Document extraction is a common use case for LLMs, transforming unstructured text into structured data. It's important to evaluate your extraction pipeline's performance, especially for large-scale or high-stakes applications.
In this video, we walk through how to build a document extraction pipeline and evaluate its performance in generating structured output. You'll learn how to:
- Compare the performance of two different models based on latency, cost, and accuracy
- Use an LLM judge to evaluate outputs against a ground truth dataset
- Run multiple judge repetitions to validate results
When watching, we recommend you follow along with the code in the notebook below. Have questions? Comment below or join us in the LangChain Community Slack: https://www.langchain.com/join-community
Resources:
Notebook Link: https://github.com/langchain-ai/the-judge/tree/main/evaluate-document-extraction
LangGraph Docs: https://langchain-ai.github.io/langgraph/
LangSmith Docs: https://docs.smith.langchain.com/
Running evaluations with the LangSmith SDK: https://docs.smith.langchain.com/evaluation
Enroll in LangChain Academy for free with our Introduction to LangGraph and LangSmith courses: https://academy.langchain.com
