How to Evaluate your LLM Response

You can leverage Command A to evaluate natural language responses that cannot be easily scored with manual rules.

Prompt

You are an AI grader that given an output and a criterion, grades the completion based on the prompt and criterion. Below is a prompt, a completion, and a criterion with which to
grade the completion. You need to respond according to the criterion instructions.
## Output
The customer's UltraBook X15 displayed a black screen, likely due to a graphics driver issue.
Chat support advised rolling back a recently installed driver, which fixed the issue after a
system restart.
## Criterion 
Rate the ouput text with a score between 0 and 1. 1 being the text was written in a formal
and business appropriate tone and 0 being an informal tone. Respond only with the score.

Output

0.8

API Request

PYTHON

import cohere
co = cohere.ClientV2(api_key="<YOUR API KEY>")
response = co.chat(
    model="command-a-plus-05-2026",
    messages=[
        {
            "role": "user",
            "content": """
    You are an AI grader that given an output and a criterion, grades the completion based on
    the prompt and criterion. Below is a prompt, a completion, and a criterion with which to grade
    the completion. You need to respond according to the criterion instructions.
    
    ## Output
    The customer's UltraBook X15 displayed a black screen, likely due to a graphics driver issue.
    Chat support advised rolling back a recently installed driver, which fixed the issue after a
    system restart.
    
    ## Criterion 
    Rate the ouput text with a score between 0 and 1. 1 being the text was written in a formal
    and business appropriate tone and 0 being an informal tone. Respond only with the score.
    """,
        }
    ],
)
print(response.message.content[0].text)