To share with you today.

Core point: A 6-page "instruction book" is worth tens of millions

Currently, when searching for questions in Perplexity, writing code on Replit, or contacting customer service of these well-known AI companies, the answer you are not a human, but an AI robot.

But why are these AI robots so smart and so professional?

The key to the answer is not how advanced the AI brain they use is, but that they have a 6-page "workbook".

This manual tells AI how to think, how to answer, and even what situations to say "I don't know".

In the AI ​​circle, we call this manual "prompt word".

This is like when you go to a five-star hotel. The reason why the waiter serves so thoughtfully is not because they are born to understand service, but because the hotel has given them a detailed service manual: what kind of guests should say, what problems should be handled, and even the angle of smiles has regulations.

Today, let's see how they introduced how these "AI Workbooks" worth tens of millions were written.

After learning these methods, you can also make AI assistants such as ChatGPT and Claude smarter and understand you better.

The first layer of foundation: Give AI a clear identity awareness

Tell AI first, "Who are you"?

Imagine this scenario: You are the boss of a company, and a new employee comes, and you let him start working without saying anything.

What will happen? This employee will definitely be at a loss: Am I here to do sales or technology? How many permissions do I have? What decisions can I make?

The same is true for AI.

If you ask it directly, "help me handle customer complaints", AI is as confused as the new employee: What capacity should I deal with it? Is it the role of ordinary customer service or the role of customer service supervisor? To what extent is the task completed?

So, the tip words from top companies will start like this:

General writing: "Help me handle customer complaints"

Professional writing: "You are a senior customer service supervisor of a technology company with 10 years of customer service experience. Your responsibilities are:

• Listen carefully to customers' problems and dissatisfaction
• Analyze the root cause of the problem
• Provide practical solutions
• Elevate complex problems to the technical team when necessary
• Ensure the conversation before the customer is satisfied."

Did you see the difference? The second way of writing allows AI to clearly know its identity, experience background, and specific responsibilities, just like sending a detailed job description to a new employee.

Decompose complex tasks into simple steps

Humans instinctively break down steps when dealing with complex tasks, but AI needs you to tell it clearly.

This is like teaching children to do math problems. You can't say "solve this equation", but you have to say "the first step is to move the terms, the second step is to merge similar terms, and the third step is to solve".

Give a practical example:

Suppose you want AI to help you analyze whether an article is worth investing in.

Bad prompt words: "Analyze this article and tell me whether I should invest."

Excellent prompt words: "Please follow the steps below to analyze this investment article: Step 1: Identify the company name, industry type, and investment amount mentioned in the article Step 2: Find the market opportunities and risk points mentioned in the article Step 3: Analyze the company's competitive advantages and disadvantages Step 4: Assess the potential returns and risk levels of investment Step 5: Based on the above analysis, give clear investment advice (invest/not invest/need more information)"

In this way, AI will complete the analysis work in an orderly manner like a professional investment analyst.

Specify clear output formats

This concept may sound technical, but it is actually easy to understand. Just like when you go to a restaurant to order food, you want the waiter to answer "There is braised pork, 35 yuan per serving, it takes about 15 minutes", rather than "Some, it's delicious and not expensive".

In the world of AI, we can use some special marks to require AI to answer in a fixed format.

For example, use XML tags (just like adding a title to a text):

Example: "Please answer in the following format: <Analysis results> Your specific analysis content</Analysis results> <Investment advice>Investment/No investment/Require more information</Investment advice> <Risk level>Low risk/Medium risk/High risk</Risk level>"

In this way, the AI's answers will be very regular and it is easier for you to extract useful information.

Just like bank business documents have fixed formats, it is easy to process and archive.

The second level of advancement: Let AI learn to "learn from one example"

Teaching AI with specific examples (this is the most important skill!)

Do you have such an experience: When someone explains a concept to you, you are still confused after talking for a long time, but as soon as they give an example, you will understand immediately? The same is true for AI.

Let me illustrate it with a real case.

Suppose you want the AI ​​to recognize the "N+1 query problem" in the code (this is a programming error that will slow the website).

Method 1: Plain text explanation "N+1 query problem refers to the situation where a database query is executed in a loop, resulting in performance problems."

AI was still confused after reading this explanation, just like you first heard of this concept.

