AI Prompts for Developers

As a developer, AI assistants can be your most powerful productivity tool—if you know how to communicate with them effectively. The difference between getting basic, copy-paste code and receiving thoughtful, well-architected solutions comes down to how you craft your prompts.

This comprehensive guide shows you exactly how to write prompts that produce production-quality code, insightful debugging help, expert code reviews, and architectural guidance. Whether you're working on frontend, backend, mobile, or infrastructure, these techniques will transform your development workflow.

Why Prompt Engineering Matters for Developers

Many developers approach AI assistants like they're search engines, typing vague queries and hoping for the best. But AI language models work differently—they thrive on context, specificity, and clear requirements.

Poor Prompt: "Write a function to sort data" Result: Generic, basic code that probably doesn't fit your needs

Expert Prompt: "Write a Python function that sorts a list of dictionaries by a nested key value. The function should handle missing keys gracefully and maintain stable sorting. Include type hints and docstring." Result: Production-ready code that handles edge cases

The time you invest in crafting good prompts pays back 10x in code quality and time saved on debugging and refactoring.

Code Generation Prompts

The Anatomy of a Great Code Prompt

Every effective code generation prompt should include:

• Language and Version: "Python 3.11" not just "Python"
• Framework and Version: "React 18 with TypeScript" not just "React"
• Functional Requirements: What the code must do
• Non-Functional Requirements: Performance, security, scalability considerations
• Constraints: Libraries you can/can't use, coding standards to follow
• Context: How this fits into your larger system

Example 1: API Endpoint Creation

Basic Prompt: "Create an API endpoint for user login"

Expert Prompt: "Create a Node.js Express API endpoint for user login with these requirements:

Framework: Express.js 4.18+ with TypeScript Authentication: JWT tokens (use jsonwebtoken library) Database: PostgreSQL with Prisma ORM Security Requirements:

• Hash passwords with bcrypt (12 salt rounds)
• Rate limit to 5 attempts per 15 minutes per IP
• Return generic error messages (don't reveal if email exists)
• Include CSRF protection

Expected input:

• email (string, validated email format)
• password (string, min 8 chars)

Expected output on success:

• accessToken (JWT, expires in 15min)
• refreshToken (JWT, expires in 7 days)
• user object (id, email, name)

Expected output on failure:

• Standard error response with appropriate HTTP status code

Please include input validation, error handling, and inline comments explaining security considerations."

Example 2: Frontend Component

Basic Prompt: "Make a data table component"

Expert Prompt: "Create a React data table component with these specifications:

Framework: React 18 with TypeScript State Management: Use React hooks (useState, useMemo, useCallback) Styling: Tailwind CSS with dark mode support

Features Required:

• Column sorting (ascending/descending)
• Pagination (client-side, configurable page size)
• Row selection (single and multi-select with checkboxes)
• Loading state display
• Empty state display
• Responsive design (stack on mobile)

Props Interface:

• data: Array>
• columns: Array<{ key: string, label: string, sortable?: boolean }>
• onRowSelect?: (selectedRows: any[]) => void
• pageSize?: number (default: 10)
• isLoading?: boolean

Performance Considerations:

• Optimize re-renders using React.memo
• Use virtual scrolling if data exceeds 1000 rows
• Memoize sort and filter operations

Please provide the component code with TypeScript types, and include a usage example."

Example 3: Database Query Optimization

Basic Prompt: "Optimize this query"

Expert Prompt: "Help me optimize this PostgreSQL query that's taking 3.2 seconds to execute:

Current Query: SELECT u.id, u.name, u.email, COUNT(o.id) as order_count, SUM(o.total) as total_spent FROM users u LEFT JOIN orders o ON u.id = o.user_id WHERE u.created_at > '2024-01-01' GROUP BY u.id ORDER BY total_spent DESC;

Context:

• users table: 500,000 rows
• orders table: 2,000,000 rows
• Current indexes: users(id), users(created_at), orders(id), orders(user_id)
• PostgreSQL version: 15.2
• Target execution time: < 500ms

Please:

• Analyze what's causing the slowness
• Suggest specific index improvements
• Recommend query restructuring if needed
• Provide the EXPLAIN ANALYZE output interpretation
• Include the optimized query with comments explaining the changes"

Debugging with AI

AI excels at debugging when you provide the right information. Here's how to structure debugging prompts for maximum effectiveness.

