Best Practices
Follow these best practices to choose the right AI models, implement them effectively, and optimize performance while controlling costs.
Model Selection
Define Requirements First
- Clearly specify your use case
- Determine quality requirements
- Set performance expectations
- Establish budget constraints
- Consider integration complexity
Start Small, Scale Up
- Begin with smaller, cheaper models
- Test multiple options
- Measure actual performance
- Upgrade only when necessary
- Consider model combinations
Consider Total Cost
- Factor in development time
- Account for maintenance overhead
- Include monitoring and debugging
- Plan for scaling costs
- Evaluate support quality
Implementation
Security First
- Never expose API keys client-side
- Use environment variables
- Implement proper authentication
- Sanitize user inputs
- Monitor for abuse
Error Handling
- Handle rate limits gracefully
- Implement retry logic with backoff
- Provide meaningful error messages
- Log errors for debugging
- Have fallback strategies
Performance Optimization
- Optimize prompt length
- Use streaming for long responses
- Implement caching strategies
- Batch similar requests
- Monitor response times
Prompt Engineering
Write Clear Prompts
- Be specific and detailed
- Provide context and examples
- Use consistent formatting
- Test different phrasings
- Iterate based on results
Structure Your Prompts
- Start with clear instructions
- Provide relevant context
- Include examples when helpful
- Specify output format
- End with the specific request
Cost Management
Monitor Usage
- Track token consumption
- Monitor daily/monthly costs
- Set up usage alerts
- Analyze cost per request
- Identify expensive operations
Optimize for Cost
- Use appropriate model sizes
- Minimize unnecessary context
- Implement smart caching
- Consider batch processing
- Use cheaper models for simple tasks
Quality Assurance
Testing Strategies
- Create comprehensive test cases
- Test edge cases and failures
- Validate output quality
- Check for bias and fairness
- Monitor production performance
Continuous Improvement
- Collect user feedback
- Analyze failure cases
- A/B test different approaches
- Update prompts and models
- Stay current with new releases
Production Deployment
Reliability
- Implement health checks
- Use circuit breakers
- Have multiple provider fallbacks
- Monitor uptime and errors
- Plan for maintenance windows
Scalability
- Design for traffic spikes
- Implement request queuing
- Use load balancing
- Plan capacity based on growth
- Monitor resource usage
Privacy and Compliance
Data Handling
- Understand provider data policies
- Minimize sensitive data exposure
- Implement data retention policies
- Consider on-premise options
- Document data flows
Compliance Requirements
- Check GDPR compliance
- Verify HIPAA requirements
- Review industry standards
- Implement audit trails
- Regular compliance reviews
Staying Updated
Follow Model Updates
- Subscribe to provider newsletters
- Monitor ModelBooth updates
- Test new model versions
- Evaluate pricing changes
- Plan migration strategies
Community Engagement
- Join developer communities
- Share experiences and learnings
- Learn from others' implementations
- Contribute to open discussions
- Report issues and feedback
Common Pitfalls to Avoid
Technical Mistakes
- Exposing API keys in frontend code
- Not implementing rate limiting
- Ignoring error handling
- Over-engineering solutions
- Not testing with real data
Business Mistakes
- Underestimating integration time
- Not planning for scale
- Ignoring ongoing costs
- Vendor lock-in without alternatives
- Not measuring actual ROI