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Introduction

Quick Summary​

In deepeval, a metric serves as a standard of measurement for evaluating the performance of an LLM output based on a specific criteria of interest. Essentially, while the metric acts as the ruler, a test case represents the thing you're trying to measure. deepeval offers a range of default metrics for you to quickly get started with, such as:

  • G-Eval
  • DAG (Deep Acyclic Graph)
  • RAG:
    • Answer Relevancy
    • Faithfulness
    • Contextual Relevancy
    • Contextual Precision
    • Contextual Recall
  • Agents:
    • Tool Correctness
    • Task Completion
  • Chatbots (for conversational agents):
    • Conversational G-Eval
    • Knowledge Retention
    • Role Adherence
    • Conversation Completeness
    • Conversation Relevancy
  • Others:
    • Json Correctness
    • Ragas
    • Hallucination
    • Toxicity
    • Bias
    • Summarization

All predefined metrics on deepeval uses LLM-as-a-judge, with various techniques such as QAG (question-answer-generation), DAG (deep acyclic graphs), and G-Eval to score test cases, which represents atomic interactions with your LLM app.

If you prefer to write your own metric algorithm, or use more traditional NLP scorers such as ROUGE, BLEU, or BLEURT, you can easily develop your own custom evaluation metrics in deepeval. All custom metrics you create are also automatically 100% integrated with deepeval's ecosystem.

note

Your LLM application can be benchmarked by providing a list of metrics and test cases:

from deepeval.metrics import AnswerRelevancyMetric
from deepeval import evaluate

evaluate(test_cases=[...], metrics=[AnswerRelevancyMetric()])

You should also login to the deepeval cloud platform, Confident AI, before running evaluate():

deepeval login

When you run an evaluation using the evaluate() function or deepeval test run, you get testing reports on Confident AI.

Run Evaluations on Confident AI

More information on everything can be found on the Confident AI evaluation docs.

Why DeepEval Metrics?​

deepeval's metrics are a step up to other implementations because they:

  • Make deterministic metric scores possible (when using DAGMetric).
  • Can be scored using any LLM judge.
  • Are "routable" - meaning you can apply different metrics based on different scenarios.
  • Can be used for both end-to-end and component-level evaluation.
  • Easily customizable (GEval and DAGMetric).
  • Are extra reliable as LLMs are only used for extremely confined tasks during evaluation to greatly reduce stochasticity and flakiness in scores.
  • Provide a comprehensive reason for the scores computed.
  • Can be customized by overriding evaluation prompts.
  • Integrated 100% with Confident AI.

All of deepeval's metrics output a score between 0-1. A metric is only successful if the evaluation score is equal to or greater than threshold, which is defaulted to 0.5 for all metrics.

Additionally, deepeval's metrics can also be used for both end-to-end evals:

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

evaluate(test_cases=[LLMTestCase(...)], metrics=[AnswerRelevancyMetric()])

And component-level evals:

from deepeval.dataset import Golden
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.tracing import observe, update_current_span
from deepeval import evaluate

@observe(metrics=[AnswerRelevancyMetric()])
def nested_component():
    update_current_span(test_case=LLMTestCase(...))
    pass

@observe
def llm_app(input: str):
    nested_component()

evaluate(goldens=[Golden(...)], observed_callback=[llm_app])

Types of Metrics​

deepeval offers a wide range of custom and generic metrics and all of them uses LLM-as-a-judge. This choice is deliberate because our experience tells us LLM-as-a-judge better align with human expectations when compared to traditional model based approaches.

info

In the early versions of deepeval back in late 2023, our initial implementation relied on non-LLM-as-judge approaches, but we found these methods significantly underperformed compared to LLM-based evaluation techniques in terms of alignment with human judgment.

Custom Metrics​

Custom metrics are use case specific (i.e. system agnostic). They work across different implementation approaches, allowing you to use the same evaluation criteria whether your application uses RAG, agents, or a hybrid architecture. A use case refers to the specific application context—such as a medical chatbot, meeting summarizer, or travel planner agent.

