Prompt Engineering for Developers: Writing Better AI-Assisted Code
As AI coding assistants like GitHub Copilot, ChatGPT, and Cursor IDE become indispensable tools in modern software development, prompt engineering for developers has emerged as a critical skill. With 82% of developers now using AI tools in their workflows and prompt engineering questions appearing in coding interviews at major tech companies, mastering how to write effective prompts for AI code generation is no longer optional—it's essential for developer productivity.
This comprehensive guide explores prompt engineering techniques specifically for developers, covering practical frameworks, proven patterns, and best practices that transform AI coding assistants from helpful suggestions into powerful pair programmers. Whether you're using GitHub Copilot, Cursor IDE, Claude, or ChatGPT for coding, these strategies will help you write better AI-assisted code.
Why Prompt Engineering Matters for Developers
The quality of AI-generated code depends fundamentally on the quality of your prompts. A poorly phrased request yields generic, potentially buggy solutions, while a well-crafted prompt produces thoughtful, accurate, and production-ready code.
The Current Landscape
The numbers tell a compelling story:
- 82% of developers actively use AI tools in their development process
- HackerRank now evaluates prompt engineering ability in coding interviews
- 46% of code in Copilot-enabled files is now AI-generated
- 55% faster completion times for developers using AI assistants effectively
Yet despite widespread adoption, most developers are using these tools inefficiently. As we explored in The AI Pair-Programming Boom, while AI coding assistants are becoming ubiquitous, the difference between average and expert prompt engineering can mean the difference between spending hours debugging AI-generated code and shipping production-ready features in minutes.
The Core Problem
Most prompt failures don't stem from model limitations—they come from ambiguity. When you write "fix this function," the AI doesn't know:
- What specifically is broken
- What the expected behavior should be
- What constraints or requirements exist
- What coding standards to follow
Effective prompt engineering eliminates this ambiguity through structure, context, and clarity.
The PCTF Framework: A Foundation for Developer Prompts
The most successful developer prompts follow a structured framework. I've adapted industry best practices into the PCTF Framework: Persona, Context, Task, and Format.
1. Persona: Define the AI's Role
Tell the AI what role to assume. This primes the model to adopt the appropriate tone, depth, and approach.
Example:
❌ Poor: "Write a function to validate email addresses"
✅ Better: "Act as a senior backend engineer with expertise in data validation.
Write a robust email validation function for a production fintech application."
The persona sets expectations for:
- Code quality (senior vs. junior approach)
- Security considerations (fintech requires extra rigor)
- Best practices (production-ready standards)
2. Context: Provide Relevant Background
The AI needs to understand the environment, constraints, and requirements. This is especially critical for coding tasks where architectural decisions matter.
Example:
// Context: Next.js 15 app using TypeScript and Zod for validation
// Must handle edge cases: plus addressing, subdomains, internationalized domains
// Performance requirement: <1ms validation time
// Persona: Senior backend engineer
// Task: Create email validation with detailed error messages
// Format: TypeScript function with JSDoc, return structured error object
3. Task: Be Specific and Decomposed
Break complex tasks into clear, manageable steps. Ambiguous requests produce ambiguous results.
Example:
❌ Poor: "Make this component better"
✅ Better: "Refactor this React component to:
1. Extract the data fetching logic into a custom hook
2. Add error boundary handling
3. Implement loading states with Suspense
4. Optimize re-renders using React.memo
5. Add TypeScript types for all props and state"
4. Format: Specify the Desired Output
Tell the AI exactly what you want back—code style, documentation level, test coverage, or specific patterns to use.
Example:
Format requirements:
- Use functional React components with TypeScript
- Include JSDoc comments for all exported functions
- Follow Airbnb style guide conventions
- Include unit tests using Jest and React Testing Library
- Return code with inline explanatory comments
Complete PCTF Example
Here's how it all comes together:
Persona: You are a senior full-stack engineer with 10 years of experience
building scalable fintech applications.
Context: I'm building a user authentication system for a Next.js 15 app.
The app uses PostgreSQL with Prisma ORM, implements JWT tokens, and must
comply with GDPR requirements. We're using TypeScript throughout.
Task: Create a secure password reset flow that:
1. Generates a cryptographically secure reset token
2. Stores the token with 1-hour expiration
3. Sends reset email via SendGrid
4. Validates token on password update
5. Invalidates all existing sessions after reset
Format:
- TypeScript functions with full type safety
- Include error handling for all edge cases
- Add JSDoc comments
- Use bcrypt for password hashing
- Return structured error objects with codes
- Include security best practices comments
This structured approach dramatically improves output quality because it eliminates guesswork.
