Databricks Generative AI Engineer Associate Question 153
Single answerYou are tasked with developing a customer support chatbot for a large e-commerce platform. The chatbot must handle a wide variety of user queries, maintain conversational context, and provide accurate answers based on product details from a proprietary database. The application needs to prioritize cost-efficiency without compromising significantly on performance. Which LLM should you select for this application?
- A
An open-source LLM fine-tuned on your proprietary database
- B
A proprietary LLM with state-of-the-art performance, deployed via API
- C
A smaller open-source LLM with no fine-tuning, optimized for inference speed
- D
A general-purpose LLM fine-tuned on customer support datasets from third parties
Show answer and explanation
Correct answer: A
Explanation
For developing a customer support chatbot for a specific e-commerce platform, fine-tuning an open-source LLM on your proprietary database strikes the best balance between cost-efficiency and performance. This approach ensures the chatbot can provide accurate, context-aware responses tailored to the unique requirements of the platform.
- A. Correct.
Fine-tuning an open-source LLM on your proprietary database ensures the model can provide accurate and context-aware responses specific to your e-commerce platform. This approach is cost-efficient and aligns with the requirements.
- B. Incorrect.
While a proprietary LLM may offer state-of-the-art performance, it is likely to incur higher costs due to API usage and may not focus specifically on your proprietary data.
- C. Incorrect.
A smaller open-source LLM optimized for inference speed may be cost-efficient, but without fine-tuning, it would lack the necessary domain-specific knowledge to provide accurate responses.
- D. Incorrect.
A general-purpose LLM fine-tuned on third-party customer support datasets may offer some relevant knowledge, but it will not be tailored to your proprietary database, leading to less accurate answers.