Answer: Zero-shot prompting

## Explanation **Zero-shot prompting** is the correct approach because: 1. **Definition**: Zero-shot prompting involves asking a model to perform a task without providing any examples or demonstrations. The model relies solely on its pre-trained knowledge and understanding of the task description. 2. **Scenario Fit**: The engineer wants the model to "answer without any prior examples and rely only on the model's general knowledge" - this is the exact definition of zero-shot learning. 3. **Other options explained**: - **Code-generation prompting**: This is for generating code, not classification tasks. - **Retrieval-augmented prompting**: This involves retrieving external information or examples to augment the prompt, which contradicts the requirement of "no prior examples." - **Reinforcement learning prompting**: This refers to training models through reward-based learning, not a prompting style for inference. 4. **AWS Bedrock Context**: In AWS Bedrock, zero-shot prompting is commonly used for classification tasks where you want the model to categorize inputs based on its general understanding without providing labeled examples. **Key Takeaway**: When you need a model to perform a task based solely on its pre-existing knowledge without any examples, zero-shot prompting is the appropriate technique.

Author: Ritesh Yadav