advanced-evaluation

What problem does it solve?

Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, comparing model outputs, or establishing quality standards for AI-generated content.

Core Features & Use Cases

• Direct Scoring: Structured, rubric-driven scoring of a single response on objective criteria.
• Pairwise Comparison: Relative quality judgments between two responses with bias-mitigation protocols.
• Rubric Generation: Domain-specific scoring rubrics to reduce variance and improve reliability.
• Bias Mitigation: Techniques to counter position, length, self-enhancement, verbosity, and authority biases.
• Evaluation Pipeline Design: End-to-end architecture for scalable evaluation.

Quick Start

Run the included evaluation_example.py script to see examples of direct scoring and pairwise comparison, and consult the references for bias mitigation patterns. Then plug in your own prompts and responses to bootstrap an automated evaluation pipeline.