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  * LangSmith 

#  LangSmith

[ LangSmith ](https://smith.langchain.com) helps you trace and evaluate your
language model applications and intelligent agents to help you move from
prototype to production.

Check out the [ interactive walkthrough ](/docs/langsmith/walkthrough) to get
started.

For more information, please refer to the [ LangSmith documentation
](https://docs.smith.langchain.com/) .

For tutorials and other end-to-end examples demonstrating ways to integrate
LangSmith in your workflow, check out the [ LangSmith Cookbook
](https://github.com/langchain-ai/langsmith-cookbook) . Some of the guides
therein include:

  * Leveraging user feedback in your JS application ( [ link ](https://github.com/langchain-ai/langsmith-cookbook/blob/main/feedback-examples/nextjs/README.md) ). 
  * Building an automated feedback pipeline ( [ link ](https://github.com/langchain-ai/langsmith-cookbook/blob/main/feedback-examples/algorithmic-feedback/algorithmic_feedback.ipynb) ). 
  * How to evaluate and audit your RAG workflows ( [ link ](https://github.com/langchain-ai/langsmith-cookbook/tree/main/testing-examples/qa-correctness) ). 
  * How to fine-tune an LLM on real usage data ( [ link ](https://github.com/langchain-ai/langsmith-cookbook/blob/main/fine-tuning-examples/export-to-openai/fine-tuning-on-chat-runs.ipynb) ). 
  * How to use the [ LangChain Hub ](https://smith.langchain.com/hub) to version your prompts ( [ link ](https://github.com/langchain-ai/langsmith-cookbook/blob/main/hub-examples/retrieval-qa-chain/retrieval-qa.ipynb) ) 

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