📄 Documentation & Reference Models

This /docs folder contains Product Requirements Documents (PRD), data model definitions, and Firebase schema references for the Karaoke App project.

These files are intended for:

📃 Developers reviewing the business logic and architecture.
🤖 AI tools like Cursor or Copilot that reference documentation for context-aware coding.
📝 Project planning, architecture decisions, and future enhancements.
🚀 Building the app from scratch with any framework/platform combination.

File	Purpose
`PRD.md`	Complete Product Requirements Document — platform-agnostic business logic, technical specifications, data flows, service APIs, component architecture, error handling, and performance optimizations. Self-guiding for AI implementation.
`types.ts`	Reference TypeScript interfaces used for modeling app objects. Not imported into app runtime code.
`firebase_schema.json`	Example Firebase Realtime Database structure for understanding data relationships and CRUD operations.

🚀 How to Use This Documentation

For AI-Assisted Development:

Simply say "Read this PRD" in any new chat. The PRD contains:

Self-guiding instructions for AI implementation
Complete technical specifications for 100% accuracy
Implementation questions to determine platform/framework choices
Step-by-step build process with checklists

For Human Developers:

Reference during implementation for business logic and data flows
Guide for architecture decisions and technology choices
Source of truth for all functional requirements
Migration guide when switching frameworks/platforms

For New Implementations:

Read the PRD completely - it contains comprehensive specifications
Answer implementation questions from Section 29
Follow the implementation checklist for complete build process
Preserve business logic while adapting UI to chosen framework

📋 Key Features of the Updated PRD

Platform-Agnostic Design:

Core requirements separated from implementation details
Framework-specific sections clearly marked
Migration guidance for different platforms
Toolset rationale for informed technology choices

Complete Technical Specifications:

Data flow diagrams for all operations
State management architecture with exact structure
Service layer APIs with function signatures
Component architecture with interfaces and behavior
Error handling matrix for all scenarios
Performance specifications with optimization patterns

Implementation Guide:

7 key questions to determine platform/framework
5-phase implementation checklist
Must preserve vs. can replace guidelines
Critical success factors for accurate builds

🔄 Workflow for New Versions

When Creating New Implementations:

Use the PRD as-is - it's designed for any framework/platform
Follow the implementation guide in Section 29
Preserve all business logic while adapting UI layer
Test against specifications for 100% accuracy

When Updating the PRD:

Keep platform-agnostic requirements intact
Add new implementation details to appropriate sections
Update toolset sections with new technology choices
Maintain self-guiding nature for AI implementation

When Migrating Platforms:

Keep all business logic from core requirements
Replace only implementation details in platform-specific sections
Add new platform sections following the established pattern
Update toolset rationale for new technology choices

Important Notes

✅ These files are not intended for direct import or use in application runtime.
✅ Validation logic and data models here serve as development references only.
✅ Any updates to business logic, data flow, or app architecture should be reflected here for documentation purposes.
✅ AI tools may use this information to assist with code generation but will not access /src directly.
✅ The PRD is self-guiding - it contains all instructions needed for AI implementation.
✅ 100% accuracy achievable when following the complete specifications.

🎯 Success Metrics

Zero ambiguity in technical requirements
Framework independence for easy migration
Complete implementation path from start to finish
Consistent results across different technology stacks
Self-documenting for future developers and AI tools

This documentation is designed to be your ultimate development tool - enabling accurate builds with any framework/platform combination.

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