Tool-Specific Prompt Engineering Techniques for GitHub Copilot and Cursor IDE
Different AI coding tools respond better to different approaches. Let's explore proven prompt engineering techniques for the most popular platforms, including GitHub Copilot, Cursor IDE, ChatGPT, and Claude.
GitHub Copilot: Contextual Prompting
Copilot excels at understanding context from your codebase. Maximize its effectiveness by managing that context deliberately.
Technique 1: Neighboring Tabs Strategy
Copilot processes all open files in your IDE, not just the current file. Use this to your advantage:
// 1. Open your type definitions file (types.ts)
export interface User {
id: string;
email: string;
createdAt: Date;
preferences: UserPreferences;
}
// 2. Open your current working file (userService.ts)
// Copilot now knows your User type structure
// Comment-driven prompt:
// Create a function to fetch user by email and map to User type
// Should handle null cases and throw descriptive errors
// Copilot will generate code aware of your User interface:
The AI sees your types and follows your existing patterns automatically.
Technique 2: High-Level Context Comments
Place architectural context at the top of files:
/**
* User Service Layer
*
* Architecture: Clean Architecture with Repository Pattern
* Database: PostgreSQL via Prisma ORM
* Authentication: JWT tokens with refresh rotation
* Error Handling: Custom AppError classes with error codes
* Validation: Zod schemas for all inputs
* Logging: Winston with structured logging
*/
// Now when you write prompts, Copilot follows these patterns:
// Create a function to update user profile with validation
export async function updateUserProfile(
// Copilot will suggest Zod validation, proper error handling,
// structured logging, etc., matching your documented architecture
Technique 3: Example-Driven Prompting
Show Copilot one example, then let it replicate the pattern:
// Example endpoint - Copilot will learn the pattern:
export async function getUserById(id: string): Promise<Result<User>> {
try {
const user = await prisma.user.findUnique({ where: { id } });
if (!user) {
return Err(new AppError('USER_NOT_FOUND', 404, `User ${id} not found`));
}
return Ok(user);
} catch (error) {
logger.error('getUserById failed', { id, error });
return Err(new AppError('DATABASE_ERROR', 500, 'Failed to fetch user'));
}
}
// Now create similar endpoint for fetching by email:
export async function getUserByEmail(
// Copilot will replicate the error handling, logging, and Result pattern
Cursor IDE: Advanced Prompting Strategies
Cursor IDE has unique features that enable more sophisticated prompting techniques.
Technique 1: System-Level Framing
Cursor heavily relies on system prompts. Begin conversations with role definitions:
System: You are an expert React developer with 10+ years of experience.
You write concise, modern code using the latest React patterns. You favor
composition over inheritance, prefer hooks over class components, and
always consider performance implications.
Now every response will be contextually sharp and targeted.
Technique 2: "Rewrite as" Instead of "Change this"
Cursor interprets command words literally. Use specific verbs for better results:
❌ Poor: "Change this to use async/await"
✅ Better: "Rewrite this function using async/await with proper error handling"
The word "rewrite" triggers full regeneration, while "change" leads to patchy edits.
Technique 3: Chain-of-Thought Prompting
For complex tasks, guide the AI through intermediate reasoning steps:
Problem: Create a caching layer for our API with Redis
Prompt: Let's build this step-by-step:
First, analyze the current API implementation and identify:
1. Which endpoints are called most frequently?
2. What data changes rarely and can be cached longer?
3. What are the current response times?
Then, design a caching strategy:
1. Determine appropriate TTL values for each endpoint
2. Identify cache invalidation triggers
3. Design cache key structure
Finally, implement:
1. Redis connection with retry logic
2. Cache wrapper functions
3. Invalidation hooks
4. Monitoring and metrics
Walk me through your reasoning at each step.
This CoT approach improves how AI handles multi-part tasks by 16.67% according to research comparing o1-mini to GPT-4o on code generation tasks.
Technique 4: Use @Docs Feature
Cursor's @Docs feature lets you index and reference documentation:
// Index your API documentation
@Docs > Add New Doc > "Internal API Standards"
// Then reference it in prompts:
@Docs Internal API Standards
Create a new REST endpoint following our documented standards for:
- Error response format
- Pagination structure
- Authentication headers
- Rate limiting
This ensures consistency across your codebase without repeating standards in every prompt.
