Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?

A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.

Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?
A . Limit the number of relevant documents available for the RAG application to retrieve from
B . Pick a smaller LLM that is domain-specific
C . Limit the number of queries a customer can send per day
D . Use the largest LLM possible because that gives the best performance for any general queries

Answer: B

Explanation:

For a small, cost-conscious startup in the cancer research field, choosing a domain-specific and smaller LLM is the most effective strategy.

Here’s why B is the best choice:

Domain-specific performance: A smaller LLM that has been fine-tuned for the domain of cancer research will outperform a general-purpose LLM for specialized queries. This ensures high-quality responses without needing to rely on a large, expensive LLM.

Cost-efficiency: Smaller models are cheaper to run, both in terms of compute resources and API usage costs. A domain-specific smaller LLM can deliver good quality responses without the need for the extensive computational power required by larger models.

Focused knowledge: In a specialized field like cancer research, having an LLM tailored to the subject matter provides better relevance and accuracy for queries, while keeping costs low. Large, general-purpose LLMs may provide irrelevant information, leading to inefficiency and higher costs.

This approach allows the startup to balance quality, cost, and customer satisfaction effectively, making it the most suitable strategy.