How to Monitor LLMs in Production

Production LLM monitoring requires tracking different metrics than traditional applications. Here's how to set up comprehensive observability for AI systems.

Step 1: Instrument Your Application

Capture key data points for every LLM interaction:

// Example with DriftRail SDK
await client.ingest({
  model: 'gpt-4',
  provider: 'openai',
  input: {
    prompt: userQuery,
    retrievedSources: ragDocuments
  },
  output: { text: llmResponse },
  metadata: {
    latencyMs: responseTime,
    tokensIn: inputTokens,
    tokensOut: outputTokens
  }
});

Step 2: Define Key Metrics

Performance Metrics

• Latency (p50, p95, p99)
• Throughput (requests/second)
• Token usage and costs
• Error rates

Quality Metrics

• Hallucination rate
• Confidence scores
• User feedback/ratings
• Task completion rate

Safety Metrics

• Toxicity detection rate
• PII exposure incidents
• Policy violation rate
• Prompt injection attempts

Step 3: Set Up Alerts

Configure alerts for critical thresholds:

• Latency: Alert when p95 exceeds 2 seconds
• Error rate: Alert when errors exceed 1%
• Safety: Immediate alert on high-risk classifications
• Cost: Alert when daily spend exceeds budget

Step 4: Create Dashboards

Build dashboards for different stakeholders:

• Engineering: Latency, errors, throughput
• Product: Quality metrics, user feedback
• Compliance: Safety metrics, audit logs
• Finance: Token usage, cost trends

Step 5: Implement Continuous Improvement

• Review flagged outputs regularly
• Track metrics over time for trends
• A/B test prompt changes
• Update guardrails based on findings

FAQ