Q57 — AWS AIF-C01 Ch.3

Question 57 of 100 | ← Chapter 3

A manufacturing company wants to create a virtual assistant for internal employees. This virtual assistant should answer technical questions exclusively based on the company’s proprietary engineering documentation. The company wants to minimize model hallucination and reduce effort related to data preparation. Which solution will satisfy these requirements with minimal operational overhead?

• A. Fine-tune a foundation model (FM) using the company’s knowledge base
• B. Use a foundation model trained on data related to the manufacturing domain
• C. Implement retrieval-augmented generation (RAG) ✓
• D. Use in-context learning by embedding extensive instructions in the prompt

Correct Answer: C. Implement retrieval-augmented generation (RAG)

Explanation

For a manufacturing company building a virtual assistant for internal employees, core requirements are: answering technical questions solely based on proprietary engineering documentation, minimizing model hallucination, reducing data preparation effort, and achieving these with minimal operational overhead. Option A — fine-tuning an FM on the company’s knowledge base — requires significant data preparation and model tuning effort and does not guarantee minimized hallucination. Option B — using an FM pre-trained on manufacturing-domain data — provides domain-relevant knowledge but lacks customization to the company’s specific proprietary documentation. Option C — implementing retrieval-augmented generation (RAG) — enables the assistant to retrieve information directly from proprietary engineering documentation and generate answers grounded in actual content, minimizing hallucination, simplifying data preparation (as retrieval is relatively lightweight), and requiring low operational overhead. Option D — in-context learning via lengthy prompt instructions — demands substantial data and compute resources and may still fail to fully leverage proprietary documentation. Therefore, option C — RAG — best satisfies the requirements with minimal operational overhead.