Why This Matters

LLM systems fail silently. Latency spikes, hallucinations, and broken prompts often go unnoticed until users complain. Traditional metrics like CPU or GPU utilization are not enough. You need observability that tracks the full request path and measures quality.

This post explains how to build observability for LLM systems using traces, evals, and quality SLOs.

What Is Missing Today

  • Token counts without context

  • No visibility into prompt to retrieval to inference flow

  • No automated evals for quality

  • No SLOs for correctness or safety

How to Build Real Observability

1. Trace Every Step

Capture spans for:

  • Prompt processing

  • Retrieval calls

  • Model inference

  • Post-processing

Use OpenTelemetry or similar frameworks. Add custom attributes like token count, latency per stage, and cache hits.

2. Add Quality Evals

Latency is not enough. You need correctness checks.

  • Golden datasets for regression tests

  • Automated evals for hallucination, toxicity, and relevance

  • Integrate evals into CI/CD gates

3. Define Quality SLOs

Examples:

  • p95 latency less than 500 ms

  • Hallucination rate less than 2 percent

  • Retrieval relevance score greater than 0.8

Track these in dashboards. Alert when thresholds break.

4. Close the Loop

Feed observability data back into routing and autoscaling.

  • Route traffic away from failing models

  • Scale up when latency breaches SLOs

Trade-offs to Know

  • More tracing means more overhead

  • Evals need good datasets

  • SLOs must balance cost and quality

Actionable Steps

  • Add OpenTelemetry spans for each stage

  • Build a small golden dataset for evals

  • Define 3 to 4 quality SLOs and track them

  • Wire alerts into Slack or PagerDuty

  • Feed metrics into autoscaler and router

References

  • OpenTelemetry

  • LLM Evals by OpenAI

  • From my experience: adding eval gates cut hallucination rate by 30 percent

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