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Prompts

deepeval lets you evaluate prompts by associating them with test runs. A Prompt in deepeval contains the prompt template and model parameters used for generation. By linking a Prompt to a test run, you can attribute metric scores to specific prompts, enabling metrics-driven prompt selection and optimization for your LLM application.

Quick summary​

There are two types of evaluations in deepeval:

  • End-to-End Testing
  • Component-level Testing

This means you can evaluate prompts end-to-end or on the component-level.

End-to-end testing is useful when you want to evaluate the prompt's impact on the entire LLM application, since metric scores in end-to-end tests are calculated on the final output. Component-level testing is useful when you want to evaluate prompts for specific LLM generation processes, since metric scores in component-level tests are calculated on the component-level.

Evaluating Prompts​

End-to-End​

You can evaluate prompts end-to-end by running the evaluate function in Python or assert_test in CI/CD pipelines.

To evaluate a prompt during end-to-end evaluation, pass your test cases and metrics to the evaluate function, and include the prompt object in the hyperparameters dictionary with any string key.

main.py
from somewhere import your_llm_app
from deepeval.prompt import Prompt, PromptMessage
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.test_case import LLMTestCase
from deepeval import evaluate

prompt = Prompt(
    alias="First Prompt",
    messages_template=[PromptMessage(role="system", content="You are a helpful assistant.")]
)

input = "What is the capital of France?"
actual_output = your_llm_app(input, prompt.messages_template)

evaluate(
    test_cases=[LLMTestCase(input=input, actual_output=actual_output)],
    metrics=[AnswerRelevancyMetric()],
    hyperparameters={"prompt": prompt}
)
tip

You can log multiple prompts in the hyperparameters dictionary if your LLM application uses multiple prompts.

evaluate(..., hyperparameters={"prompt_1": prompt_1, "prompt_2": prompt_2})
✅ If successful, you should see a confirmation log like the one below in your CLI.
✓ Prompts Logged

╭─ Message Prompt (v00.00.20) ──────────────────────────────╮
│                                                           │
│  type: messages                                           │
│  output_type: OutputType.SCHEMA                           │
│  interpolation_type: PromptInterpolationType.FSTRING      │
│                                                           │
│  Model Settings:                                          │
│    – provider: OPEN_AI                                    │
│    – name: gpt-4o                                         │
│    – temperature: 0.7                                     │
│    – max_tokens: None                                     │
│    – top_p: None                                          │
│    – frequency_penalty: None                              │
│    – presence_penalty: None                               │
│    – stop_sequence: None                                  │
│    – reasoning_effort: None                               │
│    – verbosity: LOW                                       │
│                                                           │
╰───────────────────────────────────────────────────────────╯

Based on the metric scores, you can iterate on different prompts to identify the highest-performing version and optimize your LLM application accordingly.

Component-Level​

deepeval also supports component-level prompt evaluation to assess specific LLM generations within your application. To enable this, first set up tracing, then call update_llm_span with the prompts you want to evaluate for each LLM span. Additionally, supply the metrics you want to use in the @observe decorator for each span.

main.py
from openai import OpenAI
from deepeval.tracing import observe, update_llm_span
from deepeval.prompt import Prompt, PromptMessage
from deepeval.metrics import AnswerRelevancyMetric

prompt_1 = Prompt(alias="First",  messages_template=[PromptMessage(role="system", content="You are a helpful assistant.")])

@observe(type="llm", metrics=[AnswerRelevancyMetric()])
def gen1(input: str):
    prompt_template = [{"role": msg.role, "content": msg.content} for msg in prompt_1.messages_template]
    res = OpenAI().chat.completions.create(model="gpt-4o", messages=prompt_template+[{"role":"user","content":input}])
    update_llm_span(prompt=prompt_1)
    return res.choices[0].message.content

@observe()
def your_llm_app(input: str):
    return gen1(input)
note

Since update_llm_span can only be called inside an LLM span, prompt evaluation is limited to LLM spans only.