The Complete Debugging Prompt Template

Use this template structure for any debugging request:

"I'm encountering [ERROR TYPE] in my [LANGUAGE/FRAMEWORK] application.

Error Message: [Exact error message with stack trace]

Expected Behavior: [What should happen]

Actual Behavior: [What actually happens]

Code: [Relevant code snippet]

Environment:

• [Language/Framework versions]
• [OS/Platform]
• [Relevant dependencies]

What I've Tried: [Steps you've already taken]

Additional Context: [Any other relevant information]"

Example 1: React Memory Leak

"I'm encountering a memory leak in my React application that causes the browser to crash after 5-10 minutes of use.

Error Message: DevTools shows memory continuously increasing. After ~5 minutes: "Aw, Snap! Out of memory"

Expected Behavior: Memory usage should stabilize after initial render and not continuously grow.

Actual Behavior: Memory grows from 50MB to 1.5GB+ over 5 minutes, then browser crashes.

Code: ```jsx function UserDashboard() { const [users, setUsers] = useState([]); const [filter, setFilter] = useState('');

useEffect(() => { const ws = new WebSocket('ws://api.example.com/users');

ws.onmessage = (event) => { const newUser = JSON.parse(event.data); setUsers(prev => [...prev, newUser]); };

// Fetch initial data fetch('/api/users') .then(res => res.json()) .then(data => setUsers(data)); }, [filter]);

return (

); } ```

Environment:

• React 18.2.0
• Chrome 120.0.6099.129
• Mac OS 14.2

What I've Tried:

• Checked React DevTools Profiler - see continuous re-renders
• Added React.memo to UserCard - no improvement
• Suspected WebSocket but unsure how to properly clean up

Additional Context: The users array can grow to 10,000+ items. The filter input is used frequently. WebSocket sends ~50 updates per second."

Example 2: Node.js Performance Issue

"I'm encountering severe performance degradation in my Node.js API server under load.

Symptoms:

• Response times increase from 50ms to 5000ms+ as load increases
• CPU usage spikes to 100%
• Server becomes unresponsive at ~500 concurrent requests

Expected Behavior: Should handle 2000+ concurrent requests with consistent <200ms response times.

Actual Behavior: Starts well but degrades rapidly. After 500 requests, everything slows to a crawl.

Code: ```javascript app.get('/api/products', async (req, res) => { const products = await db.query('SELECT * FROM products');

const enrichedProducts = []; for (const product of products) { const reviews = await db.query( 'SELECT * FROM reviews WHERE product_id = $1', [product.id] ); const avgRating = reviews.reduce((sum, r) => sum + r.rating, 0) / reviews.length; enrichedProducts.push({ ...product, avgRating, reviewCount: reviews.length }); }

res.json(enrichedProducts); }); ```

Environment:

• Node.js 20.10.0
• Express 4.18.2
• PostgreSQL 15.2
• pg (node-postgres) 8.11.3
• PM2 cluster mode with 4 instances
• AWS EC2 t3.medium (2 vCPU, 4GB RAM)

What I've Tried:

• Increased PM2 instances - no improvement
• Added console.time/timeEnd - most time spent in database queries
• Checked database - queries individually are fast (<10ms)

Additional Context:

• products table: 5,000 rows
• reviews table: 50,000 rows
• Average 10 reviews per product
• Database connection pool size: 20"

Code Review and Optimization

AI can provide expert-level code reviews if you ask the right questions.

Comprehensive Code Review Prompt

"Please review this [LANGUAGE] code for:

• Security Vulnerabilities: SQL injection, XSS, CSRF, authentication issues, data exposure
• Performance: Algorithmic efficiency, memory usage, database query optimization
• Code Quality: Readability, maintainability, adherence to best practices
• Error Handling: Edge cases, proper error messages, graceful degradation
• Testing: Testability, areas that need test coverage

For each issue found:

• Explain the problem and its impact
• Rate severity (Critical/High/Medium/Low)
• Provide a corrected code example
• Suggest preventive measures

Code: [Your code here]

Context: [Where this code fits, expected usage patterns, performance requirements]"

