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How to Grade Claude Outputs in Your Prompt Testing Workflow?

What’s Next After Generating Responses in Claude Prompt Evaluation?

Discover why grading generated responses is the critical next step in Claude prompt evaluation workflows, enabling data-driven improvements over hasty rewrites or deployment.

Question

You’re running a prompt evaluation workflow. You’ve used Claude to generate some responses. What’s the next step?

A. Deploy to production
B. Rewrite the original prompt
C. Create more test questions
D. Feed the responses through a grader

Answer

D. Feed the responses through a grader

Explanation

In a prompt evaluation workflow, after generating responses with Claude against test cases, the essential next step is to feed those responses through a grader—either a rule-based scorer, LLM-as-judge, or rubric—to objectively assess quality metrics like accuracy, relevance, conciseness, or hallucination rates.

This automated or semi-automated grading quantifies performance across the test suite, revealing patterns such as failure modes, score distributions, and areas needing iteration, before any rewriting (B, premature without data), more tests (C, expands scope post-analysis), or deployment (A, reckless absent validation).