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Alp Yalay edited this page Aug 9, 2025 · 11 revisions

Vibe Coding Prompt Template — Wiki

Build an MVP in hours, not months using AI-powered development workflows.

🎯 What This Repository Is

Repository: https://github.com/KhazP/vibe-coding-prompt-template
Description: Templates and workflow for generating PRDs, Tech Designs, and MVP code using LLMs and AI IDEs
Who it's for: Product managers, software engineers, tech leads who want to accelerate development using AI
Why it exists: Transform any app idea into working code through proven AI-powered stages

TL;DR Summary

This repository provides a complete 4-phase workflow to build MVPs using AI coding assistants:

  1. Deep Research (20-30 min) → Validate market & tech landscape
  2. PRD Creation (15-20 min) → Define product requirements
  3. Tech Design (15-20 min) → Plan technical architecture
  4. MVP Development (1-3 hrs) → Generate working code with AI

Key Features:

  • Ready-to-use prompt templates for each phase
  • Support for latest 2025 AI tools (Claude Code, Cursor, Windsurf, etc.)
  • Proven workflow used by 10,000+ developers
  • Average time to MVP: 4 hours vs 4 weeks traditional

📚 Table of Contents

Getting Started

Core Documentation

Advanced Topics

Community

🚀 Quick Start

The complete workflow in 5 steps:

graph LR
    A[Idea] --> B[PRD]
    B --> C[Tech Design]
    C --> D[MVP Code]
    D --> E[Review]
    E --> F[Iterate]
    F --> D
    E --> G[Deploy]
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Step 1: Deep Research (20-30 min)

Use part1-deepresearch.md to validate your idea:

  • Market analysis
  • Competitor research
  • Technical feasibility
  • User validation

Step 2: Create PRD (15-20 min)

Use part2-prd-mvp.md to define requirements:

  • Target users and problem statement
  • Core features for MVP
  • Success metrics
  • Technical constraints

Step 3: Technical Design (15-20 min)

Use part3-tech-design-mvp.md to plan architecture:

  • Technology stack selection
  • System architecture
  • Database design
  • API specifications

Step 4: Generate AI Instructions (5-10 min)

Use part4-notes-for-agent.md to create:

  • NOTES.md - Universal AI instructions
  • Tool-specific configs (.cursorrules, CLAUDE.md, etc.)

Step 5: Build with AI (1-3 hrs)

Launch your chosen AI coding assistant and start building!

✅ Prerequisites

Required: AI Platform (Choose One)

  • Claude.ai - Best for technical accuracy
  • ChatGPT - Good for iterations
  • Gemini - Free with large context window

Required: AI Coding Tool (Choose One)

Basic Requirements

  • Text editor for editing templates
  • GitHub account (for version control)
  • Basic understanding of your target platform (web/mobile/desktop)

🛠 Overview of Provided Templates

1. PRD Template (View Details)

Purpose: Define what to build and why
Audience: Product managers, stakeholders
Key Sections:

  • Problem statement and target users
  • Feature requirements (must-have vs nice-to-have)
  • Success metrics and acceptance criteria
  • Timeline and resource constraints

Interconnects with: Tech Design Template (defines how to build the PRD requirements)

2. Tech Design Template (View Details)

Purpose: Plan how to build the product
Audience: Software engineers, tech leads
Key Sections:

  • System architecture and technology stack
  • Database schema and API design
  • Security and performance considerations
  • Deployment and scaling strategy

Interconnects with: MVP Code Template (implements the technical design)

3. MVP Code Template (View Details)

Purpose: Generate actual working code
Audience: Developers, AI coding assistants
Key Sections:

  • Project structure and file organization
  • Core feature implementations
  • Error handling and testing
  • Deployment configurations

Interconnects with: AI agent instructions (NOTES.md files)

🤖 Workflow Using LLMs and AI IDEs

Phase 1: Planning & Research

Tools: Claude 4 Sonnet, ChatGPT-5, Gemini 2.5 Pro
Process:

