ChatGPT has become an essential tool for developers, but most underutilize its capabilities. The difference between mediocre and exceptional results lies in prompt engineering—crafting requests that extract precisely what you need. This guide provides battle-tested prompt patterns for common developer tasks, helping you integrate AI assistance effectively into your daily workflow.

đź“‹ Key Takeaways
  • Specific context in prompts yields dramatically better code suggestions
  • Role-based prompting improves response quality for specialized tasks
  • Iterative refinement beats single-shot prompts for complex problems
  • Always validate AI-generated code—it can be confidently wrong

I. Understanding Prompt Engineering Fundamentals

Effective prompts share common characteristics that guide AI toward useful responses. Understanding these principles transforms how you interact with ChatGPT.

A. The Context-Specificity Principle

  • Bad prompt: "Write a function to validate email" — Too vague, produces generic code.
  • Good prompt: "Write a TypeScript function that validates email addresses for a React form, returning an error message string or null if valid. Use regex patterns that handle common edge cases." — Specific technology, context, and output format.
  • Excellent prompt: Includes your existing code structure, naming conventions, and specific requirements.

B. Role-Based Prompting

  • Set expertise level: "Act as a senior backend developer with 10 years of experience in Node.js and PostgreSQL."
  • Benefits: Responses use appropriate terminology, consider edge cases experts would, and avoid over-explaining basics.
  • Stack-specific roles: "You are a WordPress security expert" yields more relevant WordPress-specific advice.

II. Code Review Prompts

AI-assisted code review catches issues humans often miss while freeing senior developers for higher-level reviews.

A. General Code Review Template

Review this [language] code for:
1. Security vulnerabilities
2. Performance issues
3. Code style and readability
4. Potential bugs
5. Suggestions for improvement

Context: This code [describe what it does and where it's used]

[paste your code here]

B. Security-Focused Review

Act as a security auditor. Review this code for:
- SQL injection vulnerabilities
- XSS attack vectors
- Authentication/authorization flaws
- Sensitive data exposure
- OWASP Top 10 vulnerabilities

Be specific about line numbers and include fix recommendations.

[paste your code here]
Ad Space - Mid Content

III. Debugging Assistance Prompts

Debugging with AI accelerates problem resolution when you provide sufficient context about the issue.

A. Error Analysis Template

I'm getting this error:
[paste error message]

In this code:
[paste relevant code]

Environment:
- Language/Framework: [specify]
- Version: [specify]
- OS: [if relevant]

What I've tried:
- [list attempted solutions]

What's causing this error and how do I fix it?

B. Logic Bug Investigation

This function should [expected behavior] but instead [actual behavior].

Function:
[paste code]

Test case that fails:
Input: [input]
Expected: [expected output]
Actual: [actual output]

Walk through the logic step by step to identify where it goes wrong.

IV. Documentation Generation Prompts

AI excels at generating documentation from code, saving hours of tedious writing.

A. API Documentation

Generate OpenAPI/Swagger documentation for this REST endpoint:

[paste controller/route code]

Include:
- Endpoint description
- Request parameters with types and descriptions
- Response schemas for success and error cases
- Example requests and responses

B. Inline Code Documentation

Add comprehensive JSDoc/docstring comments to this code:

[paste code]

Include:
- Function purpose
- Parameter descriptions with types
- Return value description
- Throws/exceptions if applicable
- Usage example for complex functions

V. Refactoring Assistance Prompts

These prompts help improve code quality while maintaining functionality.

A. Code Smell Detection

Analyze this code for code smells:

[paste code]

Identify:
- Long methods that should be broken up
- Duplicate code that could be extracted
- Complex conditionals that need simplification
- Poor naming that reduces readability
- Coupling issues

For each issue, suggest specific refactoring steps.

B. Design Pattern Application

This code [describe current implementation]:

[paste code]

Refactor it using the [specific pattern] pattern. Explain:
1. Why this pattern is appropriate here
2. How the refactored code improves maintainability
3. Any trade-offs to consider

VI. Learning and Explanation Prompts

Use ChatGPT as a patient tutor for unfamiliar technologies or concepts.

A. Concept Explanation

Explain [concept] to me as if I'm a [junior/mid/senior] developer with experience in [your background].

Include:
- Core concept explained simply
- Practical use cases
- Common pitfalls
- A minimal working example in [language]

B. Code Reading Assistance

I'm reading this [library/framework] source code and don't understand this section:

[paste code]

Explain:
1. What this code does step by step
2. Why it's implemented this way
3. Any patterns or techniques being used

VII. Testing Prompts

Generate comprehensive test cases to improve code coverage.

A. Unit Test Generation

Generate [testing framework] unit tests for this function:

[paste function]

Include tests for:
- Happy path scenarios
- Edge cases (empty inputs, null values, boundary conditions)
- Error handling
- Performance if relevant

Use descriptive test names that explain what's being tested.

B. Test Case Brainstorming

I need to test this feature: [describe feature]

List comprehensive test cases covering:
- Functional requirements
- Edge cases
- Error scenarios
- Security considerations
- Performance scenarios

Format as a checklist I can work through.

VIII. Workflow Automation Prompts

Automate repetitive development tasks with generated scripts.

A. Script Generation

Write a [bash/Python/PowerShell] script that:
[describe what you need]

Requirements:
- Handle errors gracefully with meaningful messages
- Add logging for debugging
- Include usage instructions in comments
- Make it work on [specify OS]

B. Git Workflow Helpers

Create a git hook/script that:
- [describe automation need]
- Runs on [pre-commit/pre-push/etc]
- Exits with clear error message if checks fail
- Provides helpful output on success

IX. Best Practices for AI-Assisted Development

  • Always review generated code: AI can produce code that looks correct but has subtle bugs or security issues.
  • Iterate on prompts: If the first response isn't right, refine your prompt rather than accepting subpar output.
  • Provide examples: Show the AI your coding style with examples for more consistent output.
  • Break down complex tasks: Multiple focused prompts outperform single complex ones.
  • Verify with tests: Always test generated code before committing.

X. Conclusion

ChatGPT transforms developer productivity when used effectively. The prompts in this guide provide starting points—adapt them to your specific needs and tech stack. Remember that AI is a tool to augment your skills, not replace critical thinking. The best results come from combining AI assistance with your domain knowledge and always validating outputs before they reach production.

What's your favorite way to use ChatGPT in your development workflow? Share in the comments!