MDFT Pro, a well-known training agency, is optimizing their Azure OpenAI implementation for cost efficiency and performance in their student support system. Mark, the Technical Architect, is configuring token limits to ensure responses are appropriately sized for different types of student inquiries while managing costs effectively.
Mark needs to understand how token limits work to properly balance detailed course information responses with operational efficiency. The system handles various query types from short factual questions to complex course recommendation requests, and proper token limit configuration is crucial for maintaining both response quality and cost control.
The system is configured with the following settings:
When processing a student inquiry, the system returns this response:
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": "The founder of MDFT Pro is Mark Farragher.",
"role": "assistant"
}
}
],
"created": 1679014554,
"id": "chatcmpl-6usfny2yyjkbmESe36JdqQ6bDsc01",
"model": "gpt-3.5-turbo-0301",
"object": "chat.completion",
"usage": {
"completion_tokens": 86,
"prompt_tokens": 37,
"total_tokens": 123
}
}
Are the prompt_tokens included in the calculation of the Max response tokens limit?
Choose the correct answer from the options below.
Explanations for each answer: