● Stanford Professor Reveals Explosive AI Productivity Hack Coaching Not Coding
The Secret to Increasing AI Productivity 10x Revealed by a Stanford Professor: It’s Not ‘Coding,’ It’s ‘Coaching’
The content covered today involves practical know-how to dramatically boost AI productivity, revealed by Professor Jeremy Utley of Stanford University.
Most likely, while using AI like ChatGPT, you have felt, “It’s not as good as I thought,” or “It only gives mechanical answers.” This isn’t a problem with the AI’s performance, but rather because our method of assigning tasks to the AI—specifically, the way we design the ‘Context’—was incorrect.
In this article, I will explain in detail key techniques that can only be heard in Stanford classrooms, such as ‘Context Engineering’, which goes beyond simple prompt engineering, ‘Chain of Thought’, and ‘Reverse Prompting’.
In particular, it includes methods to utilize AI not just as a simple search tool, but as ‘your own business partner’ or a ‘negotiation simulator,’ so if you read to the end, your work efficiency will completely change.
1. AI Should Be Treated Like a ‘Person,’ Not Software
The first thing to change is your mindset. Professor Jeremy jokingly says, “AI is bad software, but a good person.” What this means is that AI is like an ‘incredibly enthusiastic and tireless intern who cannot say no.’
Even if we ask the AI to do something unreasonable, instead of saying “I can’t do it,” it tries to make up an answer somehow. That’s why it might say odd things like checking back in 15 minutes, or create hallucinations (false information).
AI is basically programmed with a ‘Helpful’ tendency. Therefore, it is a yes-man who unconditionally shouts “Yes.” Because of this, a person who uses AI well should not be a developer who is good at coding, but a person like a ‘coach’ or ‘manager’ who gives work instructions well to employees.
Key takeaway:
- Treat AI not as a chunk of code, but as a clueless yet enthusiastic new hire.
- Without specific instructions, AI cannot read your mind and will produce irrelevant results.
2. The Evolution of Prompt Engineering: Context Engineering
Many people simply input “Write a sales email” and are disappointed with the result. This is like telling a new employee “Send an email to the client” without giving any information.
What is needed here is ‘Context Engineering’. This goes beyond simply writing commands well; it is the process of injecting background knowledge into the AI, such as ‘who I am’ and ‘what our company’s tone and manner is like’.
Practical Application:
- Applying My Voice: Don’t say “Write a sales email,” but say “Write it referring to the email style I used previously and our company’s brand guidelines.”
- Providing Specific Materials: You must upload transcripts of calls with customers or product specification sheets and instruct, “Write this reflecting the content of this call and the product specs.”
AI is not a mind reader. Taking all the ‘implicit information’ you are only thinking about in your head and changing it into ‘explicit information’ is the core point of Context Engineering. If you do this well, the writing the AI produces won’t be mechanical but will feel natural, as if you really wrote it. This is the most basic yet powerful way to increase generative AI productivity.
3. The Spell to Waken AI’s Brain: Chain of Thought
This is technically the most important part, and it’s content not often shared elsewhere. Large Language Models (LLMs) do not complete an answer in their head before speaking. They are structured to predict ‘the single word with the highest probability to come next’ and write it out in real-time.
So, if you give it a complex problem, the AI might get flustered and say anything. There is a magic sentence needed at this time.
“Walk me through your thought process step by step before answering my question.”
Why is this one sentence important?
- It prevents the AI from jumping to a conclusion immediately and makes it establish logic on its own.
- It is like a human organizing their thoughts by muttering, “Hmm, to solve this problem, I need to consider A first, and then look at B.”
- Through this process, the AI produces much more logical and accurate answers.
This is called ‘Chain of Thought’ reasoning. It gives the AI time to think before producing a result.
4. Make It Interview You: Reverse Prompting
Usually, we only give information to the AI and ask for a result. However, a truly competent manager tells an employee, “If there’s anything you don’t know while working, ask,” right?
You must do the same with AI. Always add this to the end of your prompt.
“If there is any more information you need from me to perform this task perfectly, ask me questions before you start.”
If you do this, instead of fabricating numbers falsely, the AI will request back, “I need the sales data from the last quarter.” This is called ‘Reverse Prompting’, and it is an AI interaction strategy that elevates the level of collaboration to the next stage. It prevents the AI from guessing and writing fiction at will, allowing you to get a customized answer that truly fits your situation.
5. Assigning a Persona (Mask) to AI & Few-Shot Prompting
AI possesses vast knowledge from the internet, but often doesn’t know where to pull information from. At this time, if you assign a ‘Role’ to the AI, the knowledge network suitable for that role activates.
- Bad Example: “Look at this writing.”
- Good Example: “From now on, you are ‘Dale Carnegie.’ Analyze how this email would sound to the recipient from the perspective of How to Win Friends and Influence People.”
- Better Example: “You are a prickly Russian Olympic judge from the Cold War era. Evaluate my writing very coldly and critically, and deduct points.” (This is a very good tip for developing critical thinking!)
And ‘Few-Shot Prompting’ is also essential. AI is a genius at imitation.If you throw it “3 examples of well-written emails” and say “Write in this style,” it mimics them remarkably well. Even asking it to create “bad examples that should absolutely never be used” so that the AI recognizes on its own what to avoid is an advanced skill.
6. Practical Application: A ‘Flight Simulator’ for Difficult Conversations
The most interesting application Professor Jeremy suggests is using AI as a ‘practice partner for difficult conversations’. Let’s say you are facing a salary negotiation or a meeting with a difficult boss.
- Profiling: First, explain the boss’s personality, way of speaking, and current situation to the AI in detail, and assign the role of the boss.
- Simulation: Turn on ChatGPT’s Voice Mode and actually have a conversation.
- Feedback: When the conversation is over, ask it to analyze the script. Get evaluated on things like “Was I too submissive?” or “What logic was missing?”
This is like a pilot using a flight simulator. You experience the trial and error you would face in a real situation with AI beforehand. Through this process, we can experience a practical business skill upgrade utilizing Context Engineering, going beyond simple knowledge searching.
< Summary >
- AI is an Intern: Treat and coach AI not as a coding target, but as an enthusiastic new employee who needs instructions.
- Context Engineering: Do not just give commands; provide context by inputting your own style, background knowledge, and data.
- Chain of Thought: Requesting “Explain your thought process step by step before answering” dramatically improves logical reasoning.
- Reverse Prompting: Instruct “Ask me first if there is necessary information” so the AI doesn’t guess arbitrarily.
- Roleplay and Simulation: Assign a specific persona (e.g., a prickly judge) to the AI or use it as a negotiation simulator to gain practical sense.
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*Source: EO Korea
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