Prompt engineering is the art and science of crafting effective prompts to get the best results from AI tools like ChatGPT, Cursor, and Claude. Understanding how to structure prompts, what techniques work best, when to use each technique, andwhy they work can dramatically improve the quality of AI responses.
This comprehensive guide covers proven prompt engineering techniques, best practices, templates, and real-world examples to help you get great results from AI tools.
What is Prompt Engineering?
Prompt engineering is the practice of designing and optimizing prompts to:
- Get Better Results: More accurate, relevant, and useful responses
- Control Output: Shape AI responses to match your needs
- Improve Efficiency: Reduce iterations and get desired results faster
- Enhance Understanding: Get explanations, not just answers
- Maintain Consistency: Get reliable, predictable outputs
The difference between a good prompt and a bad prompt can be the difference between getting generic, unhelpful output and expert-level, actionable assistance.
Essential Prompt Engineering Techniques
Role-Based Prompting
What:
Assign AI a specific role or persona
How:
Start prompts with "You are a [role] with [expertise]"
When:
When you need expert-level, context-aware responses
Why:
Helps AI understand context and provide appropriate responses
❌ Before:
How do I optimize React performance?
✅ After:
You are a senior React performance engineer with expertise in optimization. How would you optimize this React component for maximum performance? [code]
📝 Template:
You are a [role] with [years] years of experience in [domain]. You specialize in [expertise]. [Your question/task]
Step-by-Step Reasoning
What:
Ask AI to break down problems into steps
How:
Request numbered steps or explicit reasoning process
When:
For complex problems requiring logical thinking
Why:
Forces AI to think through problems systematically
❌ Before:
How do I fix this bug?
✅ After:
Fix this bug step by step: 1. First, analyze the error message and identify the root cause 2. Then, trace through the code to understand the flow 3. Next, implement a fix 4. Finally, verify the fix works and doesn't break anything [error and code]
📝 Template:
Solve this problem step by step: 1. First, [analyze/understand] 2. Then, [identify/plan] 3. Next, [implement/solve] 4. Finally, [verify/optimize] [Problem]
Few-Shot Learning
What:
Provide examples to guide AI output format
How:
Show 2-3 examples of desired output before your request
When:
When you need specific format or style
Why:
AI learns from examples and replicates the pattern
❌ Before:
Write documentation for this function
✅ After:
Here are examples of good function documentation: Example 1: "Calculates factorial using recursion. Handles edge cases." Example 2: "Validates email format using regex. Returns boolean." Now write similar documentation for: [function]
📝 Template:
Here are examples of [what you want]: Example 1: [example] Example 2: [example] Now create [similar output] for: [your case]
Chain of Thought
What:
Ask AI to show its reasoning process
How:
Request explicit explanation of thinking at each step
When:
For complex problems or when you need to understand the process
Why:
Reveals AI's reasoning and helps catch errors
❌ Before:
How do I design this API?
✅ After:
Design this REST API step by step, showing your reasoning: 1. What you understand about the requirements 2. What approach you'll take (REST principles) 3. Why you chose that approach 4. How you'll structure endpoints, request/response formats [requirements]
📝 Template:
Think through this problem step by step, showing your reasoning: [Problem] Explain: - What you understand - What approach you'll take - Why you chose that approach - How you'll implement it
Constraints & Requirements
What:
Specify limitations and requirements explicitly
How:
List constraints, requirements, and preferences clearly
When:
When output must meet specific criteria
Why:
Ensures AI considers all requirements
❌ Before:
Create a user authentication system
✅ After:
Create a user authentication system with these requirements: - Use JWT tokens - Include password hashing with bcrypt - Support email/password login - Return user data on success Constraints: - Must use TypeScript - Follow REST API conventions - Include input validation - Handle errors gracefully [tech stack]
📝 Template:
Create [what] with these requirements: - [requirement 1] - [requirement 2] - [requirement 3] Constraints: - [constraint 1] - [constraint 2] [context]
Output Format Specification
What:
Explicitly specify desired output format
How:
Request specific format: markdown, JSON, code with comments, etc.
