Prompt Engineering

Master the art of crafting effective instructions to get the best responses from AI models

🎯 What is Prompt Engineering?

Definition: The art and science of crafting effective instructions to get the best responses from AI models

Simple Analogy: Like being a skilled manager who knows exactly how to ask questions to get the best work from their team. The way you ask determines the quality of the answer.

Key Insight: AI models are incredibly capable, but they need clear, well-structured guidance to produce their best work.

Core Principles

1. Be Specific and Clear

• Vague: "Write about AI"
• Specific: "Write a 300-word explanation of machine learning for high school students, using simple analogies and real-world examples"

2. Provide Sufficient Context

Role: You are an expert data scientist
Task: Explain the concept of overfitting
Audience: Junior developers
Format: Bullet points with examples
Tone: Professional but accessible

3. Use Examples (Few-Shot Learning)

Classify the sentiment of these reviews:
Example 1: "This product is amazing!" → Positive
Example 2: "Terrible quality, waste of money" → Negative
Example 3: "It's decent, could be better" → Neutral
Now classify: "Best purchase I've made this year!" → ?

4. Structure Your Request Logically

Context: [Background information]
Task: [What you want the AI to do]
Format: [How you want the output structured]
Examples: [Sample inputs and outputs]
Instructions: [Specific guidelines and constraints]

Advanced Prompt Engineering Techniques

Chain of Thought (CoT) Prompting

Encourage the AI to show its reasoning process:

Solve this step by step:
Problem: A store sells apples for $2 per pound. If I buy 3.5 pounds and pay with a $10 bill, how much change will I receive?

Think through this step by step:
1. First, calculate the cost of apples
2. Then, determine the change
3. Finally, verify your answer

AI Response:

Let me solve this step by step:

1. Calculate the cost of apples:
   - Price per pound: $2
   - Amount bought: 3.5 pounds
   - Total cost: 3.5 × $2 = $7

2. Determine the change:
   - Amount paid: $10
   - Total cost: $7
   - Change: $10 - $7 = $3

3. Verification:
   - Cost + Change should equal amount paid
   - $7 + $3 = $10 ✓

Answer: You will receive $3 in change.

Few-Shot Learning with Complex Examples

# Example: Teaching the AI to write Python docstrings

prompt = """
Write comprehensive docstrings for Python functions following Google style:

Example 1:
```python
def calculate_area(length, width):
    \"\"\"Calculate the area of a rectangle.
    
    Args:
        length (float): The length of the rectangle in meters.
        width (float): The width of the rectangle in meters.
    
    Returns:
        float: The area of the rectangle in square meters.
    
    Raises:
        ValueError: If length or width is negative.
    
    Example:
        >>> calculate_area(5.0, 3.0)
        15.0
    \"\"\"
    if length < 0 or width < 0:
        raise ValueError("Length and width must be non-negative")
    return length * width

Now write a docstring for this function:

def find_max_value(numbers):
    # Your docstring here
    return max(numbers) if numbers else None

"""

### Role-Based Prompting

Assign specific roles to get domain-specific responses:

```text
System: You are a senior software architect with 15 years of experience in designing scalable web applications.

User: How would you design a system to handle 1 million concurrent users?