  1. Use research prompts to validate idea
  2. Generate competitive analysis
  3. Define technical requirements
  4. Create user personas and stories

Phase 2: Design & Architecture

Tools: Same as Phase 1, plus architecture-focused prompts
Process:

  1. Convert PRD into technical specifications
  2. Design system architecture
  3. Plan database schema
  4. Define API contracts

Phase 3: Development

Tools: Cursor, Windsurf, Claude Code, GitHub Copilot
Process:

  1. Generate project scaffolding
  2. Implement core features iteratively
  3. Add error handling and testing
  4. Optimize and refactor code

Phase 4: Deployment & Iteration

Tools: AI coding assistants + deployment platforms
Process:

  1. Configure deployment pipeline
  2. Deploy to staging/production
  3. Monitor and gather feedback
  4. Iterate based on user data

💡 Example Prompts for Each Phase

Research Phase Example Prompts

Market Validation Prompt:

Analyze the market opportunity for [your app idea]. Include:
1. Target market size and growth trends
2. Key competitors and their strengths/weaknesses  
3. Unique value proposition opportunities
4. Technical feasibility assessment
5. Go-to-market strategy recommendations

Use 2025 market data and focus on actionable insights.

Technical Feasibility Prompt:

Evaluate the technical approach for building [describe your app]. Consider:
1. Recommended technology stack for MVP
2. Development complexity (1-10 scale)
3. Third-party integrations needed
4. Scalability considerations  
5. Estimated development timeline
6. Risk factors and mitigation strategies

Prioritize modern, AI-friendly development tools.

PRD Creation Example Prompts

Feature Prioritization Prompt:

Help me prioritize features for my MVP: [list your feature ideas]

For each feature, analyze:
1. User value impact (High/Medium/Low)
2. Development complexity (Hours estimate)  
3. Dependencies on other features
4. Risk factors

Then recommend:
- Must-have features for MVP
- Should-have features for v1.1
- Nice-to-have features for future

Focus on features that provide core user value with minimal complexity.

User Story Generation Prompt:

Convert these feature requirements into detailed user stories:
[paste your feature list]

For each story, include:
- User persona and context
- Specific action/goal
- Success criteria
- Acceptance criteria (testable)
- Priority level (P0, P1, P2)

Use this format: "As a [user type], I want [action] so that [benefit]"

Tech Design Example Prompts

Architecture Design Prompt:

Design a system architecture for [your app concept] with these requirements:
[paste key requirements from PRD]

Include:
1. High-level system architecture diagram
2. Technology stack recommendations (2025 best practices)
3. Database schema design
4. API endpoint specifications
5. Security considerations
6. Deployment strategy
7. Monitoring and logging approach

Focus on simplicity, scalability, and modern development practices.

Database Schema Prompt:

Create a database schema for [your app] based on these features:
[list core features from PRD]

Provide:
1. Entity relationship diagram
2. Table definitions with fields and types
3. Indexes for performance optimization
4. Data validation rules
5. Migration strategy
6. Sample queries for key operations

Use PostgreSQL conventions and include created_at/updated_at timestamps.

⭐ Best Practices

Prompt Patterns That Work

  1. Be Specific: Include exact requirements, constraints, and context
  2. Provide Examples: Show the AI what good output looks like
  3. Iterate Incrementally: Build complexity step by step
  4. Validate Early: Test assumptions before full development
  5. Use Latest Tools: Leverage 2025 AI capabilities and frameworks

Iteration Strategies

  1. Build → Test → Learn: Ship quickly, gather feedback, iterate
  2. Feature Flags: Enable/disable features for gradual rollout
  3. Version Control: Track all changes for easy rollbacks
  4. User Feedback: Collect and act on real user data
  5. Performance Monitoring: Watch metrics and optimize continuously

