Role Adherence

The role adherence metric is a conversational metric that determines whether your LLM chatbot is able to adhere to its given role throughout a conversation.

Required Arguments

To use the RoleAdherenceMetric, you'll have to provide the following arguments when creating a ConversationalTestCase:

• turns
• chatbot_role

You must provide the role and content for evaluation to happen. Read the How Is It Calculated section below to learn more.

Usage

First, set the eval mode:

deepeval set-eval-mode llm         # LLM-as-a-judge (default)
deepeval set-eval-mode hybrid      # LLM extracts, Jev decides
deepeval set-eval-mode system_one  # Jev-as-a-judge, no LLM

The RoleAdherenceMetric() can be used for end-to-end multi-turn evaluation:

from deepeval.test_case import Turn, ConversationalTestCase
from deepeval.metrics import RoleAdherenceMetric
from deepeval import evaluate

convo_test_case = ConversationalTestCase(
    chatbot_role="...",
    turns=[Turn(role="...", content="..."), Turn(role="...", content="...")]
)
metric = RoleAdherenceMetric(threshold=0.5)

# To run metric as a standalone
# metric.measure(convo_test_case)
# print(metric.score, metric.reason)

evaluate(test_cases=[convo_test_case], metrics=[metric])

There are NINE optional parameters when creating a RoleAdherenceMetric:

• [Optional] threshold: a number representing the minimum passing threshold. Can also be set to None to run the metric in score-only mode. 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-5.4.
• [Optional] include_reason: a boolean which when set to True, will include a reason for its evaluation score. Defaulted to True.
• [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.
• [Optional] flaky: a boolean which when set to True, marks the metric as flaky. Defaulted to False.
• [Optional] system_one_model: the Jev model to use, as a string or a DeepEvalBaseSystemOneModel. Only used under hybrid or system_one eval_mode. Defaulted to jev-latest.
• [Optional] eval_mode: llm, hybrid or system_one, choosing whether an LLM, Jev, or both judge. Defaulted to the configured eval mode (llm unless set).

As a standalone

You can also run the RoleAdherenceMetric on a single test case as a standalone, one-off execution.

...

metric.measure(convo_test_case)
print(metric.score, metric.reason)

How Is It Calculated?

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

LLM-as-a-judge

The RoleAdherenceMetric score is calculated according to the following equation:

The RoleAdherenceMetric iterates over each assistant turn and uses an LLM to evaluate whether the content adheres to the specified chatbot_role, using previous conversation turns as context.

Hybrid

Under the hybrid eval mode, Jev, a System One model, answers one yes/no question per assistant turn: does it stay in character as the chatbot_role? Turns with P(yes) < 0.5 count as out of character. The equation and the LLM-written reason are unchanged. The LLM judges all turns in one prompt, so if any Jev call fails, the LLM judges the whole conversation instead. If a Jev call fails, the LLM makes that decision instead.

Jev-as-a-judge

Under the system_one eval mode, Jev judges the whole metric in one request. It is sent the whole conversation and the chatbot_role and asked three questions:

Each answer becomes a value in $[0, 1]$ and the score is their weighted mean. No LLM is called: the reason lists each answer with its probability, and metric.confidence reports how decisive Jev was.

FAQs