There are two types of custom metrics, with varying degree of deterministicity:

The DAG metric is a decision-tree based LLM-evaluated metric, and is currently the most versatile metric deepeval has to offer. However, G-Eval is also extremely competent and takes no effort at all to setup so we recommend everyone to start with G-Eval and move to DAG if there's a need for it.

tip

If your evaluation criteria is more subjective (e.g. answer "correctness", coherence, and tonality), go for G-Eval. If your evaluation criteria involves objective requirements (e.g. format correctness), choose DAG. If it is a mixture of both (e.g. ensure the format of an LLM output is correct before assessing its tonality), you can use G-Eval within the DAG metric.

You can also inherit a BaseMetric class to create your own custom metric. They are extremely easy to create and almost 10% of all metrics ran using deepeval are self-built metrics.

Generic Metrics​

deepeval also offers generic metrics that are system specific (i.e. use case agnostic). These metrics target particular LLM architectures regardless of domain:

  • RAG metrics evaluate retrieval and generation quality
  • Agent metrics assess tool usage, task completion
  • Conversational metrics measure overall conversation quality

Here are the most popular RAG metrics deepeval offers out-of-the-box:

For complex LLM applications combining multiple architectures (like RAG systems with agentic capabilities or multi-step reasoning workflows), use a combination of targeted metrics to evaluate each component effectively. This modular approach ensures comprehensive evaluation across your entire application pipeline.

Referenceless Metrics​

Metrics in deepeval are categorized by whether they require ground truth for evaluation:

  • Reference-based metrics require ground truth data through specific test case parameters. Examples include contextual recall/precision (needs expected_output), tool correctness (needs expected_tools), and hallucination detection (needs original context).

  • Referenceless metrics evaluate outputs without ground truth comparisons. Most generic metrics in deepeval are referenceless, allowing evaluation without labeled datasets.

For custom metrics (G-Eval and DAG), reference requirements depend on your evaluation criteria. For instance, users define answer correctness in G-Eval typically compares actual_output with expected_output. Check each metric's "Required Parameters" section in its documentation to see whether it is a referenceless metric or not.

note

By definition, online metrics used in production cannot be reference-based.

Choosing Your Metrics​

When deciding which metrics to use, it is very tempting to evaluate everything (I mean, who doesn't like to evaluate bias in their RAG QA app?). But using too many metrics means evaluating nothing at all. Limit yourself to no more than 5 metrics, with this breakdown:

  • 2-3 generic, system-specific metrics (e.g. contextual precision for RAG, tool correctness for agents)
  • 1-2 custom, use case-specific metrics (e.g. helpfulness for a medical chatbot, format correctness for summarization)

If you feel extreme pain and struggle when trying to cut down on your metric selection, especially for generic ones, you're on the right track. The goal is to force yourself to prioritize and clearly define your evaluation criteria. This will not only help you use deepeval, but also help you understand what you care most about in your LLM application.

note

In some cases, where your LLM model is doing most of the heavy lifting (e.g. drafting documents, summarizers), it is not uncommon for there to be more use case targeted metric than system targeted ones.

Here are some additional ideas if you're not sure:

  • RAG: Focus on the AnswerRelevancyMetric (evaluates actual_output alignment with the input) and FaithfulnessMetric (checks for hallucinations against retrieved_context)
  • Agents: Use the ToolCorrectnessMetric to verify proper tool selection and usage
  • Chatbots: Implement a ConversationCompletenessMetric to assess overall conversation quality
  • Custom Requirements: When standard metrics don't fit your needs, create custom evaluations with G-Eval or DAG frameworks

If you're not sure which metric to use, join our discord community or run the follow command to get some suggestions:

deepeval recommend metrics

LLM Judges​

You can use ANY LLM judge in deepeval, including:

You can also wrap your own LLM API in deepeval's DeepEvalBaseLLM class to use ANY model of your choice. Click here for full guide.

OpenAI​

To use OpenAI for deepeval's LLM metrics, supply your OPENAI_API_KEY in the CLI:

export OPENAI_API_KEY=<your-openai-api-key>

Alternatively, if you're working in a notebook environment (Jupyter or Colab), set your OPENAI_API_KEY in a cell:

%env OPENAI_API_KEY=<your-openai-api-key>
note

Please do not include quotation marks when setting your OPENAI_API_KEY if you're working in a notebook environment.

