AI World 1 · GenAI Foundations · Stage 3

Shape the Answer

Temperature, length, stop sequences, few-shot and chain-of-thought, and a prompt that cannot be hijacked.

Inference parameters
Prompt techniques
Prompt risks
Prompt Management

Domain: AI and machine learning
World: AI World 1: GenAI Foundations (Associate)
Game type: Solution board: Drop services into each row's slots, then run records through them.
Time: About 25 minutes on a first attempt (estimated)
Exam guides: AIF-C01Task 3.1: Describe design considerations for applications that use foundation models (FMs); Task 3.2: Choose effective prompt engineering techniques
MLA-C02Skill 2.2.8: Apply customization techniques for AI solutions (for example, task-specific prompt engineering, fine-tuning); Skill 3.3.7: Manage prompts (for example, Amazon Bedrock Prompt Management)
AIP-C01Skill 1.6.1: Create effective model instruction frameworks to control FM behavior and outputs (for example, by using Amazon Bedrock Prompt Management to enforce role definitions, Amazon Bedrock Guardrails to enforce responsible AI guidelines, template configurations to format responses); Skill 1.6.3: Implement comprehensive prompt management and governance systems to ensure consistency and oversight of FM operations (for example, by using Amazon Bedrock Prompt Management to create parameterized templates and approval workflows); Skill 1.6.5: Enhance FM performance to refine prompts iteratively and improve response quality beyond basic prompting techniques (for example, by using structured input components, output format specifications, chain-of-thought instruction patterns, feedback loops); Skill 4.2.4: Enhance FM performance to achieve optimal results for specific GenAI use cases (for example, by using model-specific parameter configurations, A/B testing to evaluate improvements, appropriate temperature and top-k/top-p selection based on requirements); Skill 5.2.1: Resolve content handling issues to ensure that necessary information is processed completely in FM interactions (for example, by using context window overflow diagnostics, dynamic chunking strategies, prompt design optimization, truncation-related error analysis)