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  • Overview
  • Concepts
  • Evaluation approaches
Datasets
  • Manage datasets
Set up evaluations
Analyze experiment results
  • Analyze an experiment
  • Compare experiment results
  • Filter experiments in the UI
  • Fetch performance metrics for an experiment
  • Upload experiments run outside of LangSmith
Annotation & human feedback
  • Use annotation queues
  • Set up feedback criteria
  • Annotate traces and runs inline
  • Audit evaluator scores
Common data types
  • Example data format
  • Dataset prebuilt JSON schema types
  • Dataset transformations

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  • GitHub
  • Forum
  • Forum
Get started
Observability
Evaluation
Prompt engineering
Deployment
Agent builder
Reference
Get started
Observability
Evaluation
Prompt engineering
Deployment
Agent builder
Reference
  • GitHub
  • Forum
  • Evaluation

    Welcome to the LangSmith Evaluation documentation. The following sections help you create datasets, run evaluations, and analyze results:

    Evaluation concepts

    Review core terminology and concepts to understand how evaluations work in LangSmith.

    Manage datasets

    Create and manage datasets for evaluation through the UI or SDK.

    Run evaluations

    Evaluate your applications with different evaluators and techniques to measure quality.

    Analyze results

    View and analyze evaluation results, compare experiments, filter data, and export findings.

    Collect feedback

    Gather human feedback through annotation queues and inline annotation on outputs.

    Follow tutorials

    Learn by following step-by-step tutorials, from simple chatbots to complex agent evaluations.

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