Azure OpenAI​

deepeval also allows you to use Azure OpenAI for metrics that are evaluated using an LLM. Run the following command in the CLI to configure your deepeval environment to use Azure OpenAI for all LLM-based metrics.

deepeval set-azure-openai \
    --openai-endpoint=<endpoint> \ # e.g. https://example-resource.azure.openai.com/
    --openai-api-key=<api_key> \
    --openai-model-name=<model_name> \ # e.g. gpt-4o
    --deployment-name=<deployment_name> \  # e.g. Test Deployment
    --openai-api-version=<openai_api_version> \ # e.g. 2025-01-01-preview
    --model-version=<model_version> # e.g. 2024-11-20
info

Your OpenAI API version must be at least 2024-08-01-preview, when structured output was released.

Note that the model-version is optional. If you ever wish to stop using Azure OpenAI and move back to regular OpenAI, simply run:

deepeval unset-azure-openai

Ollama​

note

Before getting started, make sure your Ollama model is installed and running. You can also see the full list of available models by clicking on the previous link.

ollama run deepseek-r1:1.5b

To use Ollama models for your metrics, run deepeval set-ollama <model> in your CLI. For example:

deepeval set-ollama deepseek-r1:1.5b

Optionally, you can specify the base URL of your local Ollama model instance if you've defined a custom port. The default base URL is set to http://localhost:11434.

deepeval set-ollama deepseek-r1:1.5b \
    --base-url="http://localhost:11434"

To stop using your local Ollama model and move back to OpenAI, run:

deepeval unset-ollama
caution

The deepeval set-ollama command is used exclusively to configure LLM models. If you intend to use a custom embedding model from Ollama with the synthesizer, please refer to this section of the guide.

Gemini​

To use Gemini models with DeepEval, run the following command in your CLI.

deepeval set-gemini \
    --model-name=<model_name> \ # e.g. "gemini-2.0-flash-001"
    --google-api-key=<api_key>

Using Any Custom LLM​

deepeval allows you to use ANY custom LLM for evaluation. This includes LLMs from langchain's chat_model module, Hugging Face's transformers library, or even LLMs in GGML format.

This includes any of your favorite models such as:

  • Azure OpenAI
  • Claude via AWS Bedrock
  • Google Vertex AI
  • Mistral 7B

All the examples can be found here, but down below is a quick example of a custom Azure OpenAI model through langchain's AzureChatOpenAI module for evaluation:

from langchain_openai import AzureChatOpenAI
from deepeval.models.base_model import DeepEvalBaseLLM

class AzureOpenAI(DeepEvalBaseLLM):
    def __init__(
        self,
        model
    ):
        self.model = model

    def load_model(self):
        return self.model

    def generate(self, prompt: str) -> str:
        chat_model = self.load_model()
        return chat_model.invoke(prompt).content

    async def a_generate(self, prompt: str) -> str:
        chat_model = self.load_model()
        res = await chat_model.ainvoke(prompt)
        return res.content

    def get_model_name(self):
        return "Custom Azure OpenAI Model"

# Replace these with real values
custom_model = AzureChatOpenAI(
    openai_api_version=openai_api_version,
    azure_deployment=azure_deployment,
    azure_endpoint=azure_endpoint,
    openai_api_key=openai_api_key,
)
azure_openai = AzureOpenAI(model=custom_model)
print(azure_openai.generate("Write me a joke"))

When creating a custom LLM evaluation model you should ALWAYS:

  • inherit DeepEvalBaseLLM.
  • implement the get_model_name() method, which simply returns a string representing your custom model name.
  • implement the load_model() method, which will be responsible for returning a model object.
  • implement the generate() method with one and only one parameter of type string that acts as the prompt to your custom LLM.
  • the generate() method should return the final output string of your custom LLM. Note that we called chat_model.invoke(prompt).content to access the model generations in this particular example, but this could be different depending on the implementation of your custom model object.
  • implement the a_generate() method, with the same function signature as generate(). Note that this is an async method. In this example, we called await chat_model.ainvoke(prompt), which is an asynchronous wrapper provided by LangChain's chat models.
tip

The a_generate() method is what deepeval uses to generate LLM outputs when you execute metrics / run evaluations asynchronously.

