Guides
In-depth guides for using Trace2 effectively
Guides
Comprehensive guides to help you get the most out of Trace2.
Available Guides
LLM Backends
Configure OpenAI, Anthropic, and other LLM providers
Best Practices
Tips and strategies for effective optimization
Guide Categories
🔧 Configuration
- LLM Backends: Set up and configure LLM providers
- API key management
- Model selection
- Cost optimization
💡 Best Practices
- Best Practices: Proven strategies for success
- Writing effective feedback
- Structuring parameters
- Optimization strategies
🐛 Troubleshooting
- Common issues and solutions
- Debugging techniques
- Performance optimization
- Error handling
🚀 Advanced Topics
- Production deployment
- Scaling considerations
- Integration patterns
- Custom optimizers
Popular Topics
Getting Better Results
- Write specific, actionable feedback
- Structure parameters appropriately
- Use the right optimizer for your task
- Monitor and iterate
Improving Performance
- Choose efficient LLM models
- Optimize feedback frequency
- Cache when possible
- Batch operations
Production Deployment
- Freeze optimized parameters
- Implement monitoring
- Version control your prompts
- Test thoroughly
Quick Tips
💡 Start Simple: Begin with basic examples before complex systems
🎯 Be Specific: Vague feedback leads to poor optimization
⚡ Iterate Fast: Quick feedback loops lead to better results
📊 Monitor Progress: Track how parameters evolve over time
🔒 Version Control: Keep track of parameter changes
Coming Soon
We're continuously adding new guides on:
- Multi-agent orchestration
- Custom optimizer implementation
- Advanced debugging techniques
- Production deployment patterns
- Cost optimization strategies
Need Help?
Can't find what you're looking for?
- Check our Examples
- Join the Discord community
- Browse GitHub Discussions
- Read the Paper