Example: Authentication Middleware Review

"Please review this Node.js authentication middleware for security vulnerabilities, edge cases, and best practices:

```javascript function authMiddleware(req, res, next) { const token = req.headers.authorization?.split(' ')[1];

if (!token) { return res.status(401).json({ error: 'No token provided' }); }

try { const decoded = jwt.verify(token, process.env.JWT_SECRET); req.user = decoded; next(); } catch (err) { res.status(401).json({ error: 'Invalid token' }); } } ```

Context:

• Used in a financial application handling sensitive transactions
• JWT_SECRET is a 32-character random string
• Tokens are issued with 24-hour expiration
• Average 10,000 requests/minute to protected routes

Please focus especially on security issues and any edge cases I might have missed."

Architecture and Design Prompts

Use AI to help make architectural decisions by providing comprehensive context.

Example: Choosing a Database

"I need help choosing between PostgreSQL and MongoDB for my application.

Application Profile:

• Social fitness app with user workouts, progress tracking, social features
• Expected users: 100K in year 1, 1M in year 2
• Data types: User profiles, workout logs (time series), social connections (graph-like), media uploads

Read/Write Patterns:

• Reads: 70% of traffic
• Writes: 30% of traffic
• Most common queries:

- User workout history (time-range queries) - Social feed (friends' recent workouts) - Leaderboards (aggregations and sorting) - Search users by name

Requirements:

• Strong consistency for user data and follows/friendships
• Eventual consistency acceptable for social feed
• Must support ACID transactions for workout logging
• Need efficient aggregations for statistics
• Mobile app should work offline and sync later

Team Context:

• 3 backend developers, strong SQL experience, limited NoSQL experience
• 6-month timeline to MVP
• Budget constraints (prefer cost-effective solution)

Please:

• Compare PostgreSQL vs MongoDB for my specific use case
• Recommend one with clear reasoning
• Suggest schema design approach for the winner
• Highlight potential pitfalls with your recommendation
• Estimate scaling considerations"

Best Practices for Developer Prompts

1. Always Specify Versions

Bad: "Using React and TypeScript" Good: "Using React 18.2.0 with TypeScript 5.3.3"

Different versions have different features and APIs. Being specific prevents outdated solutions.

2. Include Error Messages in Full

Bad: "Getting an error with async/await" Good: "Getting error: 'TypeError: Cannot read property 'then' of undefined' at line 23 when awaiting getUserData()"

Complete error messages help AI understand exactly what's wrong.

3. Show What You've Tried

Bad: "This isn't working" Good: "I've tried: 1) Adding await, 2) Wrapping in try-catch, 3) Checking if function returns a promise. Issue persists."

This prevents AI from suggesting things you've already attempted.

4. Provide Minimal Reproducible Examples

Strip away irrelevant code. Keep only what's necessary to demonstrate the issue.

5. Ask for Explanations, Not Just Code

Bad: "Fix this" Good: "Fix this and explain why it wasn't working and how your solution addresses it"

Understanding prevents similar issues in the future.

6. Specify Code Style Preferences

"Please follow these conventions:

• Use async/await instead of .then()
• Prefer functional components over class components
• Use const for all variables unless reassignment needed
• Include JSDoc comments for functions"

7. Request Tests

"Please also provide unit tests for this function using Jest, covering happy path, edge cases, and error conditions."

Advanced Techniques

Iterative Refinement

Start broad, then refine based on responses:

• "Generate a user authentication system for my Node.js app"
• Review response, then: "Modify to use refresh tokens and add rate limiting"
• Continue: "Add email verification step before account activation"

Comparative Analysis

"Compare these two approaches for handling file uploads in React: Approach A: [code] Approach B: [code]

Analyze trade-offs in terms of:

• Memory efficiency
• User experience
• Error handling robustness
• Browser compatibility

Recommend the better approach with reasoning."

Code Translation

"Convert this Python function to JavaScript while maintaining the same logic and error handling: [Python code]

Requirements:

• Use modern ES6+ syntax
• Maintain type safety with JSDoc comments
• Preserve performance characteristics"

Conclusion

Mastering prompt engineering for development work is one of the highest-leverage skills you can develop. The patterns and templates in this guide will help you get expert-level assistance from AI, dramatically accelerating your development workflow.

Remember: The key to great code from AI is providing great context in your prompts. Invest time in crafting detailed prompts, and you'll save multiples of that time in debugging, refactoring, and researching.

Start applying these techniques today, and watch your development speed and code quality improve dramatically!