If your custom model object does not have an asynchronous interface, simply reuse the same code from generate() (scroll down to the Mistral7B example for more details). However, this would make a_generate() a blocking process, regardless of whether you've turned on async_mode for a metric or not.

Lastly, to use it for evaluation for an LLM-Eval:

from deepeval.metrics import AnswerRelevancyMetric
...

metric = AnswerRelevancyMetric(model=azure_openai)
note

While the Azure OpenAI command configures deepeval to use Azure OpenAI globally for all LLM-Evals, a custom LLM has to be set each time you instantiate a metric. Remember to provide your custom LLM instance through the model parameter for metrics you wish to use it for.

caution

We CANNOT guarantee that evaluations will work as expected when using a custom model. This is because evaluation requires high levels of reasoning and the ability to follow instructions such as outputting responses in valid JSON formats. To better enable custom LLMs output valid JSONs, read this guide.

Alternatively, if you find yourself running into JSON errors and would like to ignore it, use the -c and -i flag during deepeval test run:

deepeval test run test_example.py -i -c

The -i flag ignores errors while the -c flag utilizes the local deepeval cache, so for a partially successful test run you don't have to rerun test cases that didn't error.

Running LLM Evals Metrics​

End-to-end​

To run end-to-end evaluations of your LLM system using any metric of your choice, simply provide a list of test cases to evaluate your metrics against:

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

test_case = LLMTestCase(input="...", actual_output="...")

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

Component-level​

To run component-level evaluations of your LLM system using any metric of your choice, simply decorate your components with @observe and create test cases at runtime:

from deepeval.dataset import Golden
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.tracing import observe, update_current_span
from deepeval import evaluate

@observe(metrics=[AnswerRelevancyMetric()])
def nested_component():
    update_current_span(test_case=LLMTestCase(...))
    pass

@observe
def llm_app(input: str):
    nested_component()

evaluate(goldens=[Golden(...)], observed_callback=[llm_app])

The evaluate() function or deepeval test run is the best way to run evaluations. They offer tons of features out of the box, including caching, parallelization, cost tracking, error handling, and integration with Confident AI.

tip

deepeval test run is deepeval's native Pytest integration, which allows you to run evals in CI/CD pipelines.

Measuring A Metric​

You can also execute each metric individually. All metrics in deepeval, including custom metrics that you create:

  • can be executed via the metric.measure() method
  • can have its score accessed via metric.score, which ranges from 0 - 1
  • can have its score reason accessed via metric.reason
  • can have its status accessed via metric.is_successful()
  • can be used to evaluate test cases or entire datasets, with or without Pytest
  • has a threshold that acts as the threshold for success. metric.is_successful() is only true if metric.score is above/below threshold
  • has a strict_mode property, which when turned on enforces metric.score to a binary one
  • has a verbose_mode property, which when turned on prints metric logs whenever a metric is executed

In addition, all metrics in deepeval execute asynchronously by default. You can configure this behavior using the async_mode parameter when instantiating a metric.

tip

Visit an individual metric page to learn how they are calculated, and what is required when creating an LLMTestCase in order to execute it.

Here's a quick example.

export OPENAI_API_KEY=<your-openai-api-key>
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.test_case import LLMTestCase

# Initialize a test case
test_case = LLMTestCase(
    input="...",
    actual_output="...",
    retrieval_context=["..."]
)

# Initialize metric with threshold
metric = AnswerRelevancyMetric(threshold=0.5)

Using this metric, you can execute it directly as a standalone to get its score and reason:

...

