# Documentation Requirements

## Documentation Standards
Maintain comprehensive documentation that supports both developers and AI assistants working on this project.

### Update Requirements
- **README.md**: Update when adding significant functionality or changing project scope
- **AI Configuration Files**: Keep all AI assistant configuration files current as project evolves
- **Environment Documentation**: Maintain `.env.example` with all required variables documented
- **API Documentation**: Document all AI service integrations and expected responses

### AI Service Documentation
When integrating AI services, document:

#### Service Configuration
```markdown
## OpenAI Integration
- **Model**: GPT-4 (configurable via environment)
- **Rate Limits**: 500 requests/hour per API key
- **Required Environment Variables**:
  - `OPENAI_API_KEY`: Your OpenAI API key
  - `OPENAI_MODEL`: Model to use (default: gpt-4)
  - `OPENAI_MAX_TOKENS`: Maximum tokens per request (default: 2048)
```

#### Usage Examples
Always include practical examples:
```javascript
// Example: Basic AI chat completion
const completion = await openai.chat.completions.create({
  model: process.env.OPENAI_MODEL || "gpt-4",
  messages: [
    { role: "system", content: "You are a helpful AI assistant." },
    { role: "user", content: userPrompt }
  ],
  max_tokens: parseInt(process.env.OPENAI_MAX_TOKENS) || 2048
});
```

#### Error Scenarios
Document common error patterns and handling:
```markdown
### Common Errors
- **401 Unauthorized**: Invalid API key or expired credentials
- **429 Rate Limited**: Too many requests, implement exponential backoff
- **500 Server Error**: AI service temporarily unavailable
- **Context Length Exceeded**: Input too long for model context window
```

## Code Documentation
- Use JSDoc comments for complex AI algorithms and integrations
- Document function parameters and return types for AI service calls
- Include examples in code comments for non-obvious AI implementations
- Explain AI model choice rationale and configuration decisions

## Changelog Maintenance
When adding AI functionality, update documentation with:
- New AI service integrations
- Model version changes or updates
- API endpoint modifications
- Breaking changes to AI interfaces
- Performance improvements or optimizations

## Architecture Decision Records (ADRs)
For major AI-related decisions, create ADRs documenting:
- Why specific AI models or services were chosen
- Trade-offs between different AI providers
- Performance and cost considerations
- Security and privacy implications

## Team Knowledge Sharing
- Document AI service setup steps for new team members
- Include troubleshooting guides for common AI service issues
- Maintain examples of successful AI integration patterns
- Share lessons learned from AI service limitations and workarounds