LLMOps Observability
Monitoring, debugging, and ensuring quality for LLM-based applications.
Why LLM Observability?
LLM systems require specialized observability beyond traditional application monitoring. You need to track not only latency and errors, but also output quality, hallucinations, costs, and drift.
💡 Key Insight: LLM outputs are non-deterministic. The same input can produce different outputs, making traditional testing insufficient. Continuous monitoring is essential.
📊 Key Metrics to Monitor
| Category | Metrics | Why It Matters |
|---|---|---|
| Performance | Latency, throughput, TTFT | User experience, SLA compliance |
| Cost | Tokens in/out, cost per request | Budget management, optimization |
| Quality | Groundedness, faithfulness, relevance | Output reliability, trust |
| Safety | Hallucination rate, toxicity, PII | Risk mitigation, compliance |
🚨 Drift Detection
Monitor changes in input distributions and output quality over time.
📥 Input Drift
User queries or context data changes over time. Monitor topic distribution and query patterns.
📊 RAG Drift
Knowledge base becomes stale. Monitor retrieval quality and index freshness.
👻 Hallucination Monitoring
Detect when LLMs generate false or unsupported information.
Groundedness Check
Verify that responses are supported by the provided context or retrieved documents.
Factuality Evaluation
Check claims against trusted sources or knowledge bases.
LLM-as-Judge
Use another LLM to evaluate response quality and flag potential issues.