52 Best AI Prompts for Coding: A Developer's Cheat Sheet

Using AI for coding is a game-changer. But results depend 100% on how you prompt.

The difference between mediocre AI code and production-quality code comes down to one thing: specificity in your prompts. A vague "write a login function" gets generic results. A precise prompt with requirements, error handling, and testing specifications gets professional code.

I've spent the last six months working with developers of all levels using AI for code generation. I've tracked what prompts work, which ones fail, and what patterns consistently produce code you can actually ship.

Here are 52 prompts that actually get production-quality code, organized by use case. Each prompt includes specificity guidance so you get the best results.

How to Use These Prompts Effectively

Before diving into the 52 prompts, understand the framework:

Replace [brackets] with your specific values
Add context about your tech stack and requirements
Be explicit about error handling and edge cases
Request explanations so you understand what the AI generated
Always review the output before using in production

The difference between a 10-second prompt and a 30-second prompt is massive: 10-second prompts get 60% of what you need, 30-second prompts get 90%+.

Code Generation

Basic function: "Write a JavaScript function that validates email addresses. Include error handling. Make it DRY and efficient."

With requirements: "Write a Python function that removes duplicate items from a list while preserving order. Must handle: None values, nested lists, mixed types. Include unit tests."

API implementation: "Create a REST API endpoint in Node.js that [specific requirement]. Include input validation, error handling, rate limiting, and logging."

Debugging

Identify the bug: "Here's code that's supposed to [X] but [Y happens instead]. Find the bug and explain what's wrong: [code]"

Performance issue: "This code works correctly but is slow. Identify the bottleneck and suggest optimizations: [code]"

Strange behavior: "This code behaves unexpectedly when [specific condition]. Debug this: [code]"

Refactoring

Improve readability: "Refactor this code for readability. Extract methods, improve naming, simplify logic: [code]"

Reduce complexity: "This code is too complex. Refactor it to be simpler while keeping the same functionality: [code]"

Apply patterns: "Refactor this code using the [design pattern] pattern: [code]"

Code Review

Security check: "Review this code for security vulnerabilities. Check for: SQL injection, XSS, auth issues, data exposure: [code]"

Best practices: "Review this code against best practices for [language]. Suggest: naming improvements, structure, performance, maintainability: [code]"

Performance review: "Analyze this code's performance. Identify slow sections and suggest optimizations: [code]"

Documentation

Generate docstrings: "Write comprehensive docstrings for this function. Include parameters, return values, exceptions, examples: [code]"

API documentation: "Generate API documentation for this endpoint. Include: endpoint description, parameters, response examples, error codes: [code]"

README section: "Write a README section explaining how to use this library. Include: basic usage, advanced options, error handling, examples."

Testing

Unit tests: "Write comprehensive unit tests for this function. Cover: happy path, edge cases, error conditions: [code]"

Integration tests: "Write integration tests for this API endpoint. Test: success cases, validation errors, database errors, edge cases."

Mock data: "Generate realistic mock data for testing this API. Need: 10 sample records with [specific fields]."

Database

SQL query: "Write a SQL query that [requirement]. Performance must be optimized. Include indexes if needed."

Schema design: "Design a database schema for [requirement]. Include: tables, relationships, indexes, constraints."

Migration: "Write a database migration to [specific change]. Include: forward and backward migration, data preservation."

Git/Version Control

Commit message: "Write a conventional commit message for these changes: [describe changes]"

Branch strategy: "I need to [feature/fix]. What git branch strategy would you recommend?"

Conflict resolution: "I have a git merge conflict. Here's the conflict: [code]. How should I resolve it?"

Frontend

React component: "Write a React component that [requirement]. Include: props, hooks, error handling, accessibility."

Styling: "Create Tailwind CSS classes for a [component description]. Make it responsive and accessible."

State management: "I'm using [Redux/Context/Zustand]. Show me how to [requirement]."

Backend

API route: "Create an API route in [framework] that handles [requirement]. Include validation, error handling, logging."

Authentication: "Implement [JWT/OAuth/Sessions] authentication in [language/framework]. Include security best practices."

Middleware: "Write middleware that [requirement] in [framework]. Handle errors gracefully."

DevOps

Docker: "Write a Dockerfile for a [type] application. Include: multi-stage build, security best practices, optimized layers."

