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Prompt Engineering

Master the art of communicating effectively with AI systems. Learn advanced prompting techniques to get better code, explanations, and solutions from AI tools.

🎯 Why Prompt Engineering Matters for Developers

Quality Results:

• Get more accurate and relevant code
• Reduce back-and-forth iterations
• Receive better explanations and documentation
• Avoid common AI-generated mistakes

Efficiency Gains:

• Save time on repetitive coding tasks
• Get faster debugging and problem-solving
• Learn new concepts more effectively
• Improve code review processes

Core Prompting Techniques

Clear and Specific Instructions

Be explicit about what you want the AI to do

❌ Poor Example:

Write a function

✅ Better Example:

Write a Python function that takes a list of integers and returns the sum of even numbers, with error handling for non-integer inputs

💡 Tips:

• Use action verbs
• Specify the programming language
• Include input/output requirements
• Mention error handling needs

Provide Context and Constraints

Give the AI background information and limitations

❌ Poor Example:

Optimize this code

✅ Better Example:

Optimize this Python function for a web API that handles 1000+ requests/second. Focus on memory efficiency over readability. Current response time is 200ms, target is under 50ms.

💡 Tips:

• Mention performance requirements
• Specify the environment/platform
• Include existing constraints
• State your priorities

Request Explanations

Ask AI to explain its reasoning and approach

❌ Poor Example:

"Fix this bug" or "Fix" without any additional details or context

✅ Better Example:

Fix this bug and explain: 1) What caused the issue, 2) Why your solution works, 3) How to prevent similar bugs in the future

💡 Tips:

• Ask for step-by-step explanations
• Request alternative approaches
• Ask about trade-offs
• Seek learning opportunities

Iterative Refinement

Build on previous responses to improve results

❌ Poor Example:

This isn't what I wanted with no additional details.

✅ Better Example:

The function works but is too slow for large datasets. Can you optimize it using a different algorithm? Also add type hints and docstrings.

💡 Tips:

• Be specific about what needs improvement
• Build on what works
• Add new requirements gradually
• Reference previous context

Proven Prompt Templates

Code Generation

Template:

Create a [LANGUAGE] [TYPE] that [FUNCTIONALITY].

Requirements:
- [SPECIFIC REQUIREMENT 1]
- [SPECIFIC REQUIREMENT 2]
- [PERFORMANCE/CONSTRAINT]

Please include:
- Error handling
- Type hints/annotations
- Comments explaining complex logic
- Example usage

Example Usage:

Create a Python class that manages a connection pool for database connections.

Requirements:
- Support for PostgreSQL and MySQL
- Maximum 10 concurrent connections
- Automatic retry on connection failure

Please include:
- Error handling
- Type hints/annotations
- Comments explaining complex logic
- Example usage

Code Review

Template:

Review this [LANGUAGE] code for:
- Potential bugs or edge cases
- Performance issues
- Security vulnerabilities
- Code style and best practices
- Maintainability concerns

Code:
[YOUR CODE HERE]

Please provide specific suggestions with examples.

Example Usage:

Review this Python code for:
- Potential bugs or edge cases
- Performance issues
- Security vulnerabilities
- Code style and best practices
- Maintainability concerns

Code:
def process_user_data(data):
    result = []
    for item in data:
        if item['age'] > 18:
            result.append(item['name'].upper())
    return result

Please provide specific suggestions with examples.

Debugging

Template:

I'm getting this error: [ERROR MESSAGE]

Context:
- [WHAT YOU WERE TRYING TO DO]
- [RELEVANT CODE SNIPPET]
- [ENVIRONMENT/SETUP INFO]

Please help me:
1. Understand what's causing this error
2. Provide a solution
3. Explain how to prevent it in the future

Example Usage:

I'm getting this error: AttributeError: 'NoneType' object has no attribute 'split'

Context:
- Trying to parse user input from a web form
- Using Flask framework
- Error occurs intermittently

Code snippet:
username = request.form.get('username')
parts = username.split('@')

Please help me:
1. Understand what's causing this error
2. Provide a solution
3. Explain how to prevent it in the future

Learning & Explanation

Template:

Explain [CONCEPT/TECHNOLOGY] in the context of [YOUR SITUATION].

I'm a [YOUR LEVEL] developer working on [PROJECT TYPE].

Please cover:
- Key concepts and terminology
- How it applies to my situation
- Practical examples
- Common pitfalls to avoid
- Next steps for learning more

Example Usage:

Explain microservices architecture in the context of scaling a web application.

I'm a mid-level developer working on an e-commerce platform that's growing rapidly.

Please cover:
- Key concepts and terminology
- How it applies to my situation
- Practical examples
- Common pitfalls to avoid
- Next steps for learning more

Advanced Prompting Techniques

Chain of Thought

Ask AI to think through problems step by step

Example:

Before writing the code, please think through: 1) What data structures are needed, 2) What edge cases to consider, 3) What the algorithm flow should be, then implement the solution.

Best For:

Complex algorithms, system design, debugging

Role Playing

Ask AI to take on a specific expert role

Example:

Act as a senior software architect reviewing this code for a high-traffic production system. Focus on scalability, maintainability, and performance.

Best For:

Code reviews, architecture decisions, specialized domains

Comparative Analysis

Request multiple approaches and comparisons

Example:

Show me 3 different ways to implement this feature, comparing their pros/cons in terms of performance, maintainability, and complexity.

Best For:

Design decisions, technology choices, optimization

Constraint-Based Prompting

Set specific limitations to guide the response

Example:

Solve this using only built-in Python libraries, with a maximum of 20 lines of code, optimized for readability over performance.

Best For:

Learning exercises, code golf, specific requirements

Common Prompting Mistakes

Vague Requirements

Problem: "Make this code better" - AI doesn't know what "better" means to you

Solution: Specify what you want improved: performance, readability, maintainability, etc.

No Context

Problem: Asking for code without explaining the larger system or use case

Solution: Provide context about your application, constraints, and requirements

Single-Shot Prompting

Problem: Expecting perfect results from the first prompt

Solution: Use iterative refinement - build on and improve the AI's responses

Ignoring AI Limitations

Problem: Asking AI to make architectural decisions without sufficient context

Solution: Use AI for specific technical problems, make high-level decisions yourself

Not Asking for Explanations

Problem: Getting code without understanding how or why it works

Solution: Always ask AI to explain its approach and reasoning

🎯 Practice Exercise: Improve These Prompts

Poor Prompt:

"Write a sorting function"

Your Improved Version Should Include:

• Programming language specification
• Input/output requirements
• Performance constraints
• Error handling needs
• Code style preferences

Try this: Take the poor prompt above and rewrite it using the techniques you've learned. Then test it with an AI tool and compare the results!

🛠️ Tool-Specific Prompting Tips

ChatGPT/GPT-4:

• Use system messages for consistent behavior
• Break complex tasks into steps
• Ask for code in specific formats
• Request explanations alongside code

GitHub Copilot:

• Write descriptive function names
• Use clear comments above code blocks
• Provide context in nearby code
• Use consistent coding patterns

Claude/Other AIs:

• Test different prompt structures
• Adjust formality based on the tool
• Use the tool's strengths (analysis, creativity)
• Provide feedback to improve responses

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