6.1 Monitoring and Governance
Implementing robust monitoring and governance practices is essential for maintaining control over GenAI usage and costs.Key Aspects of Monitoring and Governance
- Usage Tracking: Monitor the number of API calls, token usage, and associated costs for each model and application.
- Performance Metrics: Track response times, error rates, and model accuracy to ensure quality of service.
- Cost Allocation: Implement systems to attribute costs to specific projects, teams, or business units.
- Alerting: Set up alerts for unusual spikes in usage or costs to quickly identify and address issues.
- Compliance Monitoring: Ensure that AI usage adheres to regulatory requirements and internal policies.
Implementation Example
Here’s a basic example using Prometheus and Flask for monitoring:6.2 Caching Strategies
Implementing effective caching strategies can significantly reduce API calls and associated costs in LLM applications.Types of Caching
- Result Caching: Store and reuse results for identical queries.
- Semantic Caching: Cache results for semantically similar queries.
- Partial Result Caching: Cache intermediate results for complex queries.
Implementing a Semantic Cache
Here’s a basic example of implementing a semantic cache:6.3 Automated Model Selection and Routing
Implementing an automated system for model selection and routing can optimize cost and performance based on the specific requirements of each query.Key Components
- Query Classifier: Categorize incoming queries based on complexity, domain, etc.
- Model Selector: Choose the appropriate model based on the query classification.
- Performance Monitor: Track the performance of selected models for continuous improvement.