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.
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.

Practice

1. What is the main purpose of LangSmith evaluators in LangChain?

easy

A. To check how good AI outputs are by comparing predictions to references

B. To train new AI models from scratch

C. To store large datasets for AI training

D. To create user interfaces for AI applications

Solution

1. Step 1: Understand the role of evaluators

   LangSmith evaluators are designed to assess AI outputs by comparing them with expected answers.

2. Step 2: Identify the correct purpose

   They do not train models, store data, or build interfaces but focus on evaluation.

3. Final Answer:

   To check how good AI outputs are by comparing predictions to references -> Option A

4. Quick Check:

   Evaluator purpose = Checking AI output quality [OK]

Hint: Evaluators compare AI answers to references to check quality [OK]

Common Mistakes:

• Confusing evaluators with training tools
• Thinking evaluators store data
• Assuming evaluators build UI

2. Which of the following is the correct way to call an evaluator's evaluate method in LangSmith?

easy

A. evaluate(evaluator, prediction, reference)

B. evaluator.evaluate(prediction, reference)

C. evaluator.run(reference, prediction)

D. evaluate(prediction, reference, evaluator)

Solution

1. Step 1: Recall method usage

   The evaluate method is called on the evaluator object with prediction and reference as arguments.

2. Step 2: Match correct syntax

   evaluator.evaluate(prediction, reference) matches this pattern exactly: evaluator.evaluate(prediction, reference).

3. Final Answer:

   evaluator.evaluate(prediction, reference) -> Option B

4. Quick Check:

   Method call = evaluator.evaluate(prediction, reference) [OK]

Hint: Call evaluate on evaluator with prediction and reference [OK]

Common Mistakes:

• Swapping argument order
• Calling evaluate as a standalone function
• Using wrong method name like run

3. Given the code snippet:

evaluator = SomeEvaluator()
prediction = "The sky is blue."
reference = "The sky is clear and blue."
result = evaluator.evaluate(prediction, reference)
print(result)

What is the expected behavior of print(result)?

medium

A. It prints the reference string unchanged

B. It prints the prediction string unchanged

C. It prints a score or feedback comparing prediction to reference

D. It raises a syntax error because evaluate needs more arguments

Solution

1. Step 1: Understand evaluate output

   The evaluate method returns a score or feedback about how close the prediction matches the reference.

2. Step 2: Analyze print statement

   Printing result shows this evaluation output, not the original strings or errors.

3. Final Answer:

   It prints a score or feedback comparing prediction to reference -> Option C

4. Quick Check:

   Evaluate returns score/feedback [OK]

Hint: Evaluate returns comparison result, not original text [OK]

Common Mistakes:

• Expecting evaluate to return input strings
• Thinking evaluate raises error without extra args
• Confusing prediction and reference outputs

4. What is the error in this code snippet?

evaluator = SomeEvaluator()
result = evaluator.evaluate(reference, prediction)
print(result)

Assuming evaluate expects (prediction, reference) order.

medium

A. Arguments are reversed; prediction should come before reference

B. Missing import statement for SomeEvaluator

C. evaluate method does not exist on evaluator

D. print statement syntax is incorrect

Solution

1. Step 1: Check argument order

   The evaluate method expects prediction first, then reference, but code reverses them.

2. Step 2: Confirm other parts are correct

   Assuming SomeEvaluator is imported and evaluate exists, the main issue is argument order.

3. Final Answer:

   Arguments are reversed; prediction should come before reference -> Option A

4. Quick Check:

   Correct argument order = prediction, reference [OK]

Hint: Remember evaluate(prediction, reference) argument order [OK]

Common Mistakes:

• Swapping prediction and reference arguments
• Assuming missing imports cause this error
• Thinking print syntax is wrong

5. You want to compare multiple AI model outputs to a single reference answer using LangSmith evaluators. Which approach correctly applies evaluators to get scores for each prediction?

hard

A. Combine all predictions into one string and evaluate against reference once

B. Call evaluator.evaluate once with a list of predictions and one reference

C. Use evaluator.evaluate(reference, prediction) inside a loop over references

D. Loop over predictions, call evaluator.evaluate(prediction, reference) for each, collect results

Solution

1. Step 1: Understand evaluator usage for multiple inputs

   Evaluators typically compare one prediction to one reference at a time.

2. Step 2: Apply evaluator in a loop

   Looping over each prediction and calling evaluate separately gives individual scores.

3. Step 3: Eliminate incorrect options

   Passing lists or combining strings is not standard; argument order matters.

4. Final Answer:

   Loop over predictions, call evaluator.evaluate(prediction, reference) for each, collect results -> Option D

5. Quick Check:

   Evaluate each prediction separately in a loop [OK]

Hint: Evaluate predictions one by one in a loop against reference [OK]

Common Mistakes:

• Passing lists instead of single strings
• Mixing argument order
• Combining predictions into one string