Prompt Engineering: Learning to Talk to AI Is Lesson One

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# Basic role prompting
prompt = "You are an experienced Python developer, please explain what decorators are..."

# Specific role prompting
prompt = """
You are a Python backend engineer with 10 years of experience, who has worked at Google and Facebook.
Your expertise includes:
- High-performance web architecture design
- Distributed system optimization
- Code refactoring and performance tuning

Now please explain the concept of Python decorators to beginners, requiring:
1. Use life-like metaphors
2. Give 3 practical application scenarios
3. Include code examples and performance comparisons
"""

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# Poor prompt
"Write an article about dogs"

# Good prompt
"""
Please write an 800-word popular science article about Border Collies. Include:
1. Origin and historical background
2. Physical characteristics and personality traits
3. Training difficulty and care recommendations
4. Suitable family types

Writing style: Scientific but accessible
Target audience: Potential dog owners considering getting a dog
"""

# Ultra-clear prompt
"""
Role: Canine behavior training expert
Reader level: People with basic dog knowledge
Task: Compare Border Collies and Poodles regarding care requirements
Structure:
- Introduction: Why choose these two breeds
- Middle section:
  * Living space requirements comparison
  * Exercise requirements comparison
  * Intelligence training methods comparison
  * Common behavior problems and solutions
- Conclusion: Summary, recommendations for different family types
Word count: Around 1000 words
Tone: Professional but friendly, avoid overly academic
"""

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prompt = """
Please analyze the performance issues in the following code:

```python
def process_data(data):
    result = []
    for item in data:
        if item > 10:
            result.append(item * 2)
    return result

1

# Optimized code

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### Advanced Reasoning Techniques

#### Chain-of-Thought (CoT)

CoT technology was proposed by Google at NeurIPS 2022, revolutionizing LLM reasoning capabilities.

```python
# Traditional prompt
"Calculate: 234 + 567 + 891 = ?"

# Chain-of-thought prompt
"""
Let's calculate this addition step by step:

Step 1: 234 + 567
- 200 + 500 = 700
- 30 + 60 = 90  
- 4 + 7 = 11
- Total: 700 + 90 + 11 = 801

Step 2: 801 + 891
- 800 + 800 = 1600
- 1 + 900 = 901
- Total: 1600 + 901 = 2501

So: 234 + 567 + 891 = 2501
"""

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# Few-shot prompt
"""
Please complete the following sentiment analysis task, determining whether the sentence sentiment is positive, negative, or neutral.

Examples:
- This movie was so amazing, I watched it three times. Positive
- The phone battery life is terrible, it dies in just half a day. Negative  
- Today's weather is nice, sunny and bright. Neutral

Now analyze:
- This software interface is clean and the features are very practical.
"""

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# Tree-of-thought prompt example
"""
You need to solve a math problem, but there might be multiple solution approaches.
For each approach, please evaluate its feasibility and choose the optimal path.

Problem: How to find all prime numbers between 1 and 100?

Solution Path 1: Trial Division
- Steps: Check if each number can be divided by numbers between 2 and sqrt(n)
- Pros: Simple and easy to understand
- Cons: Inefficient for large numbers

Solution Path 2: Sieve of Eratosthenes
- Steps: Create a boolean array and progressively mark non-primes
- Pros: Time complexity O(n log log n), very efficient
- Cons: Needs extra space to store the sieve

Solution Path 3: Probabilistic algorithms
- Steps: Use probabilistic testing methods
- Pros: More effective for extremely large numbers
- Cons: Might have false positives

Please choose the optimal method and provide specific implementation.
"""

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# Self-consistency prompt
"""
Please solve the following math problem: A cage contains chickens and rabbits totaling 35 animals, with 94 legs total. How many chickens and rabbits are there?

Please provide at least 3 different solution methods and verify the consistency of results.

Method 1:
Equation method...
Result: 23 chickens, 12 rabbits

Method 2:
Assumption method...
Result: 23 chickens, 12 rabbits

Method 3:
Enumeration method...  
Result: 23 chickens, 12 rabbits

Comprehensive analysis: All three methods yield the same result, confirming the answer is correct.
"""

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# Temperature tuning examples
creative_prompt = """
Imagine a future city scene, including the following elements:
- Air transportation system
- Ecological buildings
- AI service assistants

Describe the daily life in this city using imaginative language.
temperature=0.8
"""

technical_prompt = """
Analyze 10 main challenges future cities might face, each including:
1. Problem description
2. Impact assessment
3. Possible solutions

Requirement: Make reasonable predictions based on existing technology development trends.
temperature=0.3
"""