Method 2: Give a specific example. "The following is an example of an N+1 query problem:

// Incorrect writing (N+1 query problem)
users = Get all users()  // This is the first query
for user in users:
    orders = Get user order (user.id)  // If there are 100 users, here will be queried 100 times
  print(user.name, orders)

This writing method will cause the database to be queried 101 times (1+100), seriously affecting performance. When you see similar code patterns, beware of N+1 query problems. "

Now AI understands it! It not only knows what an N+1 query problem is, but also what this problem looks like. Just like a doctor learns to diagnose a fracture by taking an X-ray.

Meta Tips: Let AI be your prompt word consultant

This is the most magical skill and a secret weapon that top companies are using: let AI help you write better prompt words.

It's like asking a writing expert to help you modify your resume. You wrote a resume, but felt it was not good enough, so you found an HR expert who helped you improve your wording, adjust your structure, and highlight your highlights. Meta prompts are such a concept.

Specific operation method:

Step 1: Write a basic version of the prompt word, for example: "Help me write an article about environmental protection."

Step 2: Ask AI to play the prompt word expert: "You are now an AI prompt word engineer with 10 years of experience, specifically helping people optimize prompt words. I have a prompt word that I want to ask you to improve it to make it clearer and more effective.

My original prompt word is: 'Help me write an article about environmental protection'

Please help me analyze the problem with this prompt word and give the improved version."

Step 3: Harvest the improved version. AI will tell you that the original prompt word is too vague, and then give you an improved version like this:

"Please write a 1500-word article as an environmental expert, with the theme of "How individuals practice environmental protection in daily life". Article requirements:

• Target readers: Ordinary people who have a preliminary interest in environmental protection
• Article style: practical, easy to understand, and convincing
• Content structure: problem background + specific methods + actual effects
• Content information: at least 5 specific and feasible environmental protection tips
• Explain each method with life-like examples."

Did you see the difference? The improved prompt word is like changing from "making a dish" to a detailed recipe.

Set "Help Button" for AI

The concept is very important, but it is often overlooked.

AI has one feature: it "wants to help you". Just like an overly enthusiastic employee, even if he is not sure about the answer, he has to bite the bullet and give you a reply.

For example, You ask AI: "Who is the Nobel Prize in Physics awarded to in 2024?" If AI's training data is only until 2023, it does not know the answer, but it may guess or make up an answer that sounds reasonable.

This is very dangerous, just like when asking for directions, people who don’t know the way are pointing in a blind way to appear useful.

Solution: Give AI an option to "not know"

"If you don't have a definite answer to the question, or need the latest information to answer accurately, please say directly, 'I need more information to answer this question accurately', and don't guess or make up the answer."

In this way, AI will have the courage to say "I don't know", and instead make it more reliable.

Third-level enterprise-level application: three-layer prompt word architecture system

Why do you need layered management?

Imagine you run a chain restaurant with branches of three different styles: McDonald's, KFC, and Pizza Hut. How will you manage it?

If you write a complete set of management manuals for each store, the workload will be huge, and a lot of basic content is repeated (such as food safety standards and customer service etiquette).

The clever approach is to establish a three-tier management system:

• The first level: the unified group standards (applicable to all branches)
• The second level: the brand characteristic rules (McDonald's rules vs. the rules of KFC)
• The third level: the specific store instructions (today's special menu)

The same is true for AI prompt words.

The first layer: system prompt words (company basic law)

This layer defines the most basic and common rules, just like the basic law of the company. These rules remain unchanged regardless of the customer you serve.

Example: "As a customer service AI, you must always follow the following principles:

• Keep a polite and professional tone
• Prefer customer satisfaction
• Protect customer privacy information
• When encountering problems that cannot be solved, submit them to manual customer service in a timely manner
• Never promise services beyond the scope of the company's policies."

This is like the basic service standards in the hotel industry. Whether it is Hilton or Marriott, these basic principles are universal.

The second layer: developer prompt words (customer customization rules)

This layer adds personalized needs of specific customers. For example, the rules for providing services to Netflix and providing services to banks are definitely different.

