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Prompt Engineering for Coding

The difference between mediocre and exceptional AI assistance

Good prompts are the key to getting great results from AI coding tools. This section provides battle-tested templates and best practices.

πŸš€ Quick Start Templates​

πŸ› Debugging​

I'm getting this error: [ERROR MESSAGE]

In this file: [FILE PATH]

Here's the relevant code:
[CODE BLOCK]

Context: [WHAT YOU WERE TRYING TO DO]

Help me understand what's wrong and how to fix it.

πŸ”„ Refactoring​

Refactor this code to be more [maintainable/performant/readable]:

[CODE BLOCK]

Requirements:
- Keep the same functionality
- Follow [LANGUAGE/FRAMEWORK] best practices
- Add comments explaining changes

πŸ“ Documentation​

Write comprehensive documentation for this code:

[CODE BLOCK]

Include:
- Function/class purpose
- Parameter descriptions
- Return value explanation
- Usage examples
- Edge cases

πŸ§ͺ Testing​

Write unit tests for this function using [TESTING FRAMEWORK]:

[CODE BLOCK]

Cover:
- Happy path
- Edge cases
- Error handling
- Boundary conditions

View all 50+ templates β†’

🎯 Prompt Engineering Principles​

1. Be Specific​

Bad: "Fix this code" Good: "Refactor this React component to use hooks instead of class components, maintaining the same functionality"

2. Provide Context​

Bad: "Add error handling" Good: "Add try-catch error handling with user-friendly messages for API calls in this Next.js page"

3. Show Examples​

Bad: "Write a function" Good: "Write a function like this example: [EXAMPLE], but for my use case: [YOUR CASE]"

4. Iterate​

Start broad, then refine:

  1. "Create a login form"
  2. "Add validation for email and password"
  3. "Add loading states and error messages"
  4. "Make it responsive and accessible"

πŸ“š Template Categories​

By Task Type​

πŸŽ“ Best Practices​

βœ… Do's​

  • Provide full context and relevant code
  • Specify the technology stack
  • Ask for explanations, not just code
  • Request multiple approaches when uncertain
  • Save and reuse successful prompts

❌ Don'ts​

  • Copy-paste without understanding
  • Skip error messages or logs
  • Use vague language ("better", "faster")
  • Forget to specify constraints
  • Ignore warnings about best practices

πŸ”₯ Advanced Techniques​

Chain of Thought Prompting​

Let's solve this step by step:

1. First, analyze the requirements
2. Then, design the architecture
3. Next, implement the core logic
4. Finally, add error handling and tests

[YOUR PROBLEM]

Role-Based Prompting​

Act as a senior [ROLE] with expertise in [TECHNOLOGY].

Review this code and provide:
- Security concerns
- Performance issues
- Best practice violations

[CODE]

Constraint-Driven Prompting​

Write a [COMPONENT] with these constraints:
- Must support [BROWSER/VERSION]
- No external dependencies
- Maximum 100 lines
- Follow [STYLE GUIDE]

[REQUIREMENTS]

πŸ’‘ Real Examples​

Example 1: React Component Refactoring​

Before (vague):

Make this better

After (specific):

Refactor this React class component to:
1. Use functional components with hooks
2. Extract custom hooks for data fetching
3. Add TypeScript types
4. Follow React 19 best practices
5. Improve accessibility

Result: Much better AI output with clear requirements

Example 2: Bug Investigation​

Before (incomplete):

This doesn't work

After (complete):

I'm getting "Cannot read property 'map' of undefined" in this React component:

[COMPONENT CODE]

Steps to reproduce:
1. Navigate to /dashboard
2. Click "Load Data"
3. Error appears

Expected: Data should display in a list
Actual: Error appears and app crashes

Environment: React 19, Next.js 15, Node 20

Result: Precise diagnosis and solution

πŸ› οΈ Tools Integration​

For Cursor​

Save frequently used prompts as .cursorrules files in your project root.

For Claude Code​

Create a prompts/ directory with markdown files for each template.

For ChatGPT/Claude​

Use the "Custom Instructions" feature to set default behavior.

πŸ” System Prompts Deep Dives​

Want to understand how professional AI coding tools work under the hood? Explore our deep-dive analyses of system prompts from popular tools:

Cursor System Prompts Analysis​

Deep dive into Cursor's core design philosophy and 5 key patterns:

  • Context-First Strategy
  • Tool Transparency Principle
  • Proactive Execution Mechanism
  • Semantic Search First
  • Simplified Code Editing Format

Best for: Developers who want to understand how AI coding assistants work and apply these patterns to their own workflows

Claude Code System Prompts Analysis​

Deep dive into Claude Code CLI tool's extreme minimalism design philosophy and 6 key patterns:

  • Extreme Minimalism Communication Strategy
  • Edit-First, Create-Never Philosophy
  • Professional Objectivity Principle
  • Context-Aware Tool Delegation
  • Parallel Execution Priority
  • Git Safety Protocol

Best for: Developers who want to understand CLI-optimized AI tool design and learn minimalist efficient interaction patterns

Cline System Prompts Analysis​

Deep dive into Cline open-source AI coding assistant's user approval-first design and 6 key patterns:

  • Mandatory Iterative Execution with Approval
  • One Tool Per Message Principle
  • Dual-Mode Architecture (PLAN/ACT)
  • Progressive Context Gathering
  • Approval Stratification Mechanism
  • Search/Replace Block System

Best for: Developers who want to understand open-source AI tool design and learn safe approval workflows

πŸ“– Learn More​

🀝 Contribute Your Prompts​

Found a prompt that works great? Share it with the community β†’