Self-Ask Prompting: Solve Problems with Self-Questioning

Want clearer answers to complex questions? The Self-Ask Prompting technique instructs the AI to generate and answer intermediate questions related to a problem before delivering the final answer. This simulates a self-questioning process, resulting in more complete and well-founded responses.

If you’re an end user who uses AI for analysis, research, or solving complex problems, mastering Self-Ask Prompting will significantly improve the quality of your interactions.

This article was created to help end users understand the core concepts, make technical concepts more accessible, and intentionally and explicitly adapt and apply the Self-Ask Prompting technique in their daily use of AI, without automatically relying on the model’s capabilities. For further technical exploration, see Learn More.

What Is the Self-Ask Prompting Technique?

Self-Ask Prompting is a prompt engineering technique that instructs the AI to break down a complex task or question into relevant subquestions, answer them, and use those answers to build the final solution. For example, to answer “Why are bees important to the ecosystem?”, the AI might generate questions like “What do bees do in plants?” or “How does pollination affect food production?” and answer them before synthesizing the final response. This approach imitates human reasoning, where questions guide problem exploration.

The benefits include greater clarity, more structured answers, and the ability to address complex issues logically. It’s an accessible technique that empowers users at all levels to obtain robust responses, especially in educational, analytical, or problem-solving contexts.

Origin of the Self-Ask Prompting Technique

Self-Ask Prompting gained prominence in 2022 with studies exploring how language models can improve reasoning by generating internal questions. Inspired by techniques like Chain-of-Thought and Socratic Prompting, it is widely used in tools like ChatGPT, Claude, and Grok, offering a structured approach to complex tasks.

How Does It Work?

Self-Ask Prompting is applied by asking the AI to generate intermediate questions that break down the problem, then use the answers to build the final solution. Follow these steps:

1. Define the task or question: Specify the problem or question that requires analysis (e.g., “Why are bees important?”).
2. Ask for intermediate questions: Instruct the AI to generate relevant subquestions to explore the topic (e.g., “What questions must be answered to understand the importance of bees?”).
3. Request answers to the subquestions: Ask the AI to answer each subquestion clearly and concisely.
4. Ask for the final answer: Instruct the AI to synthesize the responses into a complete solution to the original question.
5. Specify the format: Choose a clear format such as paragraphs, bullet points, or sections to organize the response.

Think of it as asking the AI to “talk to itself,” asking and answering questions to arrive at a deeper answer. You can also iterate—refining subquestions or requesting more details—to fine-tune the response.

When to Use It?

Self-Ask Prompting is ideal for situations that require structured reasoning, clarity on complex topics, or exploration of open-ended questions. Use it when you want to:

• Solve complex questions: Perfect for open-ended queries like “Why does something happen?” or “How does it work?”
• Deepen understanding: Great for guided learning or detailed analysis.
• Structure reasoning: Ideal for ensuring logical answers in academic or professional contexts.

Practical Examples of Self-Ask Prompting in Action

See how to apply Self-Ask Prompting in real-world scenarios with prompts you can adapt. The examples are simple, practical, and reflect common prompt engineering practices, showing how self-questioning leads to robust answers. The first example is interactive, with user-driven refinement; the second is direct and simplified.

Example 1: Understanding the Importance of Bees in the Ecosystem (Interactive)

Context: You’re a biology student needing to understand the importance of bees for a school paper.

Initial prompt:

To understand why bees are important to the ecosystem, generate three relevant subquestions. Answer each subquestion in one short sentence using simple language for high school students.

Expected initial response:

• Subquestion 1: What do bees do when they visit flowers?
  Answer: Bees collect nectar and pollen, pollinating flowers by transferring pollen between plants.
• Subquestion 2: How does pollination affect plants?
  Answer: Pollination allows plants to produce fruits, seeds, and new plants, preserving biodiversity.
• Subquestion 3: What types of plants depend on bees?
  Answer: Many crops like apples, almonds, and pumpkins rely on bee pollination.

