Prompt Engineering

Techniques and best practices for effective LLM interaction

Core Principles

Clarity and Specificity

• Be explicit: Clearly state what you want
• Provide context: Give relevant background information
• Specify format: Define expected output structure
• Set constraints: Limit response scope and length

Role Definition

You are an expert data scientist with 10 years of experience in machine learning.
Your task is to explain complex ML concepts in simple terms for a business audience.

Context Management

• Relevant information: Include necessary background
• Avoid redundancy: Don’t repeat obvious information
• Progressive disclosure: Build context gradually

Prompt Patterns

Chain-of-Thought (CoT)

Let's approach this step by step:

1. First, let's understand the problem...
2. Next, let's identify the key components...
3. Finally, let's solve it...

Problem: Calculate the area of a circle with radius 5.

Few-Shot Learning

Example 1:
Input: "What is 2+2?"
Output: "2+2 equals 4."

Example 2:
Input: "What is 5+3?"
Output: "5+3 equals 8."

Now, what is 7+4?

Zero-Shot Learning

Given a text, classify it as positive, negative, or neutral sentiment.

Text: "I love this product! It's amazing."

Advanced Techniques

Temperature Control

• Low temperature (0-0.3): Deterministic, focused responses
• Medium temperature (0.3-0.7): Balanced creativity and consistency
• High temperature (0.7-1.0): Creative, diverse responses

System vs User Messages

System: You are a helpful coding assistant. Always provide code examples in Python.

User: How do I create a function that calculates the factorial of a number?

Function Calling

{
  "name": "get_weather",
  "description": "Get current weather for a location",
  "parameters": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "City name"
      }
    },
    "required": ["location"]
  }
}

Domain-Specific Prompts

Code Generation

Write a Python function that:
- Takes a list of numbers as input
- Returns the sum of all even numbers
- Includes error handling for invalid inputs
- Has clear documentation

Data Analysis

Analyze this dataset and provide:
1. Summary statistics
2. Key insights
3. Potential issues or anomalies
4. Recommendations for further analysis

Dataset: [data here]

Content Creation

Create a blog post about machine learning that:
- Targets beginners
- Uses simple language
- Includes practical examples
- Is 500-800 words
- Has a clear structure with headings

Evaluation and Testing

Prompt Testing Framework

1. Define success criteria: What constitutes a good response?
2. Create test cases: Diverse scenarios and edge cases
3. Measure performance: Accuracy, relevance, completeness
4. Iterate and improve: Refine based on results

A/B Testing

Version A: "Explain machine learning"
Version B: "Explain machine learning in simple terms for a business audience"

Compare responses for clarity, relevance, and usefulness.

Human Evaluation

• Relevance: Does the response address the query?
• Accuracy: Is the information correct?
• Completeness: Does it cover all aspects?
• Clarity: Is it easy to understand?

Common Pitfalls

What to Avoid

• Vague prompts: Unclear or ambiguous instructions
• Overly complex: Too many requirements at once
• Missing context: Insufficient background information
• Inconsistent formatting: Unclear output expectations

Better Approaches

• Iterative refinement: Start simple, add complexity
• Clear structure: Use numbered lists and bullet points
• Specific examples: Provide concrete instances
• Feedback loops: Test and improve continuously

Tools and Frameworks

Prompt Management

• LangChain: Prompt templates and chains
• Promptfoo: Prompt testing and evaluation
• Weights & Biases: Experiment tracking

Evaluation Metrics

• BLEU Score: Text similarity
• ROUGE Score: Text summarization quality
• Custom metrics: Domain-specific evaluation

Version Control

# prompt.yaml
version: "1.0"
description: "Code generation prompt"
template: |
  You are an expert Python developer.
  Write clean, well-documented code for: 
  
  Requirements:
  - Follow PEP 8 style guide
  - Include type hints
  - Add comprehensive docstrings
  - Handle edge cases

Best Practices

Documentation

• Prompt templates: Reusable components
• Version history: Track changes and improvements
• Usage guidelines: Clear instructions for team members

Security Considerations

• Input validation: Sanitize user inputs
• Output filtering: Validate LLM responses
• Rate limiting: Prevent abuse
• Content policies: Ensure appropriate responses

Performance Optimization

• Token efficiency: Minimize unnecessary tokens
• Caching: Store common responses
• Batching: Process multiple requests together

Real-World Applications

Customer Support

You are a customer support agent for a tech company.
A customer is experiencing issues with our product.
Provide helpful, empathetic assistance while gathering necessary information.

Content Moderation

Review this content for:
- Inappropriate language
- Hate speech
- Misinformation
- Copyright violations

Content: [text to review]

Code Review

Review this code for:
- Security vulnerabilities
- Performance issues
- Code quality
- Best practices

Code: [code block]

Key Principle: Effective prompt engineering is iterative and context-dependent. Start with clear, specific prompts and refine based on results and feedback.