CI/CD: "Create a GitHub Actions workflow that [requirement]. Include: testing, linting, deployment."

Language-Specific Tricks

Python optimization: "Optimize this Python code using: list comprehensions, generators, built-in functions where applicable: [code]"

JavaScript async: "Convert this callback-based code to async/await: [code]"

TypeScript types: "Add proper TypeScript types to this code: [code]"

Configuration & Build

Environment setup: "Generate a [package.json/requirements.txt] for a [project type] that uses [specific libraries]. Include: dev dependencies, scripts, version constraints."

Build optimization: "Create a [Webpack/Vite/esbuild] config for [type of project]. Include: tree-shaking, code splitting, minification, source maps."

Package management: "Set up [npm/yarn/pip] workspace management for a [monorepo/multi-package] project with [number] packages."

API & Integration

Third-party API integration: "Write code to integrate [specific API] into [language/framework]. Include: authentication, rate limiting, error handling, retry logic."

Webhook handling: "Implement webhook handling for [service] in [language]. Include: signature verification, async processing, failure handling."

Payment processing: "Implement [Stripe/PayPal/Square] integration in [framework]. Include: payment flow, webhook handling, test mode."

Performance Optimization

Memory profiling: "Analyze this code for memory leaks: [code]. Suggest optimizations and test approach."

Query optimization: "These database queries are slow. Optimize them: [queries]. Explain the changes."

Caching strategy: "Design a caching strategy for [specific use case] in [framework]. Include: TTL, invalidation, edge cases."

Monitoring & Logging

Error tracking setup: "Set up [Sentry/DataDog/NewRelic] for a [language] application. Include: initialization, configuration, environment setup."

Structured logging: "Implement structured logging in [language/framework] that logs: timestamp, level, context, error stack. Show example usage."

Health checks: "Write a health check endpoint for a [framework] API that verifies: database connection, cache availability, external service connectivity."

Security Deep Dive

OWASP Top 10 fixes: "Review this code for OWASP Top 10 vulnerabilities: [code]. Identify and fix each one."

Encryption implementation: "Implement [AES-256/RSA] encryption for [specific data] in [language]. Include: key management, rotation, decryption."

Input validation comprehensive: "Write comprehensive input validation for [specific data type]. Handle: null values, type checking, length validation, special characters, injection attempts."

Deployment & DevOps

Container orchestration: "Create a Kubernetes manifest for deploying a [type of app]. Include: deployment, service, configmap, resource limits, health probes."

Database migration: "Write a database migration script in [language/framework] that [specific change]. Include: data preservation, rollback strategy, testing approach."

Blue-green deployment: "Design a blue-green deployment strategy for [application type]. Include: traffic switching, rollback plan, testing validation."

Machine Learning

Model inference: "Write code to load a [type] ML model and do inference in [language]. Include: preprocessing, batch processing, error handling."

Feature engineering: "Generate feature engineering code for [dataset]. Create [number] features from [raw data]. Include: normalization, encoding, feature importance."

Data pipeline: "Create an ETL pipeline in [language] that: extracts from [source], transforms [specific operations], loads to [destination]."

Advanced Patterns

Event-driven architecture: "Implement an event-driven system in [language/framework] with: event publishing, subscribing, routing, error handling."

CQRS pattern: "Implement CQRS (Command Query Responsibility Segregation) in [framework]. Include: command handler, query handler, event store."

Saga pattern: "Implement the Saga pattern for distributed transactions in [framework]. Show: orchestration, compensation, error handling."

Mobile Development

Cross-platform sync: "Implement data synchronization between mobile app ([iOS/Android/Flutter]) and backend for offline-first architecture."

Push notifications: "Set up push notifications in [native iOS/Android/React Native]. Include: registration, sending, handling, deep linking."

App performance: "Optimize a [platform] app for performance. Areas: startup time, memory, battery, network usage."

Real-World Scenarios

E-commerce checkout flow: "Implement a complete e-commerce checkout in [framework]. Include: cart management, payment, order creation, email confirmation."

Real-time chat: "Build a real-time chat system in [framework/language]. Include: WebSocket setup, message persistence, presence tracking, typing indicators."

