LLM-as-Judge Skill

LLM-based evaluation automatically assesses agent outputs using configurable criteria to determine quality. It analyzes relevance, coherence, and factual accuracy to produce structured feedback and scores, reducing manual review effort.

To evaluate chatbot responses for factual accuracy and bias, provide the prompt and agent response along with evaluation criteria. The skill produces a structured quality score and actionable feedback based on the specified parameters.

Yes, you can batch evaluate multiple prompts and responses in an evaluation pipeline. The skill supports batch processing to generate scores and structured feedback across various agent responses simultaneously.

The best way to benchmark and compare different agent responses is using LLM-based scoring across consistent criteria. This approach identifies strengths and gaps in customer support or tutoring assistants by applying uniform evaluation standards.

You retrieve evaluation configuration settings for the judge by using the configuration retrieval function. This provides the necessary configuration data to define and adjust the evaluation criteria before processing outputs.

Automated LLM evaluation for quality assurance relies on configured criteria and may miss nuanced contextual errors. It reduces manual review effort but should be monitored to ensure consistent judgments across complex or ambiguous agent outputs.