> ## Documentation Index > Fetch the complete documentation index at: https://www.adaline.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # LLM-as-a-Judge > Use an LLM to evaluate your prompt responses against a custom rubric LLM-as-a-Judge is the most versatile evaluator in Adaline. It uses an LLM to assess your prompt outputs against a custom [rubric](/docs/others/glossary#rubric), excelling at qualitative assessment where nuanced judgment matters more than simple metrics. ## Set up LLM-as-a-Judge Open the prompt you want to test and click **Evaluate** in the prompt header. Opening the Evaluate tab from a prompt Add the LLM-as-a-Judge evaluator from the evaluator menu. Adding the LLM-as-a-Judge evaluator Give a name to the evaluator and link a dataset containing your test cases. Linking a dataset to LLM-as-a-Judge Write the rubric that defines your evaluation criteria. A well-crafted rubric is the key to high-quality evaluations — it should be specific, actionable, and aligned with your success metrics. Defining a rubric for LLM-as-a-Judge Click **Evaluate** to execute the evaluation and see the results. LLM-as-a-Judge evaluation results ## Writing effective rubrics Your rubric directly determines the quality of the evaluation. Follow these guidelines: * **Be specific** — Define clear criteria for each score level. Avoid vague terms like "good" or "bad" without context. * **Be actionable** — Include concrete examples of what constitutes each rating. * **Align with goals** — Match your rubric to your actual production success metrics. * **Use a consistent scale** — Define a numerical scale (e.g., 1–4 or 1–5) with clear definitions for each level. ## Example rubrics ### Customer support response quality ```markdown theme={null} Evaluate this customer support response using the following criteria: Scoring Scale (1-4): 4 - Excellent: Completely resolves the issue, professional tone, anticipates follow-up needs 3 - Good: Addresses the main concern clearly and professionally 2 - Fair: Partially helpful but missing key information or context 1 - Poor: Fails to address the issue or uses inappropriate tone Evaluation Factors: - Problem resolution completeness - Professional communication standards - Information accuracy - User experience quality Provide a score and brief justification for your assessment. ``` ### Content marketing effectiveness ```markdown theme={null} Rate this content piece on effectiveness for our target audience (1-5): 5 - Outstanding: Highly engaging, actionable insights, clear value proposition 4 - Strong: Good engagement with solid practical value 3 - Adequate: Informative but limited engagement or actionability 2 - Weak: Basic information with minimal practical value 1 - Poor: Lacks clarity, value, or relevance to target audience Consider these dimensions: - Audience alignment and relevance - Practical value and actionability - Engagement potential - Brand positioning effectiveness ``` ### Technical documentation quality ```markdown theme={null} Assess this feature documentation quality (1-4): 4 - Comprehensive: Clear explanation, complete coverage, excellent user guidance 3 - Good: Well-explained with adequate detail and guidance 2 - Acceptable: Basic explanation but missing important details or clarity 1 - Inadequate: Confusing, incomplete, or lacks necessary user guidance Evaluation Areas: - Technical accuracy and completeness - User comprehension and clarity - Implementation guidance quality - Overall user experience ``` ### Brand voice consistency ```markdown theme={null} Evaluate brand voice alignment (1-3 scale): 3 - Excellent Alignment: Perfect adherence to brand guidelines, authentic voice 2 - Good Alignment: Generally consistent with minor deviations 1 - Poor Alignment: Inconsistent with established brand voice Assessment Criteria: - Tone consistency with brand guidelines - Language and terminology alignment - Audience appropriateness - Brand personality expression ``` ## When to use LLM-as-a-Judge is best suited for: * **Response quality assessment** — Evaluating helpfulness, accuracy, and completeness. * **Tone and voice validation** — Ensuring consistent brand voice and appropriate communication style. * **Factual accuracy checking** — Verifying that responses contain correct information. * **User satisfaction prediction** — Assessing whether responses would meet user expectations. * **Multi-turn conversation quality** — Evaluating context retention and coherence across [chat turns](/docs/evaluate/evaluate-multi-turn-chat). For technical validation (format checking, business logic, pattern matching), consider [JavaScript](/docs/evaluate/javascript) or [Text Matcher](/docs/evaluate/text-matcher) evaluators instead. ## Next steps Write custom code to validate structured outputs. Review LLM-as-a-Judge results in detail.