AWS Certified AI Practitioner AIF-C01 Practice Question
A team asks an Amazon Bedrock large language model to write a one-paragraph summary of a 50-page technical manual. The result omits important domain terminology and feels generic. According to prompt-engineering best practices, which adjustment is most likely to improve the relevance of the model's next response?
Add a brief description of the manual's subject matter, target audience, and essential terms to the prompt before asking for the summary
Increase the top-k value so the model samples from a larger token set
Lower the temperature parameter from 0.8 to 0.2 to make the model less random
Introduce a stop sequence after 120 tokens to limit the response length
Providing additional background or domain context in the prompt guides the model toward the desired subject matter and vocabulary. By stating details such as the manual's topic, intended audience, and key terms that should appear, the model anchors its generation to that information, producing a more accurate and relevant summary. Lowering temperature only reduces randomness, changing top-k alters sampling breadth, and adding a stop sequence merely truncates output; none of these directly supply the missing domain knowledge.
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What does providing domain-specific context in a prompt achieve?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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