1 / 6
Prompt design fundamentals
In this lesson
Prompt design fundamentals
Construct prompts that explicitly specify role, task, context, format, and examples, applying the structural anatomy documented in AWS Bedrock prompt engineering guidance[^2][^6].
You'll be able to
- Construct prompts that explicitly specify role, task, context, format, and examples, applying the structural anatomy documented in AWS Bedrock prompt engineering guidance[^2][^6].
- Classify an AI's pedagogical or operational role in a given assignment (mentor, tutor, coach, teammate, student, simulator, or tool) and select the corresponding prompt pattern, following the framework Mollick and Mollick established for assigning AI in learning contexts[^1].
- Evaluate whether a prompt provides sufficient context and constraints for the target model, referencing model-specific formatting requirements (such as XML markup for Anthropic Claude or conversational delimiters for Amazon Titan) as outlined in AWS Bedrock documentation[^2][^3][^4].
- Apply few-shot prompting techniques by embedding example input-output pairs into a prompt to calibrate model behavior, distinguishing this approach from zero-shot prompting as described in AWS Bedrock prompt engineering best practices[^7].
- Iterate on prompt designs by testing outputs, identifying failure modes, and refining instructions, context, or examples, rather than expecting a single prompt draft to produce production-ready results[^1][^6].