Answer: Few-shot prompting using 2–3 examples of high-quality scientific summaries

## Explanation **Correct Answer: C - Few-shot prompting using 2–3 examples of high-quality scientific summaries** ### Why this is correct: 1. **Domain-specific context**: The problem states that zero-shot attempts "miss subtle domain context." Few-shot prompting provides concrete examples that demonstrate the specific domain knowledge, terminology, and style required for scientific paper summaries. 2. **Learning from examples**: By providing 2-3 high-quality examples, the LLM can learn the patterns, technical depth, and domain-specific nuances needed for accurate scientific summaries. 3. **Most direct improvement**: Compared to other options, few-shot prompting directly addresses the gap in domain understanding by showing the model exactly what is expected. ### Why other options are less effective: - **A. Role-based prompting**: While helpful for establishing perspective, it doesn't provide the specific domain knowledge or examples needed to capture subtle scientific context. - **B. Chain-of-thought reasoning**: This helps with step-by-step reasoning but doesn't specifically address domain knowledge gaps or provide examples of high-quality scientific summaries. - **D. Increasing model depth via recursive prompting**: This is not a standard prompting technique and would likely add complexity without directly addressing the domain knowledge gap. ### Key Takeaway: When an LLM lacks domain-specific understanding, providing concrete examples (few-shot prompting) is often the most effective way to bridge that gap, as it shows the model exactly what is expected in terms of content, style, and technical depth.

Author: Ritesh Yadav