Prompt Engineering System

A Structured Approach to AI-Assisted Code Generation

The Prompt Engineering System is the foundation of the Vibe Coding Framework, providing a systematic methodology for crafting effective prompts that produce high-quality, secure, and maintainable code. Rather than approaching AI assistants with ad-hoc requests, this system establishes patterns, templates, and best practices to maximize the benefits of AI-assisted development.

The S.C.A.F.F. Prompt Structure

Our system is built around the S.C.A.F.F. prompt structure—a proven format that consistently generates higher quality code outputs:

1. Situation

Establish the development context including:

  • Project background and purpose

  • Existing codebase architecture and patterns

  • Technology stack and constraints

  • Integration points with other systems

SITUATION: I'm building a Django e-commerce application with a React frontend. The
application uses token-based authentication and follows a microservices architecture. 
The current codebase follows the repository pattern and uses Django REST Framework 
for API endpoints.

2. Challenge

Clearly define the specific coding task to be accomplished:

  • The specific feature or component needed

  • Expected inputs and outputs

  • Performance requirements

  • Specific functional requirements

CHALLENGE: Create a user profile management component that allows users to update their personal information (name, email, shipping addresses) with proper validation. The component should handle form submission, display appropriate error messages, and securely communicate with the backend API.

3. Audience

Specify who will be working with this code:

  • Developer experience level

  • Team's familiarity with the technology

  • Future maintainers' expected expertise

  • Whether the code will be public or internal

AUDIENCE: The code will be maintained by a team of mid-level developers with Django experience but limited React knowledge. The codebase is expected to be maintained for at least 3 years and will eventually be handed over to the client's in-house team.

4. Format

Define the expected structure and style of the code:

  • Coding style (e.g., functional vs. class-based)

  • Naming conventions

  • Documentation requirements

  • Testing expectations

  • File structure preferences

FORMAT: Use functional React components with hooks. Follow Airbnb style guide standards. Include PropTypes for all components. Write JSDoc comments for functions. Organize code into smaller, reusable components. Include unit tests using Jest and React Testing Library.

5. Foundations

Specify security and quality requirements:

  • Security considerations specific to the task

  • Error handling expectations

  • Performance considerations

  • Accessibility requirements

  • Compliance needs

FOUNDATIONS: Implement proper input validation and sanitization. Handle all potential error states gracefully. Ensure all user interactions have appropriate loading states. Make the form accessible according to WCAG 2.1 AA standards. Implement rate limiting on submission attempts. Ensure profile updates require current password verification before saving changes.

Prompt Templates Library

Our framework includes specialised templates for common development tasks, providing pre-built S.C.A.F.F. structures for:

  • API Integration Prompts

  • Authentication Component Prompts

  • Data Processing Prompts

  • UI Component Prompts

  • Database Interaction Prompts

  • Testing Prompts

  • Security-Focused Prompts

  • Refactoring Prompts

Each template contains task-specific guidance and best practices to ensure consistent quality.

Prompt Refinement Process

Effective prompt engineering is iterative. Our system includes a structured refinement process:

  1. Initial Prompt: Begin with the S.C.A.F.F. structure for your task

  2. Analysis: Evaluate the generated code against quality criteria

  3. Clarification: Add constraints or examples to address any shortcomings

  4. Iteration: Request improvements based on specific feedback

  5. Documentation: Save successful prompts for future reuse

This process typically requires 2-3 iterations to achieve optimal results.

Example: Prompt Refinement in Action

Initial Prompt

SITUATION: Building a Node.js Express API
CHALLENGE: Create a login endpoint 
AUDIENCE: Junior developers
FORMAT: Express route handler
FOUNDATIONS: Security is important

Analysis

The initial code lacks proper input validation, error handling, and security features.

