Purpose

LangSmith traces, evaluates, and monitors model applications, with or without LangChain. A useful trace captures prompts, models, tools, latency, and errors— but not secrets or unnecessary personal data.

Evaluation workflow

1. Build a reviewed dataset of representative inputs.
2. Run a fixed application version against it.
3. Score with deterministic checks, humans, or calibrated model evaluators.
4. Compare task success, critical slices, latency, errors, and cost.

Model-based evaluators are useful for fuzzy criteria, but they are measurements, not ground truth. Calibrate them against human labels and inspect disagreements.

Operational use

• Tag traces with application version, prompt version, model, and environment.
• Sample high-volume traffic while retaining failures and unusual paths.
• Turn repeated production failures into regression examples.
• Define retention and redaction policies before sending trace payloads.

Further reading: LangSmith evaluation concepts and observability concepts.