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Answer: Implement a context management system to track and maintain conversation state across turns.
To optimize an Azure OpenAI model for a chatbot application, implementing a context management system (B) is crucial for tracking and maintaining conversation state across multiple turns. This allows the model to generate responses that are contextually relevant and coherent. Using a single, broad prompt (A) may not be sufficient to handle a wide range of user inputs. Fine-tuning the model on a diverse dataset (C) is also important but does not directly address the need for context management. Limiting the model's responses to a predefined set of topics (D) may restrict its ability to handle a wide range of user inputs.
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You are tasked with optimizing an Azure OpenAI model to generate high-quality responses for a chatbot application. The model should be able to handle a wide range of user inputs and maintain context across multiple turns in a conversation. Which of the following techniques should you consider implementing to achieve this?
A
Use a single, broad prompt for all user inputs to maintain consistency.
B
Implement a context management system to track and maintain conversation state across turns.
C
Fine-tune the model on a diverse dataset of conversational examples to improve its understanding of various topics.
D
Limit the model's responses to a predefined set of topics to ensure relevance.
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