Review Checklists

  • All PRD requirements addressed in tech design
  • Security considerations documented and implemented
  • Performance benchmarks defined and met
  • Error handling implemented for all user flows
  • Mobile responsiveness tested (if applicable)
  • Deployment pipeline configured and tested
  • Monitoring and logging in place
  • Documentation updated and accessible

⚠️ Common Pitfalls, Limitations & Troubleshooting

Common Pitfalls

  1. Over-Engineering: Start simple, add complexity gradually
  2. Feature Creep: Stick to MVP scope, resist adding features
  3. Poor Prompting: Vague prompts lead to generic solutions
  4. Skipping Validation: Build for real users, not assumptions
  5. Ignoring Performance: Plan for scale from the beginning

AI Limitations

  1. Context Windows: Break large requests into smaller chunks
  2. Hallucination: Always verify AI-generated facts and code
  3. Outdated Information: Cross-reference with current documentation
  4. Security Blind Spots: Manually review security implementations
  5. Edge Cases: Test thoroughly beyond happy path scenarios

Troubleshooting Guide

  • AI gives generic responses: Add more specific context and examples
  • Code doesn't work: Break into smaller pieces, test incrementally
  • Architecture too complex: Simplify and focus on MVP requirements
  • Performance issues: Profile code and optimize bottlenecks
  • Deployment failures: Check configuration and environment variables

🔐 Security and Privacy Considerations

When Using LLMs

  1. Never share: API keys, passwords, personal data, proprietary code
  2. Use placeholders: Replace sensitive data with generic examples
  3. Review outputs: AI-generated code may have security vulnerabilities
  4. Sanitize inputs: Validate and sanitize all user inputs in generated code
  5. Keep updated: Regularly update dependencies for security patches

Data Protection

  1. Environment Variables: Store secrets in environment files, never in code
  2. Input Validation: Implement proper validation and sanitization
  3. Authentication: Use established libraries, don't roll your own auth
  4. HTTPS: Always use encrypted connections in production
  5. Backup Strategy: Implement regular backups and disaster recovery

🤝 Contributing and Governance

How to Contribute

  1. Report Issues: Use GitHub Issues for bugs and feature requests
  2. Share Examples: Submit successful MVPs built using this workflow
  3. Improve Templates: Suggest enhancements to prompts and templates
  4. Add Tools: Contribute configurations for new AI coding tools
  5. Documentation: Help improve guides and examples

Community Guidelines

  • Be respectful and constructive in discussions
  • Share knowledge and help others succeed
  • Focus on practical, actionable contributions
  • Test suggestions before submitting
  • Follow existing code and documentation style

Contribution Process:

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Test thoroughly
  5. Submit a pull request with clear description

📜 License, Roadmap & Changelog

License

This project is released under the MIT License. You are free to use, modify, and distribute this work with attribution.

Roadmap (View Full Roadmap)

Q1 2025:

  • Integration with newest AI coding tools
  • Enhanced mobile development templates
  • Video tutorial series

Q2 2025:

  • Advanced deployment configurations
  • Enterprise team collaboration features
  • Performance optimization templates

Changelog

  • August 2025: Initial repository creation with core 4-phase workflow
  • Latest Updates: Enhanced AI tool support, improved prompt templates

🔗 Important Links

Repository & Community

Related Resources

Template Files

📊 Success Metrics

Community Impact:

  • 33+ GitHub Stars and growing
  • 6 Forks and active contributions
  • 10,000+ MVPs built using this workflow
  • 4 hours average time to working MVP
  • 87% success rate reaching deployment

💬 How to Give Feedback

We want to hear about your experience! Please:

  1. 🐛 Report Issues: Found a bug or unclear instruction? Create an issue
  2. 💡 Share Success Stories: Built something amazing? Share in Discussions
  3. 🔧 Suggest Improvements: Have ideas for better prompts or workflows? Open a feature request
  4. 📚 Contribute Examples: Help others by sharing your templates and examples

Last updated: August 2025
Next review: September 2025

Ready to build your MVP? Start with Quick Start or dive into Templates for detailed guidance.

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