GENERATIVE-AI-ENGINEER-ASSOCIATE · Question #3
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…
The correct answer is B. Pick a smaller LLM that is domain-specific. Choosing a smaller, domain-specific LLM (option B) is the optimal balance of cost and quality for a specialized use case like cancer research. Domain-specific models are pre-trained or fine-tuned on relevant medical/scientific data, so they perform better on niche queries than…
Question
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?
Options
- ALimit the number of relevant documents available for the RAG application to retrieve from
- BPick a smaller LLM that is domain-specific
- CLimit the number of queries a customer can send per day
- DUse the largest LLM possible because that gives the best performance for any general queries
How the community answered
(32 responses)- A13% (4)
- B78% (25)
- C6% (2)
- D3% (1)
Explanation
Choosing a smaller, domain-specific LLM (option B) is the optimal balance of cost and quality for a specialized use case like cancer research. Domain-specific models are pre-trained or fine-tuned on relevant medical/scientific data, so they perform better on niche queries than large general-purpose models - often at a fraction of the inference cost. Option A (limiting documents) degrades quality. Option C (limiting queries) harms user experience and doesn't reduce per-query cost. Option D (largest LLM) maximizes cost and is overkill for a specialized domain where a smaller domain-tuned model can outperform it.
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