How to Evaluate your LLM Response
Learn how to use Command-R to evaluate natural language responses with an example of grading formality.
You can leverage our Command models 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
0.8API Request
import cohere
co = cohere.Client(api_key="Your API key")
response = co.chat(
model="command-a-03-2025",
message="""
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.text)