Phase 8: Evaluation, Safety & Observability · 55 min · LangSmith · Langfuse · OpenTelemetry
Production Observability: Tracing & Online Evaluation
Offline evals tell you the model can work. Observability tells you whether it IS working, right now.
Hiring signal: Closes the loop from production traces back into the eval set
What you will learn
- Instrument an LLM app with traces (spans, inputs, outputs, latency, cost, tokens)
- Choose online metrics: quality proxies, guardrail rates, business KPIs
- Run online evaluation: A/B tests, canaries, and user-feedback signals
- Detect and triage production failures from traces
- Feed real failures back into the offline eval set
The Problem
Your offline eval suite passed, you shipped, and now... silence. Real users send inputs you never imagined, a multi-step agent fails on step 3, costs creep up, and a slow tail forms — and your offline eval, frozen in the repo, can't see any of it. Observability is the production-side complement to offline evals: tracing every request, measuring quality online, and feeding real failures back into your eval set. This closed loop is what separates an LLM app that quietly degrades from one that improves with traffic — and "how do you monitor an LLM app in production?" is a senior interview staple.
The Concept
Offline eval (pre-ship) Observability (post-ship)
- curated dataset - real live traffic
- "can it work?" - "is it working now?"
- regression gate in CI - traces, online metrics, alerts
\________ feedback loop: real failures -> new eval cases ________/
The two halves are one system: production reveals failures, the eval set captures them so they never regress again.
A user reports your LLM gave a harmful response. You fix the prompt. What must you do to prevent this from regressing in the future?
Every production failure is a future eval case. Add the input, the expected safe response, and the assertion (e.g., "must not contain X") to your eval suite. Run the full suite in CI before deploying. This creates a feedback loop: production failures → eval cases → regression gates → never the same bug twice.
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You've read the first 2 sections. The rest of this lesson covers Build It, A/B Test: [feature name], Use It, Ship It, Evaluation, Exercises, Key Terms, Common Pitfalls, Interview Framing — plus a hands-on lab, quiz, and project artifact.
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