Questioning is the new currency: How to ask million-dollar questions in the age of AI?

New Production Formula with AI: Aware — Think — Prompt — Create — Sell

AI-Driven Production Cycle

Why asking questions is important? How can one ask a good question in the AI era? When information become currency with AI, how can you ask the million-dollar question?

If every question could generate executable code, verifiable plans, and iterative prototypes — is your “cognitive bank,” where you store your questions, rapidly appreciating or quietly going bankrupt?

This isn’t an interrogation of technology, but a battle for the right to price cognition. When the world puts invisible price tags on every question, the quality of your questioning determines whether you’re a money-printing machine or a bankrupt factory in the AI era.

In this article, I will talk about recent AI mega-events and my indie devloping experience to share with you my recent deep understanding of a new production chain centered around AI-driven questioning:

I. Aware: Penetrating Information Cocoons (Cognitive Awakening)

Question Restructuring Pyramid

• Information Dessert

When information explosion becomes the norm, algorithmic recommendations can also turn into “sweet poison.”

A study published by a Tsinghua University team in Nature Machine Intelligence indicates that after interacting with recommendation systems, information diversity actually decreases for over 57% of active users.

This phenomenon leads us to mistakenly believe we’ve seen countless pieces of information, while we’re unknowingly trapped in our own echo chambers.

To break out of the cocoon, we need to fully leverage new AI tools and multi-modal learning approaches to achieve knowledge equity and active exploration.

• AI Breakthrough

Google’s “Learn About” is a great example: when you ask a question, it instantly unfolds complete relevant knowledge. What’s even more amazing is that you can drill down based on what you’re interested in.

Through a combination of text, images, videos, and explanations, the answer is presented in a systematic and step-by-step manner, just like a game. This helps users of different levels and needs to obtain a suitable knowledge structure.

The guided learning method allows users to not only receive passively but also think actively, broaden their horizons, and avoid getting trapped in a single information stream.

Another great tool is AI-powered long-form summarization and question-and-answer decomposition of books.

For example, Google’s NotebookLLM and the ChatPDF website, through rapid document summarization and indexing, allow people to quickly extract core ideas and deepen their understanding through interactive Q&A.

For mining authentic and diverse user needs, with the proliferation of generative AI content, the information cocoons constructed by AI content are becoming thicker, especially when algorithms that advocate for repetitive trend-following result in a lot of low-quality content.

Places like Reddit, which have more restrictions on AI crawling and commercialization, make genuine human interaction even more valuable.

Limitations of time and energy are also accomplices to information cocoons. GummySearch is an interesting search engine that leverages Reddit’s large amount of original, diverse, and authentic user discussions and hierarchical categorization.

Through Reddit, we can quickly access needs or perspectives that are usually difficult to observe and break free from the “cognitive prisons” formed by common social media platforms.

II. Think: Essence Reconstruction (Thinking Revolution)

• Transformation of Traditional Questioning Thinking

When I was Product Manager, an expert once said that if you can’t write clearly, it means you haven’t thought clearly, and you probably won’t finish the project nicely.

The AI era is the same thing: although information is abundant, if you don’t understand how it actually works, you will often fall into the predicament of “knowing a lot but thinking shallowly.”

To truly make questioning a breakthrough point, we must first trace back to the underlying logical evolution of questioning.

From the Socratic method of questioning in ancient Greece, to the Toyota Five Whys method, to Drucker’s three management questions, the former advocates for revealing implicit premises through continuous questioning, thereby allowing the truth to “come to light”.

The Toyota Five Whys method emphasizes, in a manufacturing context, constantly asking “why” to find the root cause of problems; Drucker uses the three questions of “Who am I, where am I, and what should I do?” to construct the golden triangle of strategy and decision-making.

• Thinking Patterns in the AI Era

In the AI era, the dimensions of questioning have made new breakthroughs: we are overwhelmed with information, and we must learn to be sensitive to the “data density paradox.”

Instead of stuffing all information into our brains, we must understand how to focus in an overloaded state.

“Emergent cognition” further requires us to break away from linear questioning and learn to find unpredictable insights from systemic and complex thinking.

The research from MIT’s Human Dynamics Lab suggests that the human-machine collaborative interaction model allows the questioner and the AI model to create an interactive effect akin to a “pas de deux.” At this point, knowing how to grasp the “meta-question” in vast amounts of information is especially critical.

On this path, a “million-dollar questioning code” that focuses on the structured reconstruction and layered design of questions is particularly worth paying attention to.

The Question Restructuring Pyramid Model

On the one hand, we can use a question restructuring pyramid model to divide questions into three layers:

A. Fact Layer (data verification questions)

B. Pattern Layer (correlation analysis questions)

C. Meta-Question Layer (paradigm-disrupting questions).

Starting from the fact layer, we must, like scientists, use verifiable data or evidence to ensure that the description of the question is accurate; in the pattern layer, we must consider how these facts form connections and whether they can be abstracted into a certain pattern; moving up is the meta-question, which often challenges our original views or industry paradigms, just like AlphaGo did in redefining the “game path” of Go, directly changing human perceptions of chess skills and the limits of machine learning.

On the other hand, Prompt Engineering is also very crucial.

The Chain-of-Thought（COT） technique allows AI to explicitly present the intermediate thinking steps in the reasoning process, helping us understand how AI arrives at the final answer;

The counterfactual questioning framework uses a “hypothesis-deduction” approach to have the model re-examine a question in different scenarios or constraints, thereby inspiring more potential answers;

Dynamic boundary setting can prevent the model from expanding infinitely in open-ended conversations or prematurely “locking in” answers;

Cross-border question matrix construction, biomimicry perspectives, quantum thinking, and chaos engineering all provide us with more flexible and multidimensional ways of thinking.

Netflix’s attempt at “active fault injection” essentially requires us to continuously subject the system to various pressures through questions and hypotheses before the system collapses, thereby maintaining resilience in a truly high-load environment.

III. Prompt: Launching Human-Machine Collaboration (Trigger Mechanism)

In the previous stage, we restructured the question at the thinking level. Now, we need to effectively convey these ideas to AI and have AI return answers that are valuable to us. This is what we call a Prompt.

When we input the restructured question into AI, the starting point of the dialogue is no longer a simple instruction transfer, but a negotiation of a cognitive agreement. From Socrates to DeepSeek R1, the essence of questioning has never changed — but today, the value of each question is being repriced by AI.

Why is it that with the same model, some people can create software, while others use it for a few chats and think the jokes are unfunny and give up on it? Prompt engineering is an ongoing process of exploration and progress, a profession, a discipline.

• Standard Answer: Meticulous RPBE-E Prompting Method

The traditional prompt engineering framework of Role-Purpose-Background-Example used to be like an assembly line of the industrial age, ensuring the standardized production of answers. For example:

“As a senior product manager, design a fitness APP that caters to the needs of both fitness novices and fitness coaches, referencing the interaction logic of Keep…”

Indeed, it’s cumbersome and tiring to write, but it’s undeniable that in most cases, it’s quite effective. Then, there are many times when it will give you a very frustrating reality: Since I wrote it so clearly, why didn’t I just write it out myself?