The rules for serving Netflix may be: "When a customer asks for movie recommendations:

• Priority to recommend Netflix original content
• Personal recommendations based on customer history
• Avoid recommending content that will be removed from the shelves
• For children's accounts, strictly enforce age rating restrictions. "

The rules for serving banks may be: "When customers ask about financial services:

• Account information is provided only after strict verification of the customer's identity
• Second confirmation must be performed when large-scale transactions are involved
• Never provide password reset services through chat
• Remind customers to contact the bank immediately when they find suspicious transactions."

This is the content entered directly by the user, such as "recommending me with a few science fiction movies" or "querying my account balance".

Advantages of three-layer architecture:

1. High efficiency: No need to rewrite all rules for each customer
2. Easy to maintain: All customers can benefit when modifying basic rules
3. Personalization: Every customer can have their own special services
4. Low cost: Avoid repeated development

Like the modular idea in software development, breaking complex systems into reusable components.

Differences in the "personality" of different AI models

In real work, you will find that every employee has different personalities and expertise. Some people are good at communication, while others are technically capable but need clear guidance. The same is true for AI models.

Claude: an understanding "communication expert"

Claude is like a colleague with high emotional intelligence in the office. You talk to him easily, and he can always understand what you mean.

Features:

• Strong understanding of fuzzy instructions
• Replies are more humane
• Good safety awareness is not easy to be "taken off the line"

Suitable work scenarios:

• Writing creative copywriting
• Customer communication
• Content creation
• Tasks that require empathy

Practical application examples: When you say "Write me an apology letter because the product is delayed", Claude can understand that you need not just a letter, but a communication text that is both sincere and able to maintain customer relationships.

Llama: a rigorous "technical expert"

Llama is like a technical expert in the company, with strong abilities, but you need to make the needs clear, otherwise it will execute it literally.

Features:

• High logic
• Fit for structured tasks
• Require detailed and clear guidance
• Excellent performance in the technical field

Suitable work scenarios:

• Code writing and review
• Data analysis
• Logical reasoning
• Structured content generation

Practical application example: When you need to let Llama write code, you cannot simply say "write a login function", but explain in detail: "Write a user login function using the Python Flask framework, including:

1. Username and password verification
2. Error message prompt
3. After logging in successfully, jump to the homepage
4. Passwords need to be encrypted
5. Add restrictions to prevent brute-force cracking."

Cost optimization strategy: large model optimization, small model execution

This is a smart strategy commonly used by top companies, just like asking top designers to design plans and then letting ordinary workers construct according to the drawings.

Specific operation process:

Step 1: Optimize prompt words with powerful but expensive AI (such as GPT-4). I have a basic customer service prompt word. Please help me optimize it into a professional-level version, which requires being able to handle various complex customer situations..."

Step 2: Obtain the optimized perfect prompt word. After optimization, you get a very detailed and thoughtful prompt word.

Step 3: Use cheap AI models to perform daily work. Give the optimized prompt words to the cheap AI models (such as GPT-3.5) and let it work according to this "perfect instruction manual".

Cost comparison:

• Optimization with GPT-4: 100 calls, cost about $10
• Execution with GPT-3.5: 10,000 calls, cost about $20
• Total cost: $30

If you use GPT-4: 10,000 calls throughout the process, costs about $1,000

This strategy is particularly suitable for voice AI applications because the response speed is very important when users talk to AI. If you slow down for a second, users will feel that "this AI is not smart enough."

Practical skills: Methods that can be used today

Tip 1: Debugging using AI's "thinking process"

Now, some AI tools (such as Gemini, Claude) will display their "thinking process", just like letting you see the draft paper when students are doing math problems.

How to use:

1. Give AI a task
2. Observe its thinking process
3. Find out where it "thinks wrongly."

Practical example: You ask AI to analyze whether a company is worth investing in. Its thinking process may be: "Users require an analysis of investment value → I need to look at financial data → But the user only gave the company name → I searched the company → found some news → Based on news analysis..."

By observing this process, you found that AI lacks sufficient financial data, so you improved the prompt word: "Please analyze the investment value of XX Company. If there is a lack of key financial data, please clearly list which data is needed to make an accurate analysis. "

Tip 2: Establish your "case library"

Just like a doctor will record difficult cases, you must also record AI performance poorly.

