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Guide: Prompt Engineering for Developers

Master prompts for Lovable, Bolt, and Cursor. CRISP frameworks, advanced techniques, and 15+ ready-to-use templates for vibecoding.

Guide: Prompt Engineering for Developers

Complete Guide: Prompt Engineering for Developers Using Vibecoding

Prompt engineering has become an essential skill for every developer using modern vibecoding tools. Whether you're using Lovable, Bolt, or Cursor, your ability to formulate precise and contextualized instructions directly determines the quality of the generated code.

In this comprehensive guide, we'll explore proven frameworks, advanced techniques, and provide a library of 15+ prompt templates you can use immediately in your projects.

Why Prompt Engineering Matters for Developers

Vibecoding tools like Lovable, Bolt, and Cursor rely on powerful AI models. However, the quality of the output depends directly on the quality of your input.

A poor prompt can generate:

  • Inefficient or unoptimized code
  • Solutions that don't respect your architecture
  • Incomplete implementations without tests
  • Code without documentation

A good prompt, conversely, produces:

  • Production-ready code
  • Solutions aligned with your tech stack
  • Complete implementations with tests and documentation
  • Optimized performance
Pro Tip: Spending 5 minutes crafting a good prompt saves 20 minutes of rework and debugging.

CRISP Framework for Effective Prompts

The CRISP framework is a structured approach to writing prompts that consistently generate better results:

C - Context

Provide the context of your project. The AI needs to understand the environment where the code will run.

Example:

I'm developing a SaaS task management application with:
- Frontend: React 18 + TypeScript
- Backend: Node.js + Express
- Database: PostgreSQL
- Authentication: JWT

R - Role

Define the role you want the AI to play. This affects the tone and level of detail.

Example:

Act as a senior fullstack developer with 10 years of experience
in building scalable systems.

I - Input

Be precise about what you're asking. Include all relevant details.

Example:

I need to implement a REST API endpoint to retrieve tasks
filtered by status. Users must be authenticated and only see
their own tasks.

S - Specificity

Detail exactly what you expect as output.

Example:

Provide:
1. Express controller with error handling
2. Input validations using Joi
3. Unit tests with Jest
4. JSDoc API documentation

P - Parameters

Define constraints and preferences.

Example:

Preferences:
- Respect existing project architecture
- Use existing authentication middleware
- Performance: must respond in < 200ms
- Pagination: maximum 50 items per page

Advanced Role-Context-Task Framework

For more complex tasks, use this hierarchical structure:

ROLE:
You are an expert in [specific domain] with [years] of experience.

CONTEXT:
Our system uses [technologies].
The constraints are [list of limitations].
The overall project objective is [objective].

MAIN TASK:
Perform [main action].

SPECIFIC DETAILS:
- Consider [parameter 1]
- Ensure [constraint 2]
- Include [element 3]

SUCCESS CRITERIA:
- [Criterion 1]
- [Criterion 2]
- [Criterion 3]

OUTPUT FORMAT:
[Specify expected format]

Advanced Prompt Engineering Techniques

1. Chain of Thought (Step-by-Step Reasoning)

This technique forces the AI to explain its reasoning before generating code:

Before generating code, explain your approach:
1. What problem are we solving?
2. What are the performance considerations?
3. What edge cases do we need to handle?
4. What will the overall architecture be?

THEN provide the complete code.

2. Few-Shot Learning (Learning by Examples)

Providing examples of the expected code style improves consistency:

Here's an example of how we structure our services:

Example 1 - User Service:
[code example]

Now, create a similar service for tasks using the same
structure and naming conventions.

3. System Prompts

With Cursor and other tools, define system prompts that guide all interactions:

You are a coding assistant for a fullstack developer team.
- Always use TypeScript rather than JavaScript
- Provide production-ready code with error handling
- Always include unit tests
- Document your code with clear JSDoc comments
- Respect the hexagonal architecture
- Maximize performance and security

4. Prompt Chaining

Divide complex tasks into sequential prompts:

STEP 1: Analyze the requirements
STEP 2: Propose an architecture
STEP 3: Implement the main component
STEP 4: Add tests
STEP 5: Document the API

Library of 15+ Prompt Templates

Templates for UI Components

1. Reusable React Component

I want to create a reusable React component called [ComponentName].

