Part 6¶
Overview¶
Tools for tracking, debugging, and evaluating LLM applications.
| Tool | Purpose | Best For |
|---|---|---|
| Langfuse | LLM-specific observability | Production LLM apps |
| Weights & Biases | Experiment tracking | ML research/training |
| Mlflow | ML lifecycle management | Model registry & deployment |
Metrics to Track¶
Quality Metrics
- Output accuracy
- Hallucination rate
- User satisfaction
- Task completion rate
Performance Metrics
- Latency (p50, p95, p99)
- Throughput (requests/sec)
- Token generation rate
- Cost per request
System Metrics
- GPU utilization
- Memory usage
- Error rates
- Uptime
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Next: Set up monitoring for your LLM application!