🧠Getting Started

Getting Started with the Vibe Programming Framework

Please also refer to the IMPLEMENTATION GUIDE and guides for Project Managers and System Owners.

Your Path to Responsible AI-Assisted Development

This guide provides practical steps to begin implementing the Vibe Programming Framework in your development workflow. Whether you're an individual developer, a team lead, or an organisation looking to standardise AI-assisted development practices, you'll find actionable advice to get started quickly.

Quick Start Checklist

For those who want to implement core practices immediately:

• Install a security scanning tool for reviewing AI-generated code
• Adopt the basic prompt template structure

• Implement the verification checklist for all generated code

• Create a documentation system for AI-generated components

• Establish basic refactoring guidelines

Step 1: Assess Your Current Approach

Before diving into implementation, take stock of your current AI-assisted development practices:

Self-Assessment Questions:

1. How do you currently craft prompts for AI coding assistants?

2. What verification processes do you use for AI-generated code?

3. How do you address security concerns in generated code?

4. How do you document the reasoning behind AI-generated solutions?

5. What refactoring practices do you apply to AI-generated code?

6. How does your team collaborate around AI-assisted development?

Identifying your starting point will help you prioritise which framework components to implement first.

Step 2: Begin with Structured Prompts

The foundation of effective vibe programming is well-crafted prompts. Start by adopting our structured prompt template:

CONTEXT: [Describe the system, existing components, and requirements]
TASK: [Specify exactly what the code should accomplish]
CONSTRAINTS: [List any security, performance, or style requirements]
EXAMPLES: [Provide similar code patterns from your codebase]
FORMAT: [Specify code organization, naming conventions, etc.]
OUTPUT: [Define what the response should include]

Example Implementation:

Instead of a vague prompt like:

"Write a function to validate user input"

Use a structured prompt:

CONTEXT: Building a user registration system in Python with Django

TASK: Create a function to validate user email and password inputs

CONSTRAINTS:

• Must validate email format

• Password must be 8+ chars with numbers and special chars

• Must sanitise inputs to prevent injection attacks

• Must handle errors gracefully with informative messages

EXAMPLES: We use Django validators elsewhere in our codebase

FORMAT: Follow PEP8 style guidelines with descriptive function names

OUTPUT: Function with docstrings, input validation, and error handling

This structured approach dramatically improves the quality, security, and consistency of generated code.

Step 3: Implement Verification Protocols

For every piece of AI-generated code, apply this basic verification checklist:

Mandatory Verification Checklist:

• I can explain how this code works line by line

• I understand all libraries and functions being used

• I've identified potential edge cases and error scenarios

• I've verified that security considerations are addressed

• I've checked for performance implications

• I've confirmed the code meets our style and quality standards

Start by applying this checklist to small, isolated components, then expand to larger systems as your confidence grows.

Step 4: Address Security Considerations

Security is a critical concern with AI-generated code. Implement these basic security practices:

1. Add security constraints to your prompts explicitly

2. Use automated scanning tools (like OWASP ZAP or Snyk) for all generated code

3. Create a security checklist specific to your technology stack

4. Maintain a library of security-focused prompts for common vulnerabilities

For example, when generating code that handles user input, always explicitly mention input validation, sanitization, and proper error handling in your prompts.

Step 5: Establish Documentation Practices

Documentation is essential for knowledge preservation in AI-assisted development:

1. Save effective prompts alongside the code they generated

2. Document design decisions and the reasoning behind accepted solutions

3. Create context documents explaining how AI-generated components fit into the larger system

4. Include "understanding notes" that explain complex sections in plain language

A simple documentation template might include:

• Original prompt used

• Key design decisions

• Alternative approaches considered

• Limitations and constraints

• Future improvement opportunities

Step 6: Implement Basic Refactoring Guidelines

AI-generated code often benefits from thoughtful refactoring:

1. Break large functions into smaller, focused components

2. Standardize naming conventions across generated code

3. Extract repeated logic into helper functions

4. Add or improve comments for complex sections

5. Optimize performance bottlenecks identified during verification

After generating a solution, schedule time specifically for refactoring rather than accepting the initial output as final.

Step 7: Adapt for Team Collaboration

If working in a team environment, establish these fundamental collaborative practices:

1. Create a shared prompt library accessible to all team members

2. Establish code review guidelines specific to AI-generated components

3. Pair developers for prompt creation and verification

4. Schedule knowledge-sharing sessions on effective prompting techniques

5. Document team standards for when and how to use AI assistance

First Week Implementation Plan

Here's a day-by-day plan for your first week implementing the framework:

Day 1: Setup and Assessment

• Complete the self-assessment

• Choose 1-2 priority components to implement first

• Set up any necessary tools (security scanners, documentation system)

Day 2: Prompt Engineering

• Create 3-5 structured prompts for common tasks

• Test these prompts and refine them

• Save successful prompts in a shared location

Day 3: Verification Practice

• Apply the verification checklist to recently generated code

• Document any issues found and how they could have been prevented

• Refine your verification process based on findings

Day 4: Security Focus

• Add explicit security constraints to your prompt templates

• Scan existing AI-generated code for security issues

• Create a security checklist specific to your projects

Day 5: Documentation Implementation

• Create templates for documenting AI-generated components

• Apply these templates to existing code

• Evaluate how well the documentation preserves knowledge

Days 6-7: Review and Adjust

• Review your implementation of each component

• Identify successes and challenges

• Adjust your approach based on early experiences

Common Implementation Challenges

As you begin implementing the framework, you may encounter these common challenges:

Challenge: Resistance to Structured Approaches

Some developers may prefer ad-hoc prompting for perceived speed benefits. Solution: Start with small wins, demonstrating how structured prompts actually save time by reducing iterations and rework.

Challenge: Verification Feels Time-Consuming

The verification process may initially seem to negate the speed benefits of AI assistance. Solution: Start with lightweight verification for low-risk components, and demonstrate how verification prevents costly bugs and security issues.

Challenge: Documentation Overhead

Additional documentation may appear unnecessary when code is generated quickly.

Solution: Focus on minimal, high-value documentation that directly supports maintainability and knowledge transfer.

Measuring Success

Track these metrics to gauge your successful implementation:

1. Prompt effectiveness rate: Percentage of prompts that produce usable code on first attempt

2. Verification issue detection: Number of issues caught during verification

3. Documentation completeness: Percentage of AI-generated components with proper documentation

4. Code quality metrics: Maintainability and security scores of AI-generated code before and after refactoring

5. Team confidence: Developer survey on confidence working with and maintaining AI-generated components

Next Steps

After implementing these initial practices:

1. Explore the Prompt Engineering System in depth for advanced techniques

2. Learn about Team Collaboration Models for scaling your approach

3. Discover Refactoring Strategies specific to different programming languages

4. Join our Community Forum to share experiences and learn from others