advanced-evaluationlisted

# Advanced Evaluation This skill covers production-grade techniques for evaluating LLM outputs using LLMs as judges. It synthesizes research from academic papers, industry practices, and practical implementation experience into actionable patterns for building reliable evaluation systems. **Key insight**: LLM-as-a-Judge is not a single technique but a family of approaches, each suited to different evaluation contexts. Choosing the right approach and mitigating known biases is the core competency this skill develops. ## When to Activate Activate this skill when: - Building automated evaluation pipelines for LLM outputs - Comparing multiple model responses to select the best one - Establishing consistent quality standards across evaluation teams - Debugging evaluation systems that show inconsistent results - Designing A/B tests for prompt or model changes - Creating rubrics for human or automated evaluation - Analyzing correlation between automated and human judgments ## Core Concepts ### The Evaluation Taxonomy Evaluation approaches fall into two primary categories with distinct reliability profiles: **Direct Scoring**: A single LLM rates one response on a defined scale. - Best for: Objective criteria (factual accuracy, instruction following, toxicity) - Reliability: Moderate to high for well-defined criteria - Failure mode: Score calibration drift, inconsistent scale interpretation **Pairwise Comparison**: An LLM compares two responses and selects the better one. -