Evaluate your LLM response
You can leverage Command R 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
1 import cohere 2 3 co = cohere.Client(api_key='Your API key') 4 response = co.chat( 5 message=""" 6 You are an AI grader that given an output and a criterion, grades the completion based on 7 the prompt and criterion. Below is a prompt, a completion, and a criterion with which to grade 8 the completion. You need to respond according to the criterion instructions. 9 10 ## Output 11 The customer's UltraBook X15 displayed a black screen, likely due to a graphics driver issue. 12 Chat support advised rolling back a recently installed driver, which fixed the issue after a 13 system restart. 14 15 ## Criterion 16 Rate the ouput text with a score between 0 and 1. 1 being the text was written in a formal 17 and business appropriate tone and 0 being an informal tone. Respond only with the score. 18 """, 19 ) 20 print(response)