Observability in AI Systems: How to Know What Your AI Is Doing

Why AI Observability Is Different

Traditional software observability (logs, metrics, traces) is necessary but insufficient for AI systems. You also need to track:

• Prompt versions and their impact on output quality
• LLM call latency and cost per request
• Hallucination and error rates
• Token usage trends (cost management)
• Model version changes and their downstream effects
• Retrieval quality in RAG systems

The Four Observability Layers

1. Input/Output Logging

Log every LLM call: the exact prompt sent, model used, parameters, response received, latency, and token counts. This is your audit trail and debugging foundation.

2. Cost Tracking

Token cost per request × daily volume = your AI spend. Build dashboards showing cost per user, per feature, per day. Set alerts for cost anomalies.

3. Quality Metrics

Sample outputs regularly for human evaluation. Track: helpfulness score, factual accuracy rate, format compliance rate, user feedback. Without quality tracking, you don't know if your system is degrading.

4. System Metrics

Treat your AI layer like any microservice: p50/p95/p99 latency, error rates, queue depth (if async), cache hit rates for common prompts.

Tools for AI Observability

Build Observable AI Systems

Our workshop covers production AI patterns including logging, monitoring, and evaluation pipelines.