Development Guides
Project Structure
Best practices for organizing AI-powered applications and maintaining clean, scalable codebases.
Project Structure
Best practices for organizing AI-powered applications and maintaining clean, scalable codebases.
🚧 Coming Soon
This page is currently under development. Check back soon for comprehensive project structure guidelines.
What This Page Will Cover
- Recommended directory structures for AI projects
- Organizing models, prompts, and configurations
- Separation of concerns in AI applications
- Scalable architecture patterns
- Multi-environment configurations
Planned Sections
Directory Structure
- Standard project layout
- Source code organization
- Resource management
- Test structure
- Documentation placement
AI-Specific Organization
- Model directories
- Prompt templates
- Training data
- Evaluation datasets
- Configuration files
Module Architecture
- Core modules
- AI service layers
- Data processing pipelines
- API interfaces
- Utility functions
Configuration Management
- Environment-specific configs
- Model configurations
- API keys and secrets
- Feature flags
- Deployment settings
Best Practices
- Naming conventions
- File organization
- Dependency management
- Version control strategies
- Documentation standards
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