Alerting and Incident Response for LLM Ops
Set up proactive alerting for performance issues, errors, and cost anomalies, and define incident response procedures for your LLM systems.
Why Alerting for LLM Ops?
Running Large Language Model (LLM) applications in production comes with unique challenges. Proactive alerting is key to ensuring their stability, performance, and cost efficiency.
Without alerts, you might only discover issues after users complain or costs skyrocket. Timely alerts help you detect and address problems quickly, minimizing downtime and negative impact.
Key LLM Metrics to Monitor
Unlike traditional applications, LLMs have specific metrics that need close attention. Monitoring these can reveal underlying problems:
- API Latency: How long LLM calls take.
- Error Rates: Failed API calls or bad responses.
- Token Usage: Spikes can indicate inefficient prompts or abuse.
- Cost: Direct monetary impact of LLM usage.
- RAG Retrieval Failures: When your RAG system can't find relevant context.
All lessons in this course
- Horizontal Scaling of RAG Components
- Observability: Logging, Metrics, Tracing
- Alerting and Incident Response for LLM Ops
- Load Testing and Capacity Planning