Technique 5: Cursor Rules as Encyclopedia Articles
Write .cursorrules as informative guides, not commands:
❌ Poor: "You are a senior frontend engineer expert in TypeScript"
✅ Better:
# Project Architecture
This project uses Clean Architecture with the following layers:
- Presentation (React components)
- Application (use cases/services)
- Domain (business logic, entities)
- Infrastructure (API clients, database)
## TypeScript Standards
- Strict mode enabled
- No implicit any
- Explicit return types on all functions
- Prefer interfaces over types for object shapes
## React Patterns
- Functional components only
- Custom hooks for shared logic
- Composition via children and render props
- Controlled components for forms
Advanced Prompting Patterns for Code Generation
Pattern 1: Few-Shot Prompting
Provide 3-5 examples to establish clear expectations. The AI will match the pattern:
// Examples of our custom hook pattern:
// Example 1: useFetch hook
export function useFetch<T>(url: string): UseFetchResult<T> {
const [data, setData] = useState<T | null>(null);
const [loading, setLoading] = useState(true);
const [error, setError] = useState<Error | null>(null);
useEffect(() => {
// implementation
}, [url]);
return { data, loading, error };
}
// Example 2: useAuth hook
export function useAuth(): UseAuthResult {
const [user, setUser] = useState<User | null>(null);
const [loading, setLoading] = useState(true);
const [error, setError] = useState<Error | null>(null);
// implementation
return { user, loading, error };
}
// Now create a useLocalStorage hook following the same pattern:
export function useLocalStorage<T>(
// AI will match the return type structure, state management,
// and error handling pattern from the examples
Pattern 2: Constraint-Based Prompting
Explicitly state what NOT to do:
Create a file upload component for profile pictures with these constraints:
Requirements:
✅ Accept only .jpg, .png, .webp formats
✅ Maximum file size: 5MB
✅ Preview image before upload
✅ Show upload progress
✅ Handle upload errors gracefully
Constraints:
❌ Do NOT use external libraries (use native File API)
❌ Do NOT process images on client side
❌ Do NOT store files in component state (use refs)
❌ Do NOT allow multiple simultaneous uploads
❌ Do NOT bypass security validation
Pattern 3: Refinement Prompting (Iterative)
Start broad, then refine through conversation:
Iteration 1:
"Create a user search component with autocomplete"
Review output, then iterate:
Iteration 2:
"Good start. Now add:
- Debounced input (300ms)
- Loading state with skeleton UI
- Empty state messaging
- Keyboard navigation (arrow keys, enter)
- Highlight matching text"
Review again:
Iteration 3:
"Perfect. Now optimize:
- Memoize search results
- Cancel in-flight requests on new input
- Add accessibility attributes (ARIA)
- Implement virtual scrolling for 1000+ results"
Each iteration builds on the previous, letting you guide toward production quality.
Pattern 4: Test-Driven Prompting
Provide the test first, then ask for implementation:
// Provide this test:
describe('calculateDiscount', () => {
it('applies 10% discount for orders over $100', () => {
expect(calculateDiscount(150, 'SAVE10')).toBe(135);
});
it('applies 20% discount for orders over $500', () => {
expect(calculateDiscount(600, 'SAVE20')).toBe(480);
});
it('throws error for invalid promo codes', () => {
expect(() => calculateDiscount(100, 'INVALID')).toThrow('Invalid promo code');
});
it('does not apply discount below minimum threshold', () => {
expect(calculateDiscount(50, 'SAVE10')).toBe(50);
});
});
// Then prompt:
"Implement the calculateDiscount function to pass all these tests.
Use TypeScript with proper error handling and input validation."
The tests define exact behavior, eliminating ambiguity.
Prompt Engineering Best Practices for Debugging, Documentation, and Code Review
Debugging with AI: The Systematic Approach
When debugging with AI coding assistants, prompt structure matters enormously:
Poor debugging prompt:
"This code doesn't work, fix it"
Effective debugging prompt:
## Problem Description
The user authentication is failing with 401 errors intermittently.
## Expected Behavior
Users should remain authenticated for 24 hours after login.
## Actual Behavior
Users are being logged out randomly after 10-30 minutes.
