Seeing the recent buzz about AI usage on my feed really got me thinking.
As someone who has written code, built algorithms, and is now managing physical products at Cymkube, I’ve noticed that most people still use AI with a Web 1.0 "Search" mindset. They ask a question and expect a standard answer.
To me, that’s like driving a Ferrari to the grocery store—you’re only utilizing 1% of its performance.
Back when we were building crawlers or optimizing SEO algorithms, the core challenge wasn't just "fetching" data, but "cleaning" and "structuring" it. The same logic applies to AI collaboration today. If you only do the first layer of questioning, you’ll only ever get "Wikipedia-style" general knowledge. To generate commercially viable Insights, you need a system architecture mindset.
This perfectly validates the concept of "Iterative Prompting." I’ve broken this process down into three layers—this is the standard SOP I use every day to "Debug the Business":
L1: Broad Scan (Information Acquisition) This is where most people stop—treating AI like Google. In this phase, I only look for the "Big Picture." It’s like taking over a new project; I need to read the documentation first. The goal isn't precision yet, but establishing a framework.
Tech Perspective: It’s like sending a GET request. Just fetch the Raw Data first.
L2: Deep Dive (Keyword Interrogation) This is the watershed moment. Pros look at the L1 answers and extract the "Industry Jargon" or "Key Variables." Don’t understand a specific technical term? That term is a gold mine. I take that keyword and use it for a precision strike: "What is the operating principle behind this concept you just mentioned? Why is it critical?"
Tech Perspective: This is Data Parsing. Extracting the real Features from the noise. This is usually where the key to the solution lies.
L3: Loop Verification (Iterative Verification) Having deep info isn't enough because AI can confidently hallucinate. I ask the AI to roleplay as the opposition or use the L2 conclusions to stress-test the L1 information: "If your L2 analysis is true, does that mean your initial L1 logic was flawed?" Through this cross-examination, I force the AI to converge its logic and produce actionable advice.
Tech Perspective: This is a Unit Test. Ensuring the output logic is Self-consistent and bug-free before deployment.
■ The Takeaway
AI isn't just an "Answer Machine"; it is a trainable "Thinking Partner."
True Leverage comes from your willingness to design this "Acquire -> Interrogate -> Verify" loop. For entrepreneurs, filtering high-purity Insights from a sea of information is a core competency.
Don't want to just use AI as a search engine? This 3-Layer Architecture is worth a try.
As someone who has written code, built algorithms, and is now managing physical products at Cymkube, I’ve noticed that most people still use AI with a Web 1.0 "Search" mindset. They ask a question and expect a standard answer.
To me, that’s like driving a Ferrari to the grocery store—you’re only utilizing 1% of its performance.
Back when we were building crawlers or optimizing SEO algorithms, the core challenge wasn't just "fetching" data, but "cleaning" and "structuring" it. The same logic applies to AI collaboration today. If you only do the first layer of questioning, you’ll only ever get "Wikipedia-style" general knowledge. To generate commercially viable Insights, you need a system architecture mindset.
This perfectly validates the concept of "Iterative Prompting." I’ve broken this process down into three layers—this is the standard SOP I use every day to "Debug the Business":
L1: Broad Scan (Information Acquisition) This is where most people stop—treating AI like Google. In this phase, I only look for the "Big Picture." It’s like taking over a new project; I need to read the documentation first. The goal isn't precision yet, but establishing a framework.
Tech Perspective: It’s like sending a GET request. Just fetch the Raw Data first.
L2: Deep Dive (Keyword Interrogation) This is the watershed moment. Pros look at the L1 answers and extract the "Industry Jargon" or "Key Variables." Don’t understand a specific technical term? That term is a gold mine. I take that keyword and use it for a precision strike: "What is the operating principle behind this concept you just mentioned? Why is it critical?"
Tech Perspective: This is Data Parsing. Extracting the real Features from the noise. This is usually where the key to the solution lies.
L3: Loop Verification (Iterative Verification) Having deep info isn't enough because AI can confidently hallucinate. I ask the AI to roleplay as the opposition or use the L2 conclusions to stress-test the L1 information: "If your L2 analysis is true, does that mean your initial L1 logic was flawed?" Through this cross-examination, I force the AI to converge its logic and produce actionable advice.
Tech Perspective: This is a Unit Test. Ensuring the output logic is Self-consistent and bug-free before deployment.
■ The Takeaway
AI isn't just an "Answer Machine"; it is a trainable "Thinking Partner."
True Leverage comes from your willingness to design this "Acquire -> Interrogate -> Verify" loop. For entrepreneurs, filtering high-purity Insights from a sea of information is a core competency.
Don't want to just use AI as a search engine? This 3-Layer Architecture is worth a try.
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