Answer: Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.

## Detailed Explanation As an AWS Certified AI Practitioner expert, I'll analyze the prompt engineering strategies for sentiment analysis using an LLM on Amazon Bedrock. ### Optimal Strategy: Few-Shot Prompting (Option A) **Option A** recommends providing examples of text passages with corresponding positive or negative labels in the prompt, followed by the new text passage to be classified. This approach is optimal because: 1. **Contextual Learning**: LLMs excel at pattern recognition. By providing labeled examples, the model can infer the task (sentiment classification) and the desired output format (positive/negative labels). 2. **Task Specification**: The examples clearly define what constitutes "positive" and "negative" sentiment within the company's specific context, reducing ambiguity. 3. **Format Consistency**: Demonstrating the expected input-output pattern helps ensure the LLM produces responses in the correct format. 4. **Amazon Bedrock Best Practice**: This aligns with AWS documentation on effective prompt engineering for classification tasks, where few-shot examples improve accuracy and reliability. ### Analysis of Other Options **Option B**: Providing a detailed explanation of sentiment analysis and how LLMs work is suboptimal because: - LLMs don't require theoretical explanations; they need practical demonstrations of the task. - This approach wastes tokens without improving classification performance. - It may introduce unnecessary complexity without providing actionable guidance. **Option C**: Providing only the new text passage without context is ineffective because: - The LLM lacks clear instructions about the specific task. - Without examples, the model might produce inconsistent or incorrect output formats. - This zero-shot approach typically yields lower accuracy for specialized tasks like sentiment classification. **Option D**: Providing examples of unrelated tasks (like text summarization) is counterproductive because: - It introduces irrelevant context that may confuse the model. - The LLM might attempt to perform multiple tasks simultaneously, reducing sentiment classification accuracy. - This violates the principle of task-specific prompt design. ### Technical Considerations for Amazon Bedrock When implementing this on Amazon Bedrock: 1. **Example Selection**: Choose diverse, representative examples that cover edge cases. 2. **Prompt Structure**: Use clear formatting (e.g., "Text: [passage]\nSentiment: [label]") to separate examples from the target text. 3. **Model Selection**: Different foundation models on Bedrock may respond differently to few-shot prompts, requiring testing and optimization. This approach represents industry best practice for classification tasks with LLMs, balancing effectiveness with token efficiency.