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# Comparative experiment
prompt_A = """
As a data scientist, please explain the concept of overfitting in machine learning.
Include definition, causes, detection methods, and solutions.
"""

prompt_B = """
You need to explain overfitting to non-technical people. Please use:
1. Life-like metaphors (like wearing shoes)
2. Specific code examples
3. Intuitive chart descriptions
4. Avoid technical jargon, use simple language
"""

# Compare both prompts for:
# - Answer accuracy
# - Readability
# - User satisfaction

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basic_prompt = """
Please analyze the following Python code:

```python
def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n-1)

print(factorial(5))

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### Step 3: Iterative Optimization

Let's optimize this prompt step by step:

```python
# First iteration: Add specific requirements
improved_prompt = """
Please analyze the following Python code, focusing on these aspects:

1. **Code Readability**
   - Variable naming clarity
   - Code structure clarity
   - Sufficient comments

2. **Performance Analysis**
   - Time complexity analysis
   - Space complexity analysis
   - Potential performance bottlenecks

3. **Code Quality**
   - Compliance with Python best practices
   - Edge case handling
   - Error handling completeness

4. **Improvement Suggestions**
   - Specific improvement measures
   - Optimized code examples

Code:
```python
def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n-1)

print(factorial(5))

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```python
# Second iteration: Add format requirements
final_prompt = """
Please analyze the following Python code in the following format:

## Code Analysis Report

### 1. Readability Analysis
- Variable naming assessment:
  - Current status...
  - Improvement suggestions...
- Code structure assessment:
  - Current status...
  - Improvement suggestions...
- Comment quality assessment:
  - Current status...
  - Improvement suggestions...

### 2. Performance Analysis
- Time complexity:
  - Analysis: Recursive calls lead to O(n) time complexity
  - Assessment: Acceptable for small inputs, possible stack overflow for large inputs
- Space complexity:
  - Analysis: O(n) due to recursive call stack
  - Assessment: Needs additional stack space for storing call information
- Performance optimization suggestions:
  1. Use iteration instead of recursion to avoid stack overflow
  2. Add input validation to handle negative inputs
  3. Consider memoization to avoid redundant calculations

### 3. Code Quality Assessment
- PEP 8 compliance: Basically compliant, improvements suggested
- Edge case handling: Missing input validation
- Error handling: Lack of exception handling mechanism

### 4. Improved Code
```python
def factorial(n):
    """
    Calculate the factorial of n
    
    Args:
        n: Non-negative integer
        
    Returns:
        Factorial result of n
        
    Raises:
        ValueError: If n is negative
    """
    if not isinstance(n, int) or n < 0:
        raise ValueError("Input must be a non-negative integer")
    
    result = 1
    for i in range(1, n + 1):
        result *= i
    return result

# Test code
if __name__ == "__main__":
    try:
        print(factorial(5))
        print(factorial(0))  # Should output 1
        print(factorial(3))  # Should output 6
        # print(factorial(-1))  # Should raise exception
    except ValueError as e:
        print(f"Error: {e}")

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def factorial(n):
    if n == 0:
        return 1
    else:
        return n * factorial(n-1)

print(factorial(5))

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### Step 4: Testing and Validation

Run different versions of prompts and compare output quality:

1. **Basic version**: Simple analysis
2. **Improved version**: Structured analysis, but not detailed enough
3. **Final version**: Complete detailed analysis report

Through this process, you can see the significant effect of prompt optimization.

## Why Prompt Engineering Is Not Enough?

Although prompt engineering is an important foundation, it has several fundamental limitations:

### 1. No Memory Capability

Each conversation is independent; AI cannot remember previous interactions:

```python
# First round
user: "I am a Python developer"
assistant: "Great, Python is a powerful programming language..."

# Second round (AI doesn't remember "I am a Python developer")
user: "What Python frameworks should I learn?"
assistant: "As a Python developer, you could learn..."

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# AI doesn't know about latest version features
user: "What are the new features in Python 3.13?"
assistant: "Python 3.13 was released in 2024, including these new features..."  # May give wrong info

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user: "Based on our company's API documentation, how to implement user authentication?"
assistant: "Sorry, I cannot access your company's private documents..."  # Cannot provide specific help

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user: "Please help me run data analysis script and generate charts"
assistant: "I cannot directly run your code, but I can provide code suggestions..."  # Cannot actually execute

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user: "Help me analyze sales data"
assistant: "I can help you analyze, but you need to provide the data..."  # Needs user to provide info

user: "Here is the sales data file..."  # User has to repeat information
assistant: "Now I received the data, let me analyze..."  # AI doesn't remember previous request