A chatbot built on an Amazon Bedrock foundation model searches a large corpus of research papers, but it performs poorly because of many specialized scientific terms. After prompt engineering, performance is still inadequate. What should the company do to improve the chatbot?

The correct answer is to perform domain-adaptation fine-tuning. This process involves further training the foundation model on a specific dataset relevant to the specialized scientific domain. By exposing the model to a large volume of research papers, it learns the nuances, terminology, and contextual relationships within that field, significantly improving its understanding and generation of specialized content. Few-shot prompting can help guide the model's output style or format but won't fundamentally improve its understanding of complex, domain-specific terminology if it hasn't been trained on it. Adjusting inference parameters (like temperature or top-p) can influence the creativity or determinism of responses but won't address a lack of domain knowledge. Cleaning the research paper texts to remove complex terms would defeat the purpose of the chatbot, which is to interact with these specialized documents.