LLM-as-Judge Evaluation AI Prompt

Design a reliable LLM-as-judge system to evaluate the quality of data analysis outputs at scale.

Human evaluation is the gold standard but does not scale. LLM-as-judge enables automated quality evaluation across thousands of outputs — if done correctly.

1. When LLM-as-judge is appropriate:
   - When human evaluation is too expensive or slow to run at scale
   - For outputs where correctness has a nuanced, rubric-based definition
   - As a first-pass filter before human review of borderline cases
   - NOT appropriate as a sole quality gate for high-stakes outputs

2. Judge prompt design (critical — garbage in, garbage out):

   a. Role and task:
   'You are an expert data analyst evaluating the quality of an AI-generated data analysis. Your evaluation must be objective and based only on the criteria below.'

   b. Evaluation rubric (specific dimensions with clear descriptions):
   'Score the analysis on each dimension from 1 to 5:
   - Factual accuracy (1–5): Are all numbers and statistics correctly stated? Does the analysis accurately describe the data?
   - Logical reasoning (1–5): Does the analysis reason correctly from data to conclusions? Are any logical leaps unjustified?
   - Completeness (1–5): Does the analysis address the question fully? Are important insights missing?
   - Clarity (1–5): Is the analysis clearly written and easy for a business audience to understand?
   - Actionability (1–5): Does the analysis lead to a clear, specific recommended action?'

   c. Output format:
   'Return a JSON object: {"factual_accuracy": N, "logical_reasoning": N, "completeness": N, "clarity": N, "actionability": N, "overall": N, "key_issues": ["issue 1", "issue 2"], "strengths": ["strength 1"]}'

3. Reliability safeguards:
   - Reference answer: provide the correct answer alongside the candidate output so the judge can compare
   - Position bias mitigation: if comparing two outputs, run the judge twice with A/B order swapped; average the scores
   - Calibration: measure judge agreement with human evaluators on 50 calibration examples; adjust if disagreement > 20%

4. Judge validation:
   - Test the judge on known good outputs (should score > 4 on all dimensions)
   - Test on known bad outputs (should score < 2 on accuracy when factual errors are present)
   - Measure consistency: run the same input through the judge 5 times and check score variance (should be < 0.5 std)

Return: judge prompt, calibration procedure, consistency test, and a dashboard for tracking judge-assessed quality over time.