AWS Certified Cloud Practitioner
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A data analyst uses Amazon Bedrock to generate financial summaries from quarterly reports. Sometimes, the model produces vague answers.
What should the analyst do to improve the output quality?
Real Exam
Community
RRitesh
0
A
Use a shorter prompt
B
Add clearer instructions and define output format
C
Randomize wording for creativity
D
Reduce temperature to zero
Explanation:
Explanation
When using Amazon Bedrock or any generative AI service, the quality of the output is highly dependent on the quality of the input prompt. Here's why option B is correct:
- Clearer instructions: Providing specific, detailed instructions helps the model understand exactly what you want
- Defined output format: Specifying the structure (e.g., bullet points, tables, specific sections) ensures consistent and organized results
- Context and constraints: Adding boundaries and requirements helps the model stay focused on the task
Why other options are incorrect:
- A) Use a shorter prompt: Shorter prompts often lack necessary context and can lead to more vague responses
- C) Randomize wording for creativity: This would likely make outputs less consistent and potentially more vague
- D) Reduce temperature to zero: While this makes outputs more deterministic, it doesn't address the fundamental issue of unclear instructions
Best practices for prompt engineering:
- Be specific about the desired format and structure
- Provide clear examples if possible
- Define any constraints or requirements
- Use role-playing to guide the model's response style
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