Answer: Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.

## Analysis of the Problem The core issue is that a foundation model (FM) from Amazon Bedrock is performing poorly when processing complex scientific terminology from research papers, despite multiple prompt engineering attempts. The problem stems from the model's lack of domain-specific knowledge and vocabulary. ## Evaluation of Options **A: Use few-shot prompting to define how the FM can answer the questions.** - This approach has already been attempted (as stated: "After multiple prompt engineering attempts"). - Few-shot prompting provides examples to guide the model but doesn't fundamentally improve its understanding of specialized vocabulary or domain-specific concepts. - While useful for formatting or style guidance, it's insufficient for addressing deep domain knowledge gaps. **B: Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.** - **This is the optimal solution.** Domain adaptation fine-tuning involves further training the foundation model on domain-specific data (in this case, research papers with complex scientific terminology). - This process allows the model to learn the specialized vocabulary, context, and relationships between scientific concepts. - Fine-tuning adapts the model's weights to better understand and generate responses relevant to the scientific domain. - Amazon Bedrock supports customization through fine-tuning, making this a practical implementation approach. **C: Change the FM inference parameters.** - Adjusting parameters like temperature, top-p, or max tokens can influence response creativity and length but doesn't address the fundamental issue of domain knowledge. - These parameters control how the model generates text but not what knowledge it possesses. - This would be a superficial adjustment that doesn't solve the core problem of scientific terminology comprehension. **D: Clean the research paper data to remove complex scientific terms.** - This approach would fundamentally undermine the chatbot's purpose. Removing complex scientific terms would strip the research papers of their essential content. - The chatbot needs to understand and process these terms to provide accurate answers. - Data cleaning for simplification would reduce the quality and accuracy of responses, making the chatbot less useful for its intended purpose. ## Recommended Solution **Domain adaptation fine-tuning (Option B)** is the most effective approach because: 1. It directly addresses the root cause: the model's lack of familiarity with scientific terminology 2. It builds upon the existing foundation model capabilities rather than working around limitations 3. It provides a sustainable solution that improves performance across all queries in the scientific domain 4. It aligns with AWS best practices for customizing foundation models for specific use cases Fine-tuning with domain-specific data will enable the model to better understand scientific concepts, terminology, and context, leading to more accurate and relevant responses from the research paper database.