Specifications:
- Props: [list of props with types]
- Styling: [CSS/TailwindCSS]
- Responsiveness: Mobile-first, optimized for [breakpoints]
- Accessibility: WCAG 2.1 AA
- Used in [usage context]

Provide:
1. TypeScript component with full types
2. Storybook story for testing variants
3. Accessibility tests
4. Usage documentation

2. Validated Form

Create a React form for [objective].

Required fields:
- [Field 1]: [type] [validation]
- [Field 2]: [type] [validation]

Requirements:
- Real-time client-side validation
- Elegant error handling
- Loading and error states
- Integration with [API endpoint]
- Complete WCAG accessibility

Include: main component, custom hook, tests

3. Accessible Modal/Dialog

Implement a Modal component that:
- Respects ARIA accessibility patterns
- Handles focus correctly
- Supports keyboard escape
- Has a confirmation system
- Works perfectly on mobile

Use: [library: radix-ui/headless-ui]
Style: [TailwindCSS/CSS-in-JS]

Templates for Backend and APIs

4. Complete REST API Endpoint

Create a POST /api/[resource] REST API endpoint that:
- Authentication: JWT Bearer token
- Validation: [validation specifications]
- Business logic: [description]
- Error handling: appropriate HTTP codes
- Logging: all important operations
- Rate limiting: [limits]

Provide:
1. Controller/route with error handling
2. Service with business logic
3. Data validation (Joi/Zod)
4. Integration tests
5. OpenAPI/Swagger documentation

5. Database Service

Create a TypeScript service to manage [entity] in PostgreSQL:
- Complete CRUD operations
- Transactions for multi-table operations
- Query optimization (indexes, eager loading)
- Specific error handling
- Soft deletes if applicable

Stack: [ORM: Prisma/TypeORM], [QueryBuilder]
Include: tests, migrations, TypeScript types

6. Authentication Middleware

Implement a JWT authentication middleware for [framework]:
- Token extraction from [location: headers/cookies]
- Validation and decoding
- Expired token handling
- Automatic token refresh
- Failed attempt logging
- Security tests

Requirements: [secrets management], [CORS if applicable]

Templates for Testing

7. Unit Test Suite

Create a complete Jest test suite for [function/class]:
- Standard success cases
- Edge cases and boundary conditions
- Error handling
- Mocks and stubs if necessary
- Coverage > 90%
- Performance tests if relevant

Function: [provided code or description]

8. Integration Tests

Write integration tests for [feature]:
- Test database setup
- Test data seeds
- Complete flow tests: request → logic → response
- Cleanup after each test
- Test transaction handling

Framework: [Jest/Vitest], DB: [PostgreSQL/etc]
Include: fixtures, helpers, teardown

9. E2E Tests with Cypress/Playwright

Create E2E tests for the user scenario:
1. [Step 1 of scenario]
2. [Step 2 of scenario]
3. [Step 3 of scenario]

Include:
- Robust selectors (data-testid preferred)
- Explicit waits (no fixed waits)
- Screenshot on error
- Cross-browser tests if relevant

Templates for Optimization and DevOps

10. Performance Optimization

Optimize this function/component for performance:

Current code:
[code]

Analysis:
- Identify bottlenecks
- Measure impact of each optimization
- Propose alternatives

Constraints:
- Maintain readability
- Stay compatible with [Node/browser version]
- No additional external dependencies

Provide: optimized code, explanations, benchmarks

11. CI/CD Pipeline

Create a [GitHub Actions/GitLab CI] pipeline for:
- Automated tests (unit, integration, E2E)
- Linting and code formatting
- Security: dependency scanning
- Build and optimization
- Automatic deployment to [environment]

Stack: [technologies used]
Triggers: [PR, commit, tags]

12. Docker Configuration

Create an optimized Dockerfile for a [type] app:
- Multi-stage build to reduce size
- Production-ready with security best practices
- Environment variables
- Health checks
- Logs directed to stdout

Stack: [Node/Python/etc]
Target image size: [limit]

Templates for Documentation and Architecture

13. Complete API Documentation

Generate OpenAPI/Swagger documentation for these endpoints:
[list of endpoints]

Include for each endpoint:
- Clear description
- Parameters with types and examples
- Responses (success and errors)
- Authentication codes
- Rate limiting
- cURL and JavaScript examples

Format: OpenAPI 3.0 in YAML

14. Architecture and Design Patterns

Propose a [feature] architecture for:
- [Constraint 1]
- [Constraint 2]
- [Constraint 3]

Include:
- Textual structure diagram
- Explanation of choices
- Justification of patterns used
- Scalability and maintenance considerations

15. README and Setup Guide

Create a complete README for [project] including:
- Project description
- Main features
- Tech stack
- Installation instructions
- Required environment variables
- Development commands
- Folder structure
- Contribution guide
- Common troubleshooting

Audience: new contributors
Language: [EN/FR as per project]

Tool-Specific Tips

Lovable

Lovable excels at rapid interfaces. Use visual prompts:

Create an interface for [objective].

