LLMOps & Monitoring

Act 4 · ~5 min

Theory

LLMOps extends the MLOps stack with three concerns unique to language model systems.

Pillar	What it covers	Example tool
Prompt monitoring	Log traces; track judge score, latency, tokens	Comet ML Opik
Guardrails	Block injection/PII in; filter PII/hallucination out	Pydantic validators
Continuous training	Drift-triggered retrain → eval → promote	ZenML pipeline

LLM lifecycle:

Input guardrailblock injection · PII · length

Inferencemodel generates · tokens logged

Output guardrailvalidate format · filter PII

Monitorjudge score · latency · drift alert

CT pipelinecollect traces · fine-tune · promote

LLMOps production lifecycle — from request to continuous improvement.

CI/CD additions for LLM systems:

Prompt templates versioned in git; changes require passing an eval suite before deploy
Model promotion gated on judge score above threshold (4.0 / 5 in LLM Twin)
Alerts fire below 3.5 avg score (1-hour window) or above p95 latency of 5000ms