Clarification Classifier

The ClarificationClassifier in deepeval is a categorical LLM-as-a-judge that determines whether your LLM application asked a clarifying question on an ambiguous input, answered because the input was clear, or guessed.

Underspecified prompts, where the right move is to ask a question before acting, make the best test cases here. They show whether the assistant pauses or simply barrels ahead.

Labels

• asked_clarification: the input is ambiguous or underspecified and the response asks a clarifying question before proceeding.
• answered_directly: the input is clear enough and the response answers or acts on it without needing clarification.
• guessed: the input is ambiguous or underspecified but the response proceeds on an assumption instead of asking.

Usage

from deepeval.classifiers import ClarificationClassifier
from deepeval.test_case import LLMTestCase
from deepeval import evaluate

classifier = ClarificationClassifier()

test_case = LLMTestCase(
    input="Book me a flight to Portland next week.",
    actual_output="Portland, Oregon or Portland, Maine? And which day next week works best?",
    expected_labels={classifier.name: "asked_clarification"},
)

evaluate(test_cases=[test_case], classifiers=[classifier])

There are SEVEN optional parameters when creating a ClarificationClassifier:

• [Optional] model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to gpt-5.4.
• [Optional] include_reason: a boolean which when set to True, includes a reason for the chosen label. Defaulted to True.
• [Optional] allow_none: a boolean which when set to True, lets the classifier return no label when none of them fit (surfaced as label=None, with a reason). When False, the closest label is always chosen. Defaulted to False.
• [Optional] async_mode: a boolean which when set to True, enables concurrent execution within the classify() method. Defaulted to True.
• [Optional] classification_template: a subclass of ClassifierTemplate used to override the default prompts. Defaulted to ClassifierTemplate.
• [Optional] system_one_model: the Jev model to use, as a string or a DeepEvalBaseSystemOneModel. Only used under the system_one eval_mode. Defaulted to jev-latest.
• [Optional] eval_mode: llm or system_one, choosing whether an LLM or Jev picks the label. Defaulted to the configured eval mode (llm unless set).

As a standalone

You can also run the ClarificationClassifier on a single test case as a standalone, one-off execution. classify() returns the label and stores the result on the instance:

...

label = classifier.classify(test_case)
print(classifier.label, classifier.reason)

How Is It Calculated?

You can change how the ClarificationClassifier is calculated by setting the eval mode.

LLM-as-a-judge

The ClarificationClassifier is a one-shot LLM-as-a-judge: a single call to your evaluation model with the test case content and the labels above, returning the chosen label and a reason. The result is compared against the test case's expected_labels entry, if any, to decide pass or fail.

Jev-as-a-judge

Under the system_one eval mode, Jev, a System One model, decides the label as one choice over the labels above, using each label's description as its boundary. The reason is built from Jev's per-label probabilities and confidence. No LLM is used, and Jev errors are raised.

system_one needs TYPESAFE_API_KEY set and the SDK installed.