When:
When you need structured or formatted output
Why:
Ensures output matches your needs exactly
❌ Before:
Write a function to sort data
✅ After:
Write a Python function to sort data in this format: - Include type hints - Add docstring with examples - Include inline comments - Format code with proper indentation - Show usage example [requirements]
📝 Template:
Provide [output] in this format: - [format requirement 1] - [format requirement 2] - [format requirement 3] [request]
Best Practices: Before & After
Be Specific and Detailed
❌ Bad:
Write a function to sort data
✅ Good:
Write a Python function that sorts a list of dictionaries by the "price" key in descending order. Handle edge cases like empty lists and missing keys. Include type hints, docstring, and error handling.
💡 Tip: More details = better results. Include types, edge cases, error handling, and examples.
Provide Context
❌ Bad:
Fix this bug
✅ Good:
I'm getting "Cannot read property of undefined" error in my React component. I'm using React 18, TypeScript, and this is a functional component. Here's the code: [code]. The error occurs when [scenario].
💡 Tip: Include: your experience level, what you've tried, error messages, code snippets, constraints, and desired outcome.
Use Clear Structure
❌ Bad:
Help me with authentication and also database queries and maybe some frontend stuff
✅ Good:
I need help with three things: 1. User authentication (JWT tokens) 2. Database queries (PostgreSQL) 3. Frontend integration (React) Let's start with #1: [details]
💡 Tip: Break complex requests into numbered points. Address one thing at a time or clearly separate multiple topics.
Ask for Explanations
❌ Bad:
Write code to do X
✅ Good:
Write code to do X. Also explain: - Why this approach - How it works - Alternative approaches - Trade-offs - Best practices
💡 Tip: Don't just ask for code - ask for understanding. Request explanations, alternatives, and best practices.
Iterate and Refine
❌ Bad:
Expecting perfect result from one prompt
✅ Good:
Start broad: "How do I implement user authentication?" Then refine: "Can you show me JWT implementation?" Then iterate: "Add refresh token support"
💡 Tip: Start with a broad question, then refine based on responses. Build on previous answers in the conversation.
Ready-to-Use Prompt Templates
Code Generation
You are a [role] expert. Create [what] with these requirements: Requirements: - [requirement 1] - [requirement 2] Constraints: - [constraint 1] - [constraint 2] Please provide: 1. Implementation with comments 2. Explanation of approach 3. Usage example 4. Edge cases handled [context/code]
Debugging
You are a [role] debugging expert. I'm getting this error: [error message] Context: - [context 1] - [context 2] Code: [code] Please: 1. Identify the root cause 2. Explain why it's happening 3. Provide a fix 4. Suggest prevention strategies
Refactoring
You are a [role] code reviewer. Refactor this code to: Goals: 1. Improve [aspect 1] 2. Enhance [aspect 2] 3. Follow [principles] Current code: [code] Provide: - Refactored code - Explanation of changes - Before/after comparison - Benefits of refactoring
Learning
I'm a [current level] developer learning [topic]. Explain [concept] by: 1. Comparing to what I know: [familiar concept] 2. Showing practical examples 3. Explaining when to use it 4. Providing best practices 5. Including common pitfalls Make it beginner-friendly and relate to [my background].
Common Mistakes to Avoid
Vague Prompts: "Fix my code" without providing code, context, or error messages
No Context: Asking complex questions without background information or constraints
Single Shot Expectation: Expecting perfect results from one prompt instead of iterating
No Format Specification: Not specifying desired output format, leading to inconsistent results
Ignoring Responses: Not building on previous responses or asking follow-up questions
Quick Reference: Prompt Checklist
✅ Include:
- Specific role or expertise level
- Clear context and background
- Exact requirements and constraints
- Desired output format
- Examples or references
- Step-by-step instructions
- Request for explanations
❌ Avoid:
- Vague or ambiguous language
- Missing context or code
- Unclear requirements
- No output format specification
- Expecting perfection in one shot
- Not iterating on responses
- Ignoring AI suggestions
Validate AI-Generated Output
When AI generates JSON, code, or data structures, use our tools to validate, format, and ensure correctness before using in production.