Build a Production LLM Evaluation and Monitoring Pipeline
Set up systematic evaluation for your LLM application so you know when output quality changes, with automated tests that run on every prompt change and production monitoring that catches regressions.
Download workflow config (JSON)How the data moves
This is a handoff plan, not an automatic integration. Review outputs before the next step. Unlabeled arrows show order, not a verified data format. Use the capability links to explore tool substitutions.
Step 1
Build Your Golden Dataset
Collect 50–100 representative input-output pairs from your LLM application, both good outputs and known failure cases. This becomes your evaluation dataset in LangSmith.
Step 2
Define Your Evaluation Criteria
For each output in your dataset, write scoring rubrics: what makes a response correct, helpful, and safe for your specific use case. Load these as custom evaluators in LangSmith.
Step 3
Connect Production Tracing
Integrate LangSmith's tracing SDK into your application. Every production LLM call is now logged with full context, inputs, outputs, model used, latency, and cost.
Step 4
Track Experiments with Weights & Biases
When testing a new prompt version or model upgrade, log the experiment in Weights & Biases alongside your LangSmith eval scores. This gives you a complete picture of what changed and how it affected quality.
Step 5
Add Eval to CI/CD
Set up a GitHub Action that runs your LangSmith evaluation suite on every PR that changes a prompt or model. The action fails if quality drops below your defined baseline on any metric.
Time Required
2–3 days setup
Expected Result
An evaluation pipeline that runs automatically on every deployment, a production monitoring dashboard, and a process for catching quality regressions before users report them.
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