The outcome

Learn the interaction loop using a small task with a clear outcome.

LangSmith is a LLM application platform for tracing, observability, offline and online evaluation, prompt versioning, feedback, and agent deployment. Tracing agent and LLM runs, investigating failures, comparing prompts and models on datasets, collecting feedback, monitoring production quality, and promoting tested versions. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Define the application boundary, owners, sensitive fields, environments, trace sampling, retention, access, quality metrics, cost limits, and incident response before enabling tracing.

  2. 02

    Instrument a non-production environment with a scoped key, redact secrets and personal data, attach version metadata, and confirm exactly which inputs, outputs, tools, and errors are captured.

  3. 03

    Create a curated dataset from authorized cases, define code, human, and carefully calibrated model evaluators, then compare a baseline and candidate under identical conditions.

  4. 04

    Inspect disagreements and failure clusters rather than averages alone, set release thresholds, test deletion and tracing disablement, and promote only a documented approved version.

Working standard

What good use looks like.

  • State the outcome before the background.
  • Provide the real source material.
  • Review the result before expanding the task.

Tracing may capture prompts, responses, tool arguments, retrieved documents, personal data, secrets, or proprietary content. Retention differs by tier and datasets can outlive source traces. LLM-as-judge scores are model opinions and require calibration against human decisions. Use least-privilege keys and workspaces, conditional tracing, redaction, explicit retention, deletion tests, evaluator versioning, spend limits, and human review before treating a dashboard score as a release decision.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.