AIF-C01 · Question #402
An AI Practitioner is using an LLM-as-a-judge in Amazon Bedrock to evaluate the quality of agent responses in a production environment. The AI practitioner wants to apply a built-in metric that…
The correct answer is B. Completeness. The completeness metric evaluates how fully an agent’s response addresses all parts and requirements of a user’s prompt, making it the correct built-in metric for assessing thorough coverage of each question or request.
Question
An AI Practitioner is using an LLM-as-a-judge in Amazon Bedrock to evaluate the quality of agent responses in a production environment. The AI practitioner wants to apply a built-in metric that assesses how thoroughly the agent responses address all parts of each prompt or question. Which metric will meet these requirements?
Options
- ARecall-Oriented Understudy for Gisting Evaluation (ROUGE)
- BCompleteness
- CFollowing instructions
- DRefusal
How the community answered
(51 responses)- A2% (1)
- B94% (48)
- D4% (2)
Explanation
The completeness metric evaluates how fully an agent’s response addresses all parts and requirements of a user’s prompt, making it the correct built-in metric for assessing thorough coverage of each question or request.
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