advanced-evaluation

You can build an LLM evaluation pipeline by providing a sample prompt and model outputs to automate direct scoring and pairwise comparison. The pipeline generates structured scores, rubric diagnostics, and bias checks for production-quality QA.

LLM-as-a-judge bias mitigation detects and calibrates scoring biases during automated evaluations. It works by applying bias checks and confidence diagnostics to evaluation pipelines, ensuring consistent and trustworthy scoring across different model outputs.

Yes, you can generate custom rubrics for evaluating LLM outputs automatically. The pipeline creates rubric diagnostics alongside direct scoring and pairwise comparison results to improve reliability across various evaluation tasks.

Yes, the evaluation pipeline outputs structured JSON results for production-grade QA. It includes automated direct scoring, pairwise comparison data, rubric diagnostics, bias checks, and confidence calibration to ensure robust edge-case handling.

You calibrate confidence scores by running model outputs through the evaluation pipeline. It applies confidence calibration and bias diagnostics alongside direct scoring or pairwise comparison to produce trustworthy, production-quality results.