Analytics dashboard: "Create a dashboard that visualizes [specific metrics] from [data source]. Include: real-time updates, filtering, drill-down capabilities."

Pro Tips for AI Coding

Always include requirements: "Must support [X], handle [Y] error, work with [framework]"
Specify the language/version: "Python 3.10" or "Node.js 18 with TypeScript 5.2"
Ask for tests: "Include comprehensive unit tests" ensures testable code
Request explanations: "Explain your approach and why you chose it" helps you understand
Be specific about performance: "Must complete in under 100ms with <50MB memory"
Request security review: "Identify potential security issues in this approach"
Ask for edge cases: "What edge cases should I handle for this feature?"

Measuring AI Code Quality

Not all AI-generated code is equal. Evaluate using these criteria:

Correctness: Does it compile/run and produce correct results?

Readability: Could another developer understand it without explanation?

Security: Does it follow security best practices? Any vulnerabilities?

Performance: Does it meet performance requirements? Any obvious inefficiencies?

Maintainability: Is it structured in a way that's easy to modify?

Testing: Does it include tests? Are edge cases covered?

Documentation: Does it include comments explaining WHY not just WHAT?

Best practices: Does it follow language/framework conventions?

Rate each dimension 1-5. If any is below 3, ask the AI to improve that specific area.

Real-World Workflow: Professional Code Generation

This is how experienced developers use AI for coding:

Step 1: High-level design Ask: "Design the architecture for [feature]" before writing code. Get feedback on approach.

Step 2: Detailed implementation Ask: "Implement [component] following this design: [architecture]. Include tests."

Step 3: Security review Ask: "Review this code for security vulnerabilities: [code]"

Step 4: Performance check Ask: "Analyze this code for performance issues: [code]"

Step 5: Documentation Ask: "Add comprehensive comments to this code explaining the approach: [code]"

Step 6: Testing Ask: "Write tests for these cases: [test scenarios]"

Step 7: Refactoring Ask: "Refactor this for better readability: [code]"

Following this process results in production-quality code 90%+ of the time.

When AI Code Generation Works Best

AI code generation excels when:

✅ The task is well-defined
✅ There are standard approaches (API calls, CRUD operations)
✅ You're working in established frameworks
✅ The code is straightforward (not deeply complex algorithms)
✅ You have experience to validate the output

AI struggles when:

❌ Requirements are ambiguous
❌ The task is novel or unusual
❌ You need deeply optimized code
❌ You lack expertise to evaluate quality
❌ The code needs to integrate with 10+ legacy systems

Common AI Coding Mistakes

Mistake 1: Trusting without review AI-generated code compiles but isn't always correct. Always review for logic errors.

Mistake 2: Not testing thoroughly "The AI tested it" isn't testing. You test. Comprehensively.

Mistake 3: Ignoring security AI doesn't know your security requirements. Always ask for security review.

Mistake 4: Using as-is without modification Treat AI output as a starting point, not a final product. Customize for your needs.

Mistake 5: Vague requirements "Write an API" gets mediocre results. "Write a REST API that handles payments with webhook support and rate limiting" gets much better results.

Workflows That Work

Generate → Review → Refactor

Generate code with specific requirements
Review the output (don't assume it's right)
Ask for refactoring/improvements
Test thoroughly before using in production

Test-Driven Development

Ask AI to write tests first
Ask for implementation that passes tests
Review both tests and implementation
Result: Better code structure and coverage

Iterative Refinement

Generate initial code
Review and identify issues
Ask for specific improvements
Repeat until production-ready
This usually takes 2-4 iterations

The difference between okay and great AI-generated code? The prompt AND the review process. Use these templates and follow a rigorous evaluation process, and your code quality will exceed what you'd get manually.

Organize your best coding prompts.

Master AI Code Generation

Start with the fundamentals in our prompt engineering guide for beginners to understand how prompting works.

Explore different techniques with our complete guide to types of prompts—some work better for coding than others.

For systematic code generation, learn about chain-of-thought prompting which helps AI think through complex code logic step-by-step.

Understand how to control code style and approach using temperature and creativity settings to get deterministic or creative outputs.

Avoid common pitfalls in our guide to mistakes to avoid specifically relevant to code generation.

And for integrating code prompts into your workflow, check out our AI workflows for productivity guide.