AWS Certified AI Practitioner AIF-C01 Practice Question
A developer must prevent an LLM from returning personal data in its responses. Which prompt-engineering practice provides this guardrail at the prompt level?
Increase the maximum output tokens so responses can fully elaborate.
Insert a negative prompt that instructs the model to exclude any personal data.
Provide chain-of-thought examples so the model explains its reasoning step-by-step.
Set the temperature parameter close to 0 to make outputs more deterministic.
Adding a negative instruction (negative prompt) inside the prompt explicitly tells the model what it must not include, such as personal data. This operates as a guardrail before generation begins. Simply lowering temperature, using chain-of-thought examples, or enlarging the token limit affects creativity or verbosity but does not directly block disallowed content.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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