Introduction
Optimizing AI agent performance involves balancing speed, accuracy, and cost. This guide covers strategies for improving all three dimensions.
Response Time Optimization
1. Caching
- Cache common queries and responses
- Use edge caching for global distribution
- Implement cache invalidation strategies
2. Parallel Processing
- Process multiple queries simultaneously
- Use async/await for non-blocking operations
- Optimize database queries
3. Model Selection
- Use faster models for simple queries
- Reserve powerful models for complex tasks
- Consider model size vs. speed trade-offs
Token Usage and Cost Management
1. Prompt Optimization
- Keep prompts concise but complete
- Use few-shot examples efficiently
- Remove unnecessary context
2. Response Length Limits
- Set max token limits
- Truncate long responses
- Use streaming for long outputs
3. Model Selection
- Use cheaper models when possible
- Reserve expensive models for critical tasks
- Monitor token usage per interaction
Accuracy Improvements
1. Prompt Engineering
- Clear, specific instructions
- Examples of desired output
- Chain-of-thought reasoning
2. RAG Tuning
- Optimize chunk sizes
- Improve retrieval quality
- Fine-tune similarity thresholds
3. Feedback Loops
- Collect user feedback
- Identify common errors
- Iteratively improve prompts
Monitoring and Alerting
- Track response times
- Monitor token usage
- Measure accuracy metrics
- Set up alerts for anomalies
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