Refined Prompt

SITUATION: Building a Node.js Express API (v4.17.1) with MongoDB (v4.4) that serves a React SPA. We use JWT for authentication with bcrypt for password hashing. The codebase follows MVC pattern with service layers.
CHALLENGE: Create a login endpoint that authenticates users by email and password, returning a JWT token upon success. The endpoint should follow RESTful best practices and include proper rate limiting to prevent brute force attacks.
AUDIENCE: Junior backend developers who have basic knowledge of Express but need guidance on security best practices. Code should be well-commented to serve as a learning example.
FORMAT: Express route handler following our pattern of controller → service → repository. Use async/await with try/catch blocks for error handling. Include input validation using Joi schema. Follow our naming convention of descriptive function names with action prefix (e.g., authenticateUser, validateLoginInput).
FOUNDATIONS: Implement proper input validation with sanitization. Use parameterized queries to prevent injection. Implement brute force protection through rate limiting. Set secure and HTTP-only cookies for JWT. Follow OWASP security guidelines for authentication. Log authentication attempts without exposing sensitive information. Handle errors gracefully with appropriate status codes and messages that don't leak system details.

Result

This refined prompt produces secure, well-structured code with proper validation, error handling, and security measures—suitable for production use and accessible to junior developers.

Specialised Prompt Techniques

Beyond the basic structure, our system includes advanced techniques for specific scenarios:

1. Example-Driven Prompting

When you have specific patterns to follow:

SITUATION: ...CHALLENGE: ...AUDIENCE: ...FORMAT: ...FOUNDATIONS: ...EXAMPLES: Here's how we've implemented similar components:

// js// Example of our existing code pattern
const validateUser = async (req, res, next) => {  
    try {    // Validation logic  } 
    catch (error) {    // Error handling  }}

2. Constraint-Based Prompting

When you need to enforce specific limitations:

SITUATION: ... CHALLENGE: ... AUDIENCE: ... FORMAT: ... FOUNDATIONS: ... CONSTRAINTS:

  • Must not use any external libraries beyond what's already imported

  • Must be compatible with Internet Explorer 11

  • Must complete all database operations within 100ms

  • Must not exceed 150 lines of code

3. Test-Driven Prompting

When you want to specify behaviour through tests:

SITUATION: ... CHALLENGE: ... AUDIENCE: ... FORMAT: ... FOUNDATIONS: ... TEST CASES: The implementation should pass the following test cases:

jstest('should return 400 if email is missing', () => { ... })test('should return 401 if password is incorrect', () => { ... })test('should return 200 and token if credentials are valid', () => { ... })

Measuring Prompt Effectiveness

Our framework includes metrics to evaluate and improve prompt effectiveness:

1. First-Attempt Success Rate:

Percentage of prompts that produce usable code on the first try

2. Iteration Efficiency:

Average number of refinements needed to reach production-ready code

3. Comprehension Index:

How easily developers can understand and explain the generated code

4. Security Score:

How well the generated code adheres to security best practices

5. Maintenance Rating:

How maintainable the code remains after 3/6/12 months

Teams using our Prompt Engineering System report:

- 60-80% reduction in boilerplate coding time

- 40-50% decrease in security vulnerabilities in generated code

- 70% improvement in code consistency across team members

Integration with Development Workflows

The Prompt Engineering System integrates into existing development workflows through:

IDE Extensions

- VSCode Extension for template access

- JetBrains Plugin for prompt library

Knowledge Management

- Prompt library with tagging and search

- Team prompt sharing and version control

Continuous Improvement

- Prompt effectiveness tracking

- Community contribution of effective prompts

Getting Started with Prompt Engineering

To begin implementing the Prompt Engineering System:

1. Select or create a template for your task category

2. Fill in the S.C.A.F.F. sections with your specific details

3. Generate code and evaluate the results

4. Refine your prompt based on the initial output

5. Document your effective prompts for team reuse

For team adoption, we recommend starting with a small group pilot, focusing on non-critical components while team members develop their prompt engineering skills.

Next Steps

- Explore our [Prompt Templates Library](/prompt-templates) for pre-built structures

- Learn about [Verification Protocols](/framework-components/verification-protocols) for evaluating generated code

- Discover [Security-Focused Prompts](/prompt-templates/security-focused-prompts) for high-risk components

PreviousSecurity ToolsNextVerification Protocols

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