Then run the evals_iterator to evaluate the prompts configured for each LLM span.

main.py
from deepeval.dataset import EvaluationDataset, Golden
...

dataset = EvaluationDataset([Golden(input="Hello")])
for golden in dataset.evals_iterator():
    your_llm_app(golden.input)
✅ If successful, you should see a confirmation log like the one above in your CLI.
✓ Prompts Logged

╭─ Message Prompt (v00.00.20) ──────────────────────────────╮
│                                                           │
│  type: messages                                           │
│  output_type: OutputType.SCHEMA                           │
│  interpolation_type: PromptInterpolationType.FSTRING      │
│                                                           │
│  Model Settings:                                          │
│    – provider: OPEN_AI                                    │
│    – name: gpt-4o                                         │
│    – temperature: 0.7                                     │
│    – max_tokens: None                                     │
│    – top_p: None                                          │
│    – frequency_penalty: None                              │
│    – presence_penalty: None                               │
│    – stop_sequence: None                                  │
│    – reasoning_effort: None                               │
│    – verbosity: LOW                                       │
│                                                           │
╰───────────────────────────────────────────────────────────╯

Creating Prompts​

Loading Prompts​

When loading prompts from .json files, the file name is automatically taken as the alias, if unspecified.

main.py
from deepeval.prompt import Prompt

prompt = Prompt()
prompt.load(file_path="example.json")
Click to see example.json
example.json
{
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    }
  ]
}
caution

When evaluating prompts, you must call load or pull before passing the prompt to the hyperparameters dictionary for end-to-end evaluation, and before calling update_llm_span for component-level evaluations.

From Scratch​

You can create a prompt in code by instantiating a Prompt object with an alias. Supply either a list of messages for a message-based prompt, or a text string for a text-based prompt.

main.py
from deepeval.prompt import Prompt, PromptMessage

prompt = Prompt(
    alias="First Prompt",
    messages_template=[PromptMessage(role="system", content="You are helpful assistant.")]
)

Additional Attributes​

In addition to prompt templates, you can associate model and output settings with a Prompt.

Model Settings​

Model settings include the model provider and name, as well as generation parameters such as temperature:

main.py
from deepeval.prompt import Prompt, ModelSettings, ModelProvider

model_settings=ModelSettings(
    provider=ModelProvider.OPEN_AI,
    name="gpt-3.5-turbo",
    max_tokens=100,
    temperature=0.7
)
prompt = Prompt(..., model_settings=model_settings)

You can configure the following nine model settings for a prompt:

  • provider: An ModelProvider enum specifying the model provider to use for generation.
  • name: The string specifying the model name to use for generation.
  • temperature: A float between 0.0 and 2.0 specifying the randomness of the generated response.
  • top_p: A float between 0.0 and 1.0 specifying the nucleus sampling parameter.
  • frequency_penalty: A float between -2.0 and 2.0 specifying the frequency penalty.
  • presence_penalty: A float between -2.0 and 2.0 specifying the presence penalty.
  • max_tokens: An integer specifying the maximum number of tokens to generate.
  • verbosity: A Verbosity enum specifying the response detail level.
  • reasoning_effort: An ReasoningEffort enum specifying the thinking depth for reasoning models.
  • stop_sequences: A list of strings specifying custom stop tokens.

Output Settings​

The output settings include the output type and optionally the output schema, if the output type is OutputType.SCHEMA.

main.py
from deepeval.prompt import OutputType
from pydantic import BaseModel
...

class Output(BaseModel):
    name: str
    age: int
    city: str

prompt = Prompt(..., output_type=OutputType.SCHEMA, output_schema=Output)

There are TWO output settings you can associate with a prompt:

  • output_type: The string specifying the model to use for generation.
  • output_schema: The schema of type BaseModel of the output, if output_type is OutputType.SCHEMA.