Refinement prompt:

Based on the subquestions provided, generate an additional subquestion about the impact of the absence of bees in the ecosystem. Answer in one short sentence using simple language.

Expected refinement response:

• Additional subquestion: What would happen if bees disappeared from the ecosystem?
  Answer: Without bees, food production would decrease, affecting agriculture and the food chain.

Final prompt:

Using the four generated subquestions and answers, explain why bees are important to the ecosystem. Format the answer in a paragraph using clear language for high school students.

Expected final response:
Bees are essential to the ecosystem because they pollinate plants, helping produce food and preserve biodiversity. When they visit flowers, they carry pollen that allows crops like apples and almonds to grow. This supports farming and the food chain. Without bees, many plants would stop producing, food supplies would shrink, and animals that depend on these plants would be affected. That’s why bees are crucial for the environment and food security.

Why it works:
The interactive example shows a three-step dialogue: the initial prompt generates basic subquestions and answers, the refinement adds a question about bee extinction, and the final prompt synthesizes everything into a clear explanation. The format (“paragraph”) and language (“simple for high school”) make the response accessible and structured.

Example 2: Analyzing How 3D Printing Technology Works

Context: You’re an entrepreneur exploring 3D printing for a new manufacturing business.

Single prompt:

To explain how 3D printing technology works, generate four relevant subquestions. Answer each subquestion in one short sentence and use the answers to explain how 3D printing works in bullet points. Use objective language for a business report.

Expected result:

Subquestions and Answers:

• Subquestion 1: What materials are used in 3D printing?
  Answer: 3D printing uses materials like plastics, metals, and resins, depending on the application.
• Subquestion 2: How does a 3D printer create an object?
  Answer: The printer builds the object layer by layer, following a digital model.
• Subquestion 3: What is a digital model in 3D printing?
  Answer: A digital model is a 3D file created by software that guides the printer.
• Subquestion 4: What are the main types of 3D printing technologies?
  Answer: Common technologies include FDM (fused deposition modeling) and SLA (stereolithography).

Explanation of How It Works:

• 3D printing starts with a digital model designed in software, which defines the object’s shape.
• The 3D printer uses materials like plastic or metal, chosen based on the object’s purpose.
• The object is built layer by layer, with the printer depositing or solidifying material according to the model.
• Various technologies, like FDM for plastic and SLA for resin, allow applications in manufacturing, medicine, and design.

Why it works:
The prompt instructs the AI to generate and answer relevant subquestions, ensuring a solid foundation for the explanation. The format (“bullet points”) and tone (“objective for a report”) organize the response clearly and professionally.

Start Using Self-Ask Prompting Now

Self-Ask Prompting is a powerful technique for solving complex questions, structuring reasoning, and obtaining deeper answers by asking the AI to generate and answer subquestions. Whether learning concepts or analyzing technologies, it ensures clarity and logic—helping you craft more effective prompts.

Benefits of the technique:

• More structured responses with guided reasoning
• Ideal for complex questions or guided learning
• Accessible to beginners, robust for advanced users

🎯 In summary

🧠 Technique: Self-Ask Prompting.
💡 Ideal for: Solving complex questions, deepening understanding, structuring reasoning.
✅ Helps you: Solve problems through internal questioning.

Extra Tip

Combine Self-Ask Prompting with the Generated Knowledge Prompting technique to generate initial facts, then explore subquestions such as “What questions help me understand these facts?” See more in our series!

Advanced Variations

Techniques like Socratic Prompting (external reflective questions) or Chain-of-Thought (linear reasoning) can complement Self-Ask Prompting, but this one is ideal for automated self-questioning.

🔗 Want to explore more techniques like this?
Read our Practical Guide to Prompt Techniques, Frameworks, and Formulas for LLMs

Learn More

Check out related studies:

• Press et al., 2022: Measuring and Narrowing the Compositionality Gap in Language Models
• Brown et al., 2020: Language Models are Few-Shot Learners