Specific operation:

1. Prepare a document or notebook
2. Every time the AI ​​answers are not satisfactory, record them:

• Your original prompt word
• AI's answer
• Your expected answer
• Where is the problem
• Summary of improvement
• Update your prompt word template

Example:

Problem record: 
Original tip: "Write a product introduction for me"
AI answer: I wrote a very general template
Expected answer: Personalized introduction to our product characteristics
Problem analysis: The prompt word is too vague and no product information is provided
Improvement plan: 
"Please write a product introduction for our smartwatch. Product features: 
- 7 days of battery life
- 50 meters of waterproof
- Supports heart rate monitoring
- Price 999 yuan
- Target users: 25-40-year-old sports enthusiast

Tip 3: General Meta Prompt Template

This is a universal template you can use immediately:

You are an experienced AI Prompt Word Optimization Expert. I have a prompt word that you need help improve. 

My goal is: [Describe the goals you want to achieve in detail]
Target users: [Describe the people who use AI to output content]
Application scenario: [Describe the situation under which circumstances]
My original prompt words: 
[Paste your prompt words]
Please help me: 
1. Analysis of the problems with the original prompt words
2. Provide an improved version
3. Explain why this is improved
4. Give suggestions for use
- Clearer and clearer
- Reduce ambiguity
- Improve output quality
- Easy to reuse

Example of use: Suppose you want AI to help you write a copy for your circle of friends to promote your coffee shop:

You are an experienced AI prompt word optimization expert. I have a prompt word that you need help improve. 

My goal is to let AI write a coffee shop circle copy that can attract customers
Target users: white-collar workers aged 25-35, who likes coffee culture
Applicable scenarios: Post to friends every day to promote coffee shop
My original prompt word: 
"Help me write a coffee shop circle copy"
 1. Analyze the problems with the original prompt words
2. Provide an improved version
3. Explain why this improvement
4. Give suggestions for use

AI will help you analyze the problem (too vague, lack of specific information, etc.), and then give an improved version.

Core points: The path to advance from a beginner to an expert

Newbie Stage: Master the Basic Principles

At this stage, you need to develop these good habits:

Give AI a clear role positioning: Don’t say “Write articles for me”, but say “You are a marketing expert with 10 years of experience, help me write a product promotion article for young people.” Just like giving the actor a clear role setting, he knows how to act.

Replace abstract descriptions with concrete examples: Rather than saying "write it vividly", it is better to give a specific example: "Write like this: 'The fragrance of coffee is like a warm hug, which instantly surrounds the entire room'". Examples are better than thousands of words.

Design escape mechanisms: Always tell AI: "If you are not sure of the answer, say 'I need more information', don't guess." This makes AI more honest and more reliable.

Advanced stage: Master the system method

Use meta-tips: Let AI help you improve the prompt words, just like asking the writing teacher to modify your composition. This is the fastest way to improve.

Establish a hierarchical architecture: Separate general rules from specific needs to improve reusability. Just like establishing a company system, there are basic laws and specific business rules.

Understanding the characteristics of different AI: Claude is suitable for creative work, while Llama is suitable for technical tasks. Choosing the right AI is like choosing the right employee, and it will be twice the result with half the effort.

Expert stage: Form your own methodology

Establish a case library: Record each time the AI ​​performs poorly, summarize the rules, and form your own best practices.

Cost optimization: Use a large language model to optimize prompt words, use small models to perform tasks, and find the best balance between quality and cost.

Continuous iteration: The prompt words are not one-time, and must be continuously optimized according to actual results, just like the product needs to be continuously iterated.

From "One-time task" to "long-term cooperation": Good tip words are investments that can be reused thousands of times once written. It’s like establishing a good workflow, investing in one place, and benefiting in the long run.

From "perfectionism" to "iteration optimization": Don't expect to write perfect prompt words the first time. First, write a basic version, and then continuously improve according to actual results.

Just like starting a business, MVP (the smallest viable product) comes first, and then it iterates quickly.

Remember: Writing a prompt word well is not a technical job, but a communication art.

It requires you to think from the perspective of AI: What information does it need to help you complete the task? Just like managing a new employee, clear communication and reasonable expectations are always the key to success.

When you master these methods, you will find that AI is no longer an elusive black box, but an obedient, efficient, and reliable assistant. It can help you write copy, analyze data, solve problems, and even create content that you unexpectedly want.

This is the power of prompt words: It is not just a text, but also a bridge of communication between you and AI, and the core skill of managing the AI ​​team.