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G-Eval is a framework that uses LLMs with chain-of-thoughts (CoT) to evaluate LLM outputs based on ANY custom criteria. The G-Eval metric is the most verstile type of metric deepeval has to offer, and is capable of evaluating almost any use case with human-like accuracy.


You can run real-time evaluations in production on metrics such as GEval using Confident AI.

Required Arguments

To use the GEval, you'll have to provide the following arguments when creating an LLMTestCase:

  • input
  • actual_output

You'll also need to supply any additional arguments such as expected_output and context if your evaluation criteria depends on these parameters.


To create a custom metric that uses LLMs for evaluation, simply instantiate an GEval class and define an evaluation criteria in everyday language:

from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCaseParams

correctness_metric = GEval(
criteria="Determine whether the actual output is factually correct based on the expected output.",
# NOTE: you can only provide either criteria or evaluation_steps, and not both
"Check whether the facts in 'actual output' contradicts any facts in 'expected output'",
"You should also heavily penalize omission of detail",
"Vague language, or contradicting OPINIONS, are OK"
evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT],

There are three mandatory and six optional parameters required when instantiating an GEval class:

  • name: name of metric
  • criteria: a description outlining the specific evaluation aspects for each test case.
  • evaluation_params: a list of type LLMTestCaseParams. Include only the parameters that are relevant for evaluation.
  • [Optional] evaluation_steps: a list of strings outlining the exact steps the LLM should take for evaluation. If evaluation_steps is not provided, GEval will generate a series of evaluation_steps on your behalf based on the provided criteria. You can only provide either evaluation_steps OR criteria, and not both.
  • [Optional] threshold: the passing threshold, defaulted to 0.5.
  • [Optional] model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to 'gpt-4o'.
  • [Optional] strict_mode: a boolean which when set to True, enforces a binary metric score: 1 for perfection, 0 otherwise. It also overrides the current threshold and sets it to 1. Defaulted to False.
  • [Optional] async_mode: a boolean which when set to True, enables concurrent execution within the measure() method. Defaulted to True.
  • [Optional] verbose_mode: a boolean which when set to True, prints the intermediate steps used to calculate said metric to the console, as outlined in the How Is It Calculated section. Defaulted to False.

For accurate and valid results, only the parameters that are mentioned in criteria should be included as a member of evaluation_params.

As mentioned in the metrics introduction section, all of deepeval's metrics return a score ranging from 0 - 1, and a metric is only successful if the evaluation score is equal to or greater than threshold, and GEval is no exception. You can access the score and reason for each individual GEval metric:

from deepeval.test_case import LLMTestCase

test_case = LLMTestCase(
input="The dog chased the cat up the tree, who ran up the tree?",
actual_output="It depends, some might consider the cat, while others might argue the dog.",
expected_output="The cat."


How Is It Calculated?

G-Eval is a two-step algorithm that first generates a series of evaluation_steps using chain of thoughts (CoTs) based on the given criteria, before using the generated steps to determine the final score using the parameters presented in an LLMTestCase.

When you provide evaluation_steps, the GEval metric skips the first step and uses the provided steps to determine the final score instead.

Did Your Know?

In the original G-Eval paper, the authors used the the probabilities of the LLM output tokens to normalize the score by calculating a weighted summation?

This step was introduced in the paper because it minimizes bias in LLM scoring. This normalization step is automatically handled by deepeval by default (unless you're using a custom model).