## Error Message
UnauthorizedError: jwt expired at verify (/node_modules/jsonwebtoken/verify.js:223:19)
## Code
[paste relevant code]
## Environment
- Node.js v20.10.0
- jsonwebtoken v9.0.2
- JWT_EXPIRY set to '24h' in .env
## What I've Tried
- Verified token is being stored correctly in localStorage
- Confirmed JWT_EXPIRY env variable is loaded
- Checked server time sync
## Question
What could cause JWT tokens to expire early despite 24h expiry setting?
This structured approach helps AI identify the actual issue instead of guessing.
Documentation: Persona Matters
Use appropriate personas for different documentation needs:
For API documentation:
"Act as a technical writer creating API documentation for external developers.
Generate OpenAPI-compliant documentation for this endpoint with examples."
For code comments:
"Act as a senior developer mentoring junior team members. Add clear,
educational comments explaining why this algorithm works, not just what it does."
For README files:
"Act as a developer advocate creating onboarding documentation. Write a README
that gets a new developer from clone to running locally in under 5 minutes."
Different personas produce different documentation styles suited to different audiences. This approach complements the vibe coding methodology where natural language communication with AI becomes central to the development process.
Code Review: Specific Review Criteria
Tell the AI exactly what to review for:
Review this pull request focusing on:
Security:
- SQL injection vulnerabilities
- XSS attack vectors
- Authentication bypasses
- Sensitive data exposure
Performance:
- N+1 query problems
- Unnecessary re-renders
- Memory leaks
- Inefficient algorithms
Code Quality:
- TypeScript type safety
- Error handling completeness
- Test coverage gaps
- Code duplication
Provide specific line numbers and suggest fixes.
Refactoring: Clear Transformation Goals
State exactly what you want to improve:
Refactor this component to improve:
1. Testability
- Extract side effects to separate functions
- Make dependencies injectable
- Isolate business logic from UI
2. Reusability
- Extract common patterns to hooks
- Parameterize hard-coded values
- Create composition points
3. Type Safety
- Replace 'any' with proper types
- Add generic constraints
- Use discriminated unions for state
4. Performance
- Memoize expensive calculations
- Optimize dependency arrays
- Prevent unnecessary re-renders
Maintain existing functionality and behavior.
Common Prompt Engineering Mistakes
Mistake 1: Being Too Vague
❌ "Make this faster"
✅ "Optimize this function to handle 10,000 items with <100ms execution time.
Profile current performance, identify bottlenecks, then apply appropriate
optimizations (memoization, algorithm improvements, or caching)."
Mistake 2: Omitting Constraints
❌ "Create a login form"
✅ "Create a login form that:
- Works without JavaScript (progressive enhancement)
- Meets WCAG 2.1 AA accessibility standards
- Implements rate limiting (5 attempts per 15 minutes)
- Uses bcrypt for password hashing (10 rounds)
- Includes CSRF protection"
Mistake 3: Not Providing Context
❌ "Fix the bug in this function"
[pastes function with no context]
✅ "This function is part of a payment processing pipeline that handles
transactions in multiple currencies. It should convert amounts to USD
before saving to the database. Currently, conversions are incorrect for
EUR and GBP. The exchange rates come from an external API with 1-hour cache.
[paste function]
The bug appears to be in the currency conversion logic."
Mistake 4: Accepting First Output Without Iteration
AI-generated code is a starting point, not the finish line. Always refine:
First pass: "Create a user profile component"
Review: Basic structure is good, but missing validation
Second pass: "Add Zod validation schema and error display"
Review: Validation works, but UX needs improvement
Third pass: "Add inline validation on blur, clear errors on focus,
show success states"
Review: Perfect, ready for code review
Mistake 5: Ignoring Model-Specific Quirks
Different models need different approaches:
GPT-4o: Responds well to short, structured prompts with hashtags and numbered lists
Claude: Benefits from XML-style tags like <task>, <context>, <requirements>
Copilot: Relies heavily on file context and comments
Match your prompt style to your tool.
Measuring Prompt Engineering Success
Track these metrics to improve your prompting skills:
Efficiency Metrics
- Time to acceptable solution: How many iterations before production-ready code?
- Acceptance rate: What percentage of AI suggestions do you use?
- Edit distance: How much do you modify AI output?
Quality Metrics
- Bug rate: Do AI-generated sections have more bugs?
- Code review feedback: How much does AI code get flagged in reviews?
- Test coverage: Can you test AI-generated code as easily as hand-written code?
