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LLM applications can be challenging to evaluate since they often generate conversational text with no single correct answer. This guide shows you how to define an LLM-as-a-judge evaluator for offline evaluation using the LangSmith SDK.
For a quick start, use prebuilt evaluators, which provide ready-to-use LLM-as-a-judge evaluators.

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Create your own LLM-as-a-judge evaluator

For complete control of evaluator logic, create your own LLM-as-a-judge evaluator and run it using the LangSmith SDK (Python / TypeScript). Requires langsmith>=0.2.0 An LLM-as-a-judge evaluator consists of three key components:
  1. Evaluator function: A function that receives the example inputs and application outputs, then uses an LLM to score the quality. The function should return a boolean, number, string, or dictionary with score information.
  2. Target function: Your application logic being evaluated (wrapped with @traceable for observability).
  3. Dataset and evaluation: A dataset of test examples and the evaluate() function that runs your target function on each example and applies your evaluators.

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Example

from langsmith import evaluate, traceable, wrappers, Client
from openai import OpenAI
from pydantic import BaseModel

# Wrap the OpenAI client to automatically trace all LLM calls
oai_client = wrappers.wrap_openai(OpenAI())

# 1. Define your evaluator function
# This function receives the inputs and outputs from each test example
def valid_reasoning(inputs: dict, outputs: dict) -> bool:
    """Use an LLM to judge if the reasoning and the answer are consistent."""
    # Define the evaluation criteria
    instructions = """
Given the following question, answer, and reasoning, determine if the reasoning
for the answer is logically valid and consistent with the question and the answer."""

    # Use structured output to get a boolean score
    class Response(BaseModel):
        reasoning_is_valid: bool

    # Construct the prompt with the actual inputs and outputs
    msg = f"Question: {inputs['question']}\nAnswer: {outputs['answer']}\nReasoning: {outputs['reasoning']}"

    # Call the LLM to judge the output
    response = oai_client.beta.chat.completions.parse(
        model="gpt-4o",
        messages=[{"role": "system", "content": instructions}, {"role": "user", "content": msg}],
        response_format=Response
    )

    # Return the boolean score
    return response.choices[0].message.parsed.reasoning_is_valid

# 2. Define your target function (the application being evaluated)
# The @traceable decorator logs traces to LangSmith for debugging
@traceable
def dummy_app(inputs: dict) -> dict:
    return {"answer": "hmm i'm not sure", "reasoning": "i didn't understand the question"}

# 3. Create a dataset with test examples
ls_client = Client()
dataset = ls_client.create_dataset("big questions")
examples = [
    {"inputs": {"question": "how will the universe end"}},
    {"inputs": {"question": "are we alone"}},
]
ls_client.create_examples(dataset_id=dataset.id, examples=examples)

# 4. Run the evaluation
# This runs dummy_app on each example and applies the valid_reasoning evaluator
results = evaluate(
    dummy_app,              # Your application function
    data=dataset,           # Dataset to evaluate on
    evaluators=[valid_reasoning]  # List of evaluator functions
)
For more information on how to write a custom evaluator, refer to How to define a code evaluator (SDK).
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