Databricks Generative AI Engineer Associate Question 156
Single answerYou are developing a customer support chatbot for a retail company that requires accurate context understanding, multi-turn conversations, and the ability to generate human-like responses. The model must also support fine-tuning with your domain-specific data. Which LLM would be the best choice for this application?
- A
A smaller, open-source LLM with low computational requirements but limited fine-tuning capabilities
- B
A general-purpose LLM with high context length, fine-tuning support, and strong conversational abilities
- C
A domain-specific LLM pre-trained on retail data but without fine-tuning capabilities
- D
An LLM optimized for code generation with high accuracy in programming tasks
Show answer and explanation
Correct answer: B
Explanation
For a customer support chatbot with requirements for accurate context understanding, multi-turn conversational abilities, and fine-tuning, a general-purpose LLM with high context length and strong fine-tuning support is the most appropriate choice. This ensures the model can handle complex dialogues, generate human-like responses, and adapt to the specific needs of the retail domain.
- A. Incorrect.
This option is not suitable because a smaller LLM with limited fine-tuning capabilities will struggle with the complexity of multi-turn conversations and domain-specific customization required for the chatbot.
- B. Correct.
This is the correct answer because a general-purpose LLM with high context length and fine-tuning capabilities can handle multi-turn conversations, generate human-like responses, and be adapted to the retail domain using your specific data.
- C. Incorrect.
While a domain-specific LLM pre-trained on retail data might help with understanding the retail context, the lack of fine-tuning capabilities makes it less adaptable to specific requirements of your chatbot.
- D. Incorrect.
This option is not appropriate as the LLM is optimized for code generation, which is unrelated to the task of generating conversational responses for a customer support chatbot.