Prompt Alignment

The prompt alignment metric uses LLM-as-a-judge to measure whether your LLM application is able to generate actual_outputs that aligns with any instructions specified in your prompt template. deepeval's prompt alignment metric is a self-explaining LLM-Eval, meaning it outputs a reason for its metric score.

Required Arguments

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

• input
• actual_output

Read the How Is It Calculated section below to learn how test case parameters are used for metric calculation.

Usage

The PromptAlignmentMetric() can be used for end-to-end evaluation:

from deepeval import evaluate
from deepeval.test_case import LLMTestCase
from deepeval.metrics import PromptAlignmentMetric

metric = PromptAlignmentMetric(
    prompt_instructions=["Reply in all uppercase"],
    model="gpt-4",
    include_reason=True
)
test_case = LLMTestCase(
    input="What if these shoes don't fit?",
    # Replace this with the actual output from your LLM application
    actual_output="We offer a 30-day full refund at no extra cost."
)

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

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

There are ONE mandatory and SIX optional parameters when creating an PromptAlignmentMetric:

• prompt_instructions: a list of strings specifying the instructions you want followed in your prompt template.
• [Optional] threshold: a float representing the minimum 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-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.

Within components

You can also run the PromptAlignmentMetric within nested components for component-level evaluation.

from deepeval.dataset import Golden
from deepeval.tracing import observe, update_current_span
...

@observe(metrics=[metric])
def inner_component():
    # Set test case at runtime
    test_case = LLMTestCase(input="...", actual_output="...")
    update_current_span(test_case=test_case)
    return

@observe
def llm_app(input: str):
    # Component can be anything from an LLM call, retrieval, agent, tool use, etc.
    inner_component()
    return

evaluate(observed_callback=llm_app, goldens=[Golden(input="Hi!")])

As a standalone

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

...

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

How Is It Calculated?

The PromptAlignmentMetric score is calculated according to the following equation:

The PromptAlignmentMetric uses an LLM to classify whether each prompt instruction is followed in the actual_output using additional context from the input.