Model-graded metrics

promptfoo supports several types of model-graded assertions:

Output-based:

llm-rubric - checks if the LLM output matches given requirements, using a language model to grade the output based on the rubric.
model-graded-closedqa - similar to the above, a "criteria-checking" eval that ensures the answer meets a specific requirement. Uses an OpenAI-authored prompt from their public evals.
factuality - a factual consistency eval which, given a completion A and reference answer B evaluates whether A is a subset of B, A is a superset of B, A and B are equivalent, A and B disagree, or A and B differ, but difference don't matter from the perspective of factuality. Uses the prompt from OpenAI's public evals.
answer-relevance - ensure that LLM output is related to original query
classifier - see classifier grading docs.
moderation - see moderation grading docs.
select-best - compare outputs from multiple test cases and choose a winner

RAG-based (requires query and/or context vars):

context-recall - ensure that ground truth appears in context
context-relevance - ensure that context is relevant to original query
context-faithfulness - ensure that LLM output uses the context

Examples (output-based)

Example of llm-rubric and/or model-graded-closedqa:

assert:
  - type: model-graded-closedqa # or llm-rubric
    # Make sure the LLM output adheres to this criteria:
    value: Is not apologetic

Example of factuality check:

assert:
  - type: factuality
    # Make sure the LLM output is consistent with this statement:
    value: Sacramento is the capital of California

For more information on factuality, see the guide on LLM factuality.

Here's an example output that indicates PASS/FAIL based on LLM assessment (see example setup and outputs):

Using variables in the rubric

You can use test vars in the LLM rubric. This example uses the question variable to help detect hallucinations:

providers:
  - openai:gpt-4o-mini
prompts:
  - file://prompt1.txt
  - file://prompt2.txt
defaultTest:
  assert:
    - type: llm-rubric
      value: 'Says that it is uncertain or unable to answer the question: "{{question}}"'
tests:
  - vars:
      question: What's the weather in New York?
  - vars:
      question: Who won the latest football match between the Giants and 49ers?

Examples (RAG-based)

RAG metrics require variables named context and query. You must also set the threshold property on your test (all scores are normalized between 0 and 1).

Here's an example config of a RAG-based knowledge bot that evaluates RAG context metrics:

prompts:
  - |
    You are an internal corporate chatbot.
    Respond to this query: {{query}}
    Here is some context that you can use to write your response: {{context}}
providers:
  - openai:gpt-4
tests:
  - vars:
      query: What is the max purchase that doesn't require approval?
      context: file://docs/reimbursement.md
    assert:
      - type: contains
        value: '$500'
      - type: factuality
        value: the employee's manager is responsible for approvals
      - type: answer-relevance
        threshold: 0.9
      - type: context-recall
        threshold: 0.9
        value: max purchase price without approval is $500. Talk to Fred before submitting anything.
      - type: context-relevance
        threshold: 0.9
      - type: context-faithfulness
        threshold: 0.9
  - vars:
      query: How many weeks is maternity leave?
      context: file://docs/maternity.md
    assert:
      - type: factuality
        value: maternity leave is 4 months
      - type: answer-relevance
        threshold: 0.9
      - type: context-recall
        threshold: 0.9
        value: The company offers 4 months of maternity leave, unless you are an elephant, in which case you get 22 months of maternity leave.
      - type: context-relevance
        threshold: 0.9
      - type: context-faithfulness
        threshold: 0.9

Examples (comparison)

The select-best assertion type is used to compare multiple outputs in the same TestCase row and select the one that best meets a specified criterion.

Here's an example of how to use select-best in a configuration file:

prompts:
  - 'Write a tweet about {{topic}}'
  - 'Write a very concise, funny tweet about {{topic}}'

providers:
  - openai:gpt-4

tests:
  - vars:
      topic: bananas
    assert:
      - type: select-best
        value: choose the funniest tweet

  - vars:
      topic: nyc
    assert:
      - type: select-best
        value: choose the tweet that contains the most facts

Overriding the LLM grader

By default, model-graded asserts use GPT-4 for grading. If you do not have access to GPT-4 or prefer not to use it, you can override the rubric grader. There are several ways to do this, depending on your preferred workflow:

Using the --grader CLI option:

promptfoo eval --grader openai:gpt-4o-mini

Using test.options or defaultTest.options on a per-test or testsuite basis:

defaultTest:
  options:
    provider: openai:gpt-4o-mini
tests:
  - description: Use LLM to evaluate output
    assert:
      - type: llm-rubric
        value: Is spoken like a pirate

Using assertion.provider on a per-assertion basis:

tests:
  - description: Use LLM to evaluate output
    assert:
      - type: llm-rubric
        value: Is spoken like a pirate
        provider: openai:gpt-4o-mini

Use the provider.config field to set custom parameters:

provider:
  - id: openai:gpt-4o-mini
    config:
      temperature: 0

Also note that custom providers are supported as well.

Multiple graders

Some assertions (such as answer-relevance) use multiple types of providers. To override both the embedding and text providers separately, you can do something like this:

defaultTest:
  options:
    provider:
      text:
        id: azureopenai:chat:gpt-4-deployment
        config:
          apiHost: xxx.openai.azure.com
      embedding:
        id: azureopenai:embeddings:text-embedding-ada-002-deployment
        config:
          apiHost: xxx.openai.azure.com

If you are implementing a custom provider, text providers require a callApi function that returns a ProviderResponse, whereas embedding providers require a callEmbeddingApi function that returns a ProviderEmbeddingResponse.

Overriding the rubric prompt

For the greatest control over the output of llm-rubric, you may set a custom prompt using the rubricPrompt property of TestCase or Assertion.

The rubric prompt has two built-in variables that you may use:

{{output}} - The output of the LLM (you probably want to use this)
{{rubric}} - The value of the llm-rubric assert object

In this example, we set rubricPrompt under defaultTest, which applies it to every test in this test suite:

defaultTest:
  options:
    rubricPrompt: >
      [
        {
          "role": "system",
          "content": "Grade the output by the following specifications, keeping track of the points scored:\n\nDid the output mention {{x}}? +1 point\nDid the output describe {{y}}? +1 point\nDid the output ask to clarify {{z}}? +1 point\n\nCalculate the score but always pass the test. Output your response in the following JSON format:\n{pass: true, score: number, reason: string}"
        },
        {
          "role": "user",
          "content": "Output: {{ output }}"
        }
      ]

See the full example.

select-best rubric prompt

For control over the select-best rubric prompt, you may use the variables {{outputs}} (list of strings) and {{criteria}} (string). It expects the LLM output to contain the index of the winning output.

Classifiers

Classifiers can be used to detect tone, bias, toxicity, helpfulness, and much more. See classifier documentation.

Other assertion types

For more info on assertions, see Test assertions.

Examples (output-based)​

Using variables in the rubric​

Examples (RAG-based)​

Examples (comparison)​

Overriding the LLM grader​

Multiple graders​

Overriding the rubric prompt​

select-best rubric prompt​

Classifiers​

Other assertion types​