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

Or you can assert a test case using assert_test() via deepeval test run:

test_file.py
from deepeval import assert_test
...

def test_answer_relevancy():
    assert_test(test_case, [metric])
deepeval test run test_file.py

Or using the evaluate function:

from deepeval import evaluate
...

evaluate([test_case], [metric])

Measuring Metrics in Async​

When a metric's async_mode=True (which is the default for all metrics), invocations of metric.measure() will execute internal algorithms concurrently. However, it's important to note that while operations INSIDE measure() execute concurrently, the metric.measure() call itself still blocks the main thread.

info

Let's take the FaithfulnessMetric algorithm for example:

  1. Extract all factual claims made in the actual_output
  2. Extract all factual truths found in the retrieval_context
  3. Compare extracted claims and truths to generate a final score and reason.
from deepeval.metrics import FaithfulnessMetric
...

metric = FaithfulnessMetric(async_mode=True)
metric.measure(test_case)
print("Metric finished!")

When async_mode=True, steps 1 and 2 execute concurrently (i.e., at the same time) since they are independent of each other, while async_mode=False causes steps 1 and 2 to execute sequentially instead (i.e., one after the other).

In both cases, "Metric finished!" will wait for metric.measure() to finish running before printing, but setting async_mode to True would make the print statement appear earlier, as async_mode=True allows metric.measure() to run faster.

To measure multiple metrics at once and NOT block the main thread, use the asynchronous a_measure() method instead.

import asyncio
...

# Remember to use async
async def long_running_function():
    # These will all run at the same time
    await asyncio.gather(
        metric1.a_measure(test_case),
        metric2.a_measure(test_case),
        metric3.a_measure(test_case),
        metric4.a_measure(test_case)
    )
    print("Metrics finished!")

asyncio.run(long_running_function())

Debugging A Metric​

You can turn on verbose_mode for ANY deepeval metric at metric initialization to debug a metric whenever the measure() or a_measure() method is called:

...

metric = AnswerRelevancyMetric(verbose_mode=True)
metric.measure(test_case)
note

Turning verbose_mode on will print the inner workings of a metric whenever measure() or a_measure() is called.

Customizing Metric Prompts​

All of deepeval's metrics use LLM-as-a-judge evaluation with unique default prompt templates for each metric. While deepeval has well-designed algorithms for each metric, you can customize these prompt templates to improve evaluation accuracy and stability. Simply provide a custom template class as the evaluation_template parameter to your metric of choice (example below).

info

For example, in the AnswerRelevancyMetric, you might disagree with what we consider something to be "relevant", but with this capability you can now override any opinions deepeval has in its default evaluation prompts.

You'll find this particularly valuable when using a custom LLM, as deepeval's default metrics are optimized for OpenAI's models, which are generally more powerful than most custom LLMs.

note

This means you can better handle invalid JSON outputs (along with JSON confinement) which comes with weaker models, and provide better examples for in-context learning for your custom LLM judges for better metric accuracy.

Here's a quick example of how you can define a custom AnswerRelevancyTemplate and inject it into the AnswerRelevancyMetric through the evaluation_params parameter:

from deepeval.metrics import AnswerRelevancyMetric
from deepeval.metrics.answer_relevancy import AnswerRelevancyTemplate

# Define custom template
class CustomTemplate(AnswerRelevancyTemplate):
    @staticmethod
    def generate_statements(actual_output: str):
        return f"""Given the text, breakdown and generate a list of statements presented.

Example:
Our new laptop model features a high-resolution Retina display for crystal-clear visuals.

{{
    "statements": [
        "The new laptop model has a high-resolution Retina display."
    ]
}}
===== END OF EXAMPLE ======

Text:
{actual_output}

JSON:
"""

# Inject custom template to metric
metric = AnswerRelevancyMetric(evaluation_template=CustomTemplate)
metric.measure(...)
tip

You can find examples of how this can be done in more detail on the Customize Your Template section of each individual metric page, which shows code examples, and a link to deepeval's GitHub showing the default templates currently used.

What About Non-LLM-as-a-judge Metrics?​

If you're looking to use something like ROUGE, BLEU, or BLEURT, etc. you can create a custom metric and use the scorer module available in deepeval for scoring by following this guide.

The scorer module is available but not documented because our experience tells us these scorers are not useful as LLM metrics where outputs require a high level of reasoning to evaluate.