Design:
- Modern and clean, inspired by [design reference]
- Colors: [specified palette]
- Responsive: desktop, tablet, mobile
- Dark mode supported

Features:
- [List of interactions]

Dependencies: [React, TailwindCSS, etc]
Lovable loves detailed visual descriptions. Talk about layout, colors, and typography. The more specific you are about the design, the better the result.

Bolt

Bolt is perfect for fullstack applications. Provide complete context:

Create a fullstack application for [objective]:

Frontend:
- Framework: React
- Style: TailwindCSS
- State management: [option]
- Pages: [list]

Backend (optional if Node.js):
- API endpoints: [specifications]
- Database: [type]

Main features:
1. [Feature 1]
2. [Feature 2]
3. [Feature 3]

Cursor

Cursor as an editor benefits from maintained system directives:

// In .cursor/rules or system prompt

You are a coding assistant for this specific project.
Conventions:
- File names: kebab-case
- Variables/functions: camelCase
- Classes: PascalCase
- Constants: UPPER_SNAKE_CASE

Code style:
- Use strict TypeScript types
- Include JSDoc for public functions
- Prefer functional components
- Export types with implementations

Before writing code:
1. Explain your approach
2. Identify dependencies
3. Explain architectural choices

Advanced Patterns

Pattern 1: Iterative Generation

Don't ask for everything at once. Build progressively:

ITERATION 1:
Generate the basic structure of [Component].

ITERATION 2:
Add state logic and handlers.

ITERATION 3:
Add validations and error handling.

ITERATION 4:
Add tests and documentation.

Pattern 2: Prompt with File Context

Include portions of existing code:

Based on this existing service:
[service code]

Create a similar service for [new entity] respecting
the same patterns, naming conventions, and error structure.

Pattern 3: Critique and Improvement

Ask the AI to critique its own code:

Here's the proposed code:
[code]

Before concluding:
1. Critique this code: what are potential issues?
2. What edge cases are missing?
3. How can we improve performance?
4. Propose an optimized version of the code.

Common Mistakes to Avoid

  1. Too vague prompts
    • ❌ "Create an API"
    • ✅ "Create a REST API GET /api/users?status=active with pagination, filtering, and tests"
  2. Lack of technical context
    • ❌ "Make a login component"
    • ✅ "React login component with real-time validation, JWT integration, and accessibility tests"
  3. No constraints
    • ❌ "Optimize this code"
    • ✅ "Optimize this code: < 200ms response, no new dependencies, < 50KB bundle"
  4. Prompts that are too long
    • ✅ Use CRISP structure (150-300 words)
    • ❌ Don't write confusing walls of text
  5. Don't specify output format
    • ❌ "Document this API"
    • ✅ "Create OpenAPI 3.0 documentation in YAML for this API"

Checklist for Effective Prompts

Before submitting a prompt, verify:

  • Clear context: Tech stack and architecture known?
  • Role defined: What role does the AI play for this prompt?
  • Specific task: What exactly do you need to deliver?
  • Crucial details: Constraints, limitations, non-functional requirements?
  • Expected format: Code, documentation, diagrams?
  • Success criteria: How do you measure success?
  • Examples provided: Have you shown the expected style/pattern?

To deepen your mastery of vibecoding:

For a complete introduction to vibecoding, check our Vibecoding Guide.

Conclusion

Prompt engineering is not an inexact science; it's a skill that develops with practice. The CRISP and Role-Context-Task frameworks give you a solid foundation. Advanced techniques like Chain of Thought and Few-Shot Learning refine your results.

Start with the provided templates, adapt them to your specific use cases, and build your own library of reusable prompts. The better your prompts, the better your generated code, and the faster your development.


About the Author

Idlen helps developers master vibecoding and modern AI tools. Join our community on vibecoding for guides, templates, and regular updates on prompt engineering best practices.

Do you have specific questions about prompt engineering? Check our Vibecoding FAQ or engage directly with our team.

Ready to master vibecoding? Start now with Lovable, Bolt, or Cursor and apply these techniques to your own projects.