📄️ Overview
Many challenges hinder the creation of a high-quality, production-grade LLM applications, including:
📄️ Quick Start
In this walkthrough, you will evaluate a chain over a dataset of examples. To do so, you will:
📄️ Datasets
Datasets are a collections of examples that can be used to evaluate or otherwise improve a chain, agent, or model. Examples are rows in the dataset, containing the inputs and (optionally) expected outputs for a given interaction. Below we will go over the current types of datasets as well as different ways to create them.
📄️ LangChain Evaluators
LangChain's evaluation module provides evaluators you can use as-is for common evaluation scenarios.
📄️ Custom Evaluators
In this guide, you will create a custom string evaluator for your agent. You can choose to use LangChain components or write your own custom evaluator from scratch.
📄️ Feedback
This guide will walk you through feedback in LangSmith. For more end-to-end examples incorporating feedback into a workflow, see the LangSmith Cookbook.
🗃️ Additional Resources
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