Evaluation how-to guides

Step-by-step guides that cover key tasks and operations for doing evaluating and testing your applications in LangSmith.

Evaluation SDK & API

Write evaluations to test and improve your application.

• Evaluate an LLM application in the SDK
• Define a custom evaluator
• Evaluate on intermediate steps
• Use LangChain off-the-shelf evaluators (Python only)
• Evaluate an existing experiment
• Run a pairwise evaluation
• Run evals using the API only

Unit testing

Run assertions and expectations designed to quickly identify obvious bugs and regressions in your AI system, natively in your favorite testing library.

• Unit test LLM applications (Python only)

Auto-evaluation

Set up auto-evaluators that LangSmith will automatically run on your experiments.

• Set up an Auto-Evaluator to run on all experiments
• Create few-shot evaluators

Online evaluation

Set up evaluations to run on incoming traces to understand your application's behavior in production.

• Set up online evaluations
• Create few-shot evaluators

Experiments

Use the experiments UI & API to understand your evaluations.

• Run an evaluation in the prompt playground
• Compare experiments with the comparison view
• Filter experiments
• View pairwise experiments
• Fetch experiment results in the SDK
• Upload experiments run outside of LangSmith with the REST API

Datasets

Manage datasets in LangSmith used by your offline evaluations (as well as other downstream applications).

• Manage datasets in the application
• Manage datasets programmatically
• Version datasets
• Share or unshare a dataset publicly
• Export filtered traces from an experiment to a dataset

Annotation Queues and Human Feedback

Collect feedback from subject matter experts and users to improve your LLM applications.

• Use annotation queues
• Capture user feedback from your application to traces
• Set up a new feedback criteria
• Annotate traces inline
• Audit and correct evaluator scores