llm-eval-grounded-theory

To build an LLM evaluation pipeline, you automate stages from trace collection to calibration and continuous monitoring. This process includes qualitative error analysis, rubric design, and offline regression testing for robust AI system explainability.

LLM calibration is the process of aligning LLM-as-judge outputs against gold sets. It works by evaluating the judge's performance on known data, ensuring accurate quantitative scoring and reliable continuous monitoring in production environments.

You convert qualitative error analysis into quantitative LLM evaluation by applying open coding and axial coding methodologies. This structured approach allows you to design scoring rubrics that translate observed errors into measurable performance metrics.

Yes, you can use Python scripts for AI explainability in regulated environments. The framework provides processing and reporting tools specifically designed to ensure robust evaluation and compliance monitoring for AI systems.

The best way to detect online drift in LLM systems is through continuous monitoring pipelines. By calibrating your LLM judge against a gold set and tracking production outputs, you can automatically identify performance regressions and drift.

You need offline regression testing for your LLM-as-judge pipeline to maintain trustworthiness over time. It validates that updates or changes to the model do not degrade evaluation accuracy and explainability before production deployment.