Question 5
Domain 5: Security, Compliance, and Governance for AI SolutionsA company wants to create a chatbot that answers questions about human resources policies. The company is using a large language model (LLM) and has a large digital documentation base. Which technique should the company use to optimize the generated responses?
Correct answer: A
Explanation
RAG fits this use case because the source says search and recommendation systems “typically combin[e] them with a retrieval-augmented generation (RAG) pattern.” A chatbot over HR policies needs to retrieve relevant documents from the company’s “large digital documentation base” so the LLM can ground answers in current policy text rather than relying only on its training data.
Why each option is right or wrong
A. Use Retrieval Augmented Generation (RAG).
The question describes an LLM chatbot that must answer from a large internal policy repository, which is exactly the kind of search-and-retrieval workflow the source ties to embedding/vector-similarity plus RAG. Under the cited material, RAG is the pattern used when the model needs to pull relevant documents from a large digital documentation base at inference time so responses are grounded in the company’s current HR text rather than only the model’s pre-trained knowledge.
B. Use few-shot prompting.
Few-shot prompting supplies examples, not document retrieval from a knowledge base.
C. Set the temperature to 1.
Temperature controls randomness in generation, not access to internal policy documents.
D. Decrease the token size.
Token size affects context length, not retrieval of HR policy information.