QuestionQ11

Solution Design

You are testing an agent that generates natural-language responses to user queries, such as providing explanations or answering open-ended questions. The outputs must be evaluated for quality, semantic correctness, and reasoning. Which evaluator type is most appropriate for this scenario?

  • A JSON similarity, as it assesses the structural similarity of outputs to ensure they align accurately.
  • B LLM-as-a-Judge, as it provides flexible evaluation of semantic correctness and reasoning beyond exact matching.
  • C Deterministic evaluators, as they guarantee all outputs strictly match predefined rules for correctness.
  • D Exact match, as it ensures the agent output matches the expected response word-for-word.
Explanation

LLM-as-a-Judge can evaluate open-ended responses using contextual criteria such as answer quality, semantic correctness, and reasoning. Exact-match, JSON-similarity, and deterministic evaluators are better suited to outputs with fixed expected text, structure, or rules.

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