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LangSmith
Platform for LLM observability and evaluation
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Overview
Datasets
Create a dataset
Manage datasets
Evaluations
Run an evaluation
Evaluation methods
Evaluator types
Evaluation techniques
Improve evaluators
Analyze experiment results
Analyze a single experiment
Compare experiment results
Filter experiments in the UI
Fetch performance metrics for an experiment
Upload experiments run outside of LangSmith
Download experiment results as a CSV
Rename an experiment
Run evaluators on experiments
Annotation & human feedback
Use annotation queues
Set up feedback criteria
Annotate traces and runs inline
Audit evaluator scores
Tutorials
Evaluate a chatbot
Evaluate a RAG application
Test a ReAct agent with Pytest/Vitest and LangSmith
Evaluate a complex agent
Run backtests on a new version of an agent
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Evaluation
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Welcome to the LangSmith Evaluation documentation. The following sections help you create datasets, run evaluations, and analyze results:
Datasets
: Create and manage datasets for evaluation, including creating datasets through the UI or SDK and managing existing datasets.
Evaluations
: Run evaluations on your applications using various methods and techniques, including different evaluator types and evaluation techniques.
Analyze experiment results
: View and analyze your evaluation results, including comparing experiments, filtering results, and downloading data.
Annotation & human feedback
: Collect human feedback on your application outputs through annotation queues and inline annotation.
Tutorials
: Follow step-by-step tutorials to evaluate different types of applications, from chatbots to complex agents.
For terminology definitions and core concepts, refer to the
introduction on evaluation
.
With the UI
Next
Assistant
Responses are generated using AI and may contain mistakes.