The outcome

Turn a plausible draft into work you are willing to stand behind.

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

    Restate the original outcome and acceptance criteria before reviewing the response.

  2. 02

    Mark every factual claim, calculation, quotation, code path, or image detail that could materially affect the result.

  3. 03

    Check those items against the original source, a primary reference, a test, or direct inspection.

  4. 04

    Ask the tool to list assumptions and uncertainty, but do not rely on self-review as the only check.

  5. 05

    Complete a human edit for relevance, tone, privacy, accessibility, and the exact delivery format.

Working standard

What good use looks like.

  • Verify consequential claims independently.
  • Test outputs in the environment where they will be used.
  • Keep a record of sources and review decisions.

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.