Q58 — AWS AIF-C01 Ch.1

Question 58 of 100 | ← Chapter 1

A research company has implemented a chatbot using foundation models (FMs) from Amazon Bedrock. The chatbot searches a database of academic research papers to answer user questions. After extensive prompt engineering attempts, the company realizes that the FM's performance is poor due to the highly complex scientific terminology in the research papers. What should the company do to improve the chatbot's performance?

• A. Use concise prompts to define how the FM should answer questions.
• B. Apply domain-adaptive fine-tuning to adapt the FM to complex scientific terminology. ✓
• C. Modify the FM's inference parameters.
• D. Clean the research paper data by removing complex scientific terminology.

Correct Answer: B. Apply domain-adaptive fine-tuning to adapt the FM to complex scientific terminology.

Explanation

This question tests methods for improving chatbot performance. In scenarios involving complex scientific terminology, domain-adaptive fine-tuning enables the foundation model to better understand and generate responses using domain-specific vocabulary. Option A’s concise prompting offers limited improvement; Option C’s inference parameter changes are unlikely to specifically address terminology complexity; Option D’s removal of terminology would discard critical information. Therefore, Option B—domain-adaptive fine-tuning—is the correct choice to enhance chatbot performance.