LangSmith evaluators in LangChain - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a LangSmith evaluator in LangChain?

A LangSmith evaluator is a tool that automatically checks and scores the quality of outputs generated by language models, helping developers improve their AI applications.

beginner

How does a LangSmith evaluator improve AI model outputs?

It compares the model's output against expected results or criteria, providing feedback or scores that guide developers to refine prompts or models for better performance.

intermediate

Name two types of LangSmith evaluators commonly used.

1. String match evaluator - checks if output matches expected text exactly or partially.<br>2. Custom evaluator - uses custom logic or AI to assess output quality.

intermediate

What is the role of a custom LangSmith evaluator?

A custom evaluator lets you define your own rules or AI-based checks to score outputs, allowing flexible and domain-specific evaluation beyond simple text matching.

beginner

Why is it important to use evaluators in LangChain projects?

Evaluators help ensure the AI outputs meet quality standards, reduce errors, and improve user experience by providing measurable feedback during development.

What does a LangSmith evaluator primarily do?

A Trains new language models

B Checks and scores AI model outputs

C Stores user data securely

D Generates random text

LangSmith evaluators are designed to check and score the outputs from language models.

Which of the following is NOT a typical use of a LangSmith evaluator?

A Comparing output to expected answers

B Providing feedback for improvement

C Running the language model itself

D Custom scoring based on rules

Evaluators assess outputs but do not run or train the language model.

What type of evaluator would you use to check if output text exactly matches a target string?

A String match evaluator

B Custom evaluator

C Random evaluator

D Data storage evaluator

String match evaluators compare output text directly to expected text.

Why might you create a custom LangSmith evaluator?

A To store evaluation results in a database

B To speed up model training

C To generate new prompts automatically

D To define specific scoring rules for your project

Custom evaluators allow you to set your own rules to score outputs.

Which benefit does using LangSmith evaluators bring to AI development?

A Improves output quality through feedback

B Automatically fixes bugs in code

C Increases server storage space

D Creates user interfaces

Evaluators provide feedback that helps improve AI output quality.

Explain what a LangSmith evaluator is and why it is useful in LangChain projects.

Think about how you check homework to improve next time.

Describe the difference between a string match evaluator and a custom evaluator in LangSmith.

One is like checking answers word-for-word, the other is like judging quality with your own criteria.