Why LangSmith evaluators in LangChain? - Purpose & Use Cases

The Big Idea

Discover how to stop wasting hours manually checking AI answers and get instant quality feedback instead!

The Scenario

Imagine you build a language model app and want to check if its answers are good. You try reading every response yourself and writing notes on what's right or wrong.

The Problem

Manually reviewing each answer is slow, tiring, and easy to miss mistakes. It's hard to keep track of feedback and compare results over time.

The Solution

LangSmith evaluators automatically check model outputs against rules or examples. They give quick, consistent feedback so you can improve your app faster.

Before vs After

✗ Before

response = model.generate(input)
# Manually read and write notes about response quality

✓ After

from langsmith import Evaluator

evaluator = Evaluator()
result = evaluator.evaluate(model_output, reference)
print(result.score)

What It Enables

It enables fast, reliable evaluation of language model outputs to improve quality and user experience.

Real Life Example

A chatbot company uses LangSmith evaluators to automatically score answers and spot when the bot gives wrong or confusing replies.

Key Takeaways

Manual review of language model outputs is slow and error-prone.

LangSmith evaluators automate checking and scoring responses.

This helps improve models quickly with consistent feedback.

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