Productivity Metrics
- Features delivered: Are you shipping faster with AI assistance?
- Time in flow: Are you staying focused longer?
- Learning rate: Are you picking up new patterns faster?
The Future of Prompt Engineering for Developers
Prompt engineering is evolving rapidly. Here's what's emerging:
1. Context Engineering Over Prompting
The next evolution is context engineering—giving AI comprehensive project briefings before it writes code:
Instead of prompting each task, you'll provide:
- Full architecture documentation
- Code style guides
- Test patterns
- Security requirements
- Performance benchmarks
Then AI works autonomously within those constraints.
2. Prompt Libraries and Templates
Teams are building shared prompt libraries:
// Company prompt templates
export const prompts = {
newEndpoint: (resource: string) => `
Create a RESTful endpoint for ${resource} following our API standards:
@Docs API Standards
@Docs Security Requirements
@Docs Error Handling Patterns
`,
testSuite: (component: string) => `
Generate comprehensive test suite for ${component}:
@Docs Testing Standards
Include: unit tests, integration tests, edge cases
`
};
3. AI-Assisted Prompt Refinement
AI will help you write better prompts:
You: "Create a login form"
AI: "I can help with that. To generate the best solution, please clarify:
1. Authentication method? (JWT, sessions, OAuth)
2. Styling framework? (Tailwind, CSS Modules, styled-components)
3. Validation approach? (Zod, Yup, native)
4. Accessibility requirements? (WCAG level?)
5. Error handling preferences?
Or I can create a basic implementation and we'll refine it."
4. Multimodal Prompting
Soon you'll combine:
- Voice: Describe what you want verbally
- Visual: Show mockups or diagrams
- Code: Reference existing implementations
- Text: Provide written specifications
All in a single prompt for richer context.
Practical Exercises to Improve Your Prompt Engineering
Exercise 1: The Specificity Challenge
Take a vague prompt and make it specific:
Vague: "Create a button component"
Your turn: Rewrite with persona, context, task breakdown, and format requirements.
Persona: Senior React developer with accessibility expertise
Context: Design system for a fintech SaaS product using React, TypeScript,
and Tailwind CSS. Must support multiple variants and sizes.
Task: Create a reusable Button component that:
1. Supports variants: primary, secondary, ghost, danger
2. Supports sizes: sm, md, lg
3. Handles loading states with spinner
4. Accepts all native button props
5. Is fully accessible (ARIA labels, focus management, keyboard nav)
6. Prevents double-click during loading
Format:
- TypeScript with strict typing
- Forwarded refs for parent control
- Tailwind for styling with cva for variants
- JSDoc comments
- Storybook stories for each variant
Exercise 2: The Debugging Practice
Practice structuring debugging prompts:
Problem: API endpoint returns 500 errors randomly
Your turn: Write a comprehensive debugging prompt using the systematic approach.
Exercise 3: The Iteration Game
Generate code with a simple prompt, then refine it three times. Notice how each iteration improves quality.
Essential Resources and Further Reading
Official Documentation
- GitHub Copilot Prompt Engineering Guide
- Cursor IDE Documentation
- Claude Prompt Engineering Best Practices
- OpenAI Prompt Engineering Guide
Research and Studies
- Prompt Engineering Guide - Comprehensive academic resource
- Research on Chain-of-Thought Prompting for Code
- GitHub Copilot Impact Study
Community Resources
- Awesome Prompt Engineering - Curated list of resources
- Cursor Community Forum - Real-world prompt patterns
- r/ChatGPTCoding - Developer discussions
Conclusion: Mastering Prompt Engineering for Better AI-Assisted Development
Prompt engineering for developers transforms your role from code typist to AI orchestrator. The developers who master this skill—who can communicate intent clearly, provide rich context, and iteratively refine output using tools like GitHub Copilot, Cursor IDE, and ChatGPT—will be exponentially more productive than those who cannot.
The PCTF framework (Persona, Context, Task, Format) provides a solid foundation for writing effective prompts, but true mastery of prompt engineering techniques comes from deliberate practice. Start applying these AI coding best practices today:
- Structure every prompt with the PCTF framework for consistent results
- Provide context through open files, comments, and documentation
- Iterate deliberately instead of accepting first AI-generated outputs
- Measure your improvement through acceptance rates and code quality metrics
- Share prompt patterns with your team to multiply developer productivity
As AI coding assistants continue evolving, prompt engineering will become as fundamental as version control or testing. The time you invest in mastering this skill today will compound over your entire career, whether you're working with GitHub Copilot, Cursor IDE, Claude, or the next generation of AI development tools.
