?" into "Does AI feel pain?" and "Does AI have desires?" - neither of which are valid sub-questions. The model wasn’t thinking. It was mimicking a process.
Know when to use it. If the problem has clear, factual steps - use decomposition. If it’s about meaning, tone, or creativity - stick with simple prompts.
Real-World Results: What Users Are Saying
People aren’t just testing this in labs. They’re using it every day.
A data analyst on Reddit said self-ask cut her client query resolution time by 27%. Why? Instead of guessing answers to complex financial reports, she broke each question into sub-questions: "Which quarter? What metric? What year-over-year comparison?" She got faster, more accurate results - and her clients noticed.
But there’s a cost. One software engineer on HackerNews noted a 40% spike in API costs. Why? Because each sub-question adds tokens. A single decomposition chain can use 35-47% more tokens than a direct answer. If you’re running this at scale, that adds up.
On G2, users gave decomposition tools a 4.3/5 rating. Top praise: "I can see how the AI got to its answer." That’s huge. In business, auditability matters. If your AI gives a wrong financial forecast, can you trace why? With decomposition, you can. Without it, it’s a black box.
But complaints are real too. 21 out of 32 Trustpilot reviews mentioned slow response times. 18 complained about not knowing how deep to break things down. One user said, "I asked for 3 sub-questions. The AI gave me 12. I got lost."
How to Get Started - Step by Step
You don’t need to be a researcher. You don’t need to retrain a model. You just need to change your prompts.
Step 1: Master Chain-of-Thought first. Before you try self-ask, learn to write prompts like: "Think step by step. Explain your reasoning." That’s the foundation.
Step 2: Pick a simple problem. Try: "If John has 15 apples and gives away 1/3 to Mary, who then shares half of her apples with Tom, how many does Tom have?"
Step 3: Add scaffolding. Write your prompt like this:
"Answer the following question by breaking it into sub-questions. For each sub-question, write 'Follow up:' and then the question. Then write 'Intermediate answer:' and your answer. Finally, give the final answer. Do not skip steps." Question: If John has 15 apples and gives away 1/3 to Mary, who then shares half of her apples with Tom, how many apples does Tom have?
Step 4: Add verification. After each intermediate answer, ask: "Does this make sense? [Yes/No] If no, revise." This catches 20%+ of errors.
Step 5: Iterate. If the model gives too many sub-questions, tell it: "Break this into no more than 3 steps." If it skips steps, say: "You missed a step. Go back and list all required sub-questions."
It takes 8-12 hours of practice to get good. But once you do, you’ll see a huge jump in reliability.
What’s Changing in 2025 - And What It Means for You
OpenAI released GPT-4.5 in November 2025 with built-in decomposition. It now auto-generates sub-questions without you asking. That’s huge. You no longer need to write "Follow up:" - the model does it for you.
But that doesn’t mean you can stop learning. Why? Because the model’s auto-generated steps aren’t always right. Sometimes they’re too shallow. Sometimes they miss key connections. You still need to know what good decomposition looks like to spot when it fails.
Anthropic’s Claude 4, launching in early 2026, will check each intermediate answer against verified databases. That’s the next leap: not just breaking down the problem, but verifying each step with real-world data.
Right now, you’re the verifier. You’re the one checking if "Justin Bieber was born in 1994" is correct. In the future, the AI might do that too. But until then, your job is to make sure the chain is solid.
The Hidden Risk: False Confidence
There’s a dangerous side to decomposition. It makes answers look smart.
A 2025 MIT study found that 22.8% of decomposition chains in scientific domains contained critical errors in intermediate steps - but the final answer still sounded logical. The model got the wrong birth year, then used it to pick the wrong Masters winner. The path looked perfect. The conclusion looked right. But the whole thing was built on a lie.
This is why you can’t just trust the output. You need to validate. Ask: "Is this fact correct?" Cross-check a sub-answer with a trusted source. Don’t assume the AI’s reasoning is flawless just because it’s structured.
Decomposition doesn’t fix bad data. It just makes bad reasoning look better.
Who’s Using This - And Why
This isn’t just for hobbyists. Enterprises are adopting it fast.
In legal tech, 42% of companies use decomposition to analyze contracts. Instead of asking, "Does this clause violate GDPR?", they break it down: "What data does this clause collect? Is it personal data under GDPR? Does it have consent language?"
In healthcare, 38% use it for diagnostic support. "Patient has fever, rash, joint pain. What could this be?" becomes: "What infections cause fever and rash? Which ones also cause joint pain? Are there geographic risk factors?"
Even the EU is stepping in. Its November 2025 AI guidance requires "auditable decomposition chains" for high-risk applications - meaning if your AI helps make a loan decision or diagnoses a tumor, you must be able to show how it got there.
That’s not regulation for regulation’s sake. It’s because decomposition is the only way to prove your AI isn’t hallucinating.
Final Advice: Don’t Automate the Thinking - Guide It
Self-ask and decomposition aren’t about making AI smarter. They’re about making you smarter at using it.
You’re not replacing human judgment. You’re enhancing it. The AI is your assistant - not your replacement. Your job is to ask the right questions, check the steps, and know when to trust the answer.
Start small. Pick one complex question you’ve struggled with. Break it down. Test it. See how much better it works. Then scale it.
The future of AI isn’t bigger models. It’s better prompting. And decomposition is the most reliable way to get there - if you do it right.