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Select Best

The select-best assertion compares multiple outputs in the same test case and selects the one that best meets a specified criterion. This is useful for comparing different prompt or model variations to determine which produces the best result.

How to use it​

To use the select-best assertion type, add it to your test configuration like this:

assert:
  - type: select-best
    value: 'choose the most concise and accurate response'

Note: This assertion requires multiple prompts or providers to generate different outputs to compare.

How it works​

The select-best checker:

  1. Takes all outputs from the test case
  2. Evaluates each output against the specified criterion
  3. Selects the best output
  4. Returns pass=true for the winning output and pass=false for others

Example Configuration​

Here's a complete example showing how to use select-best to compare different prompt variations:

prompts:
  - 'Write a tweet about {{topic}}'
  - 'Write a very concise, funny tweet about {{topic}}'
  - 'Compose a tweet about {{topic}} that will go viral'
providers:
  - openai:gpt-4
tests:
  - vars:
      topic: 'artificial intelligence'
    assert:
      - type: select-best
        value: 'choose the tweet that is most likely to get high engagement'
  - vars:
      topic: 'climate change'
    assert:
      - type: select-best
        value: 'choose the tweet that best balances information and humor'

Overriding the Grader​

Like other model-graded assertions, you can override the default grader:

  1. Using the CLI:

    promptfoo eval --grader openai:gpt-4.1-mini

  2. Using test options:

    defaultTest:
      options:
        provider: openai:gpt-4.1-mini

  3. Using assertion-level override:

    assert:
      - type: select-best
        value: 'choose the most engaging response'
        provider: openai:gpt-4.1-mini

Customizing the Prompt​

You can customize the evaluation prompt using the rubricPrompt property:

defaultTest:
  options:
    rubricPrompt: |
      Here are {{ outputs | length }} responses:
      {% for output in outputs %}
      Output {{ loop.index0 }}: {{ output }}
      {% endfor %}

      Criteria: {{ criteria }}

      Analyze each output against the criteria.
      Choose the best output by responding with its index (0 to {{ outputs | length - 1 }}).

Further reading

See model-graded metrics for more options.