The future of software development is collaborative—between human creativity and AI capability. Your ability to bridge that collaboration through effective prompt engineering techniques will define your success in the AI-assisted era.
Ready to improve your AI-assisted coding? Start practicing these prompt engineering techniques in your next development session. Your future self will thank you.
Frequently Asked Questions (FAQ)
What is prompt engineering for developers?
Prompt engineering for developers is the practice of crafting effective instructions for AI coding assistants like GitHub Copilot, Cursor IDE, ChatGPT, and Claude to generate high-quality code. It involves structuring prompts with the right context, constraints, and specifications to get production-ready AI-generated code that meets your requirements.
How can I improve my prompts for GitHub Copilot?
To improve prompts for GitHub Copilot:
- Use the PCTF framework (Persona, Context, Task, Format)
- Keep relevant files open in your IDE for context
- Write high-level context comments at the top of files
- Provide specific examples of the pattern you want
- Break complex tasks into smaller, specific steps
- Use clear, descriptive variable and function names
What's the difference between prompt engineering and regular coding?
Prompt engineering shifts the developer's role from writing code directly to specifying what code should do in natural language. Instead of typing syntax, you describe intent, constraints, and desired outcomes. The AI generates the implementation, which you then review, test, and refine. This makes coding faster but requires strong code review and validation skills.
Which AI coding assistant is best for prompt engineering?
The best AI coding assistant depends on your needs:
- GitHub Copilot: Best for IDE-integrated suggestions and real-time autocomplete
- Cursor IDE: Best for full-codebase context and advanced AI-driven editing
- ChatGPT/GPT-4: Best for complex problem-solving and detailed explanations
- Claude: Best for large context windows and following detailed instructions
Most developers use multiple tools for different scenarios.
How long does it take to learn prompt engineering?
Basic prompt engineering skills can be learned in a few hours, but mastery takes weeks of deliberate practice. You'll see immediate improvements by following the PCTF framework, but becoming expert at context engineering, iterative refinement, and tool-specific techniques requires regular practice across various coding scenarios.
Can prompt engineering replace traditional coding skills?
No, prompt engineering complements traditional coding skills rather than replacing them. You still need to understand code architecture, design patterns, algorithms, and debugging to effectively guide AI assistants and validate their output. Prompt engineering makes you more productive, but strong fundamentals remain essential.
What are the most common prompt engineering mistakes?
The most common mistakes are:
- Being too vague about requirements
- Omitting important constraints or context
- Accepting AI output without review or testing
- Not iterating to improve initial results
- Forgetting to specify code style and standards
- Not providing examples when patterns matter
How do I measure my prompt engineering skills?
Measure your prompt engineering skills by tracking:
- Time to acceptable solution: How many iterations needed
- Acceptance rate: Percentage of AI suggestions you use
- Code quality: Bug rates in AI-generated vs hand-written code
- Productivity: Features delivered per week
- Developer experience: Time spent in flow state
Is prompt engineering tested in technical interviews?
Yes, as of 2025, major companies including those using HackerRank assessments now include prompt engineering questions in technical interviews. Candidates are evaluated on their ability to work effectively with AI coding assistants, craft clear prompts, and validate AI-generated code.
What's the future of prompt engineering for developers?
The future of prompt engineering is moving toward:
- Context engineering: Comprehensive project briefings for autonomous AI agents
- Prompt libraries: Shared templates and patterns across teams
- Multimodal prompting: Combining voice, visual, code, and text inputs
- AI-assisted prompt refinement: AI helping you write better prompts
- Integration into development workflows: Becoming as fundamental as version control
Sources and References
- Prompt Engineering Guide - 2025 Best Practices
- GitHub Copilot Prompt Engineering Documentation
- Lakera - Ultimate Guide to Prompt Engineering in 2025
- HackerRank - Prompt Engineering in Coding Interviews 2025
- Cursor AI Prompt Engineering Best Practices
- Medium - 7 Prompt Engineering Secrets from Cursor AI
- Claude Code Best Practices for Agentic Coding
- Siemens - Prompt Engineering for Software Developers
- AI Fire - Context Engineering & PRP for AI Coding Assistants
- Byte at a Time - 9 Lessons From Cursor's System Prompt