production-evals-framework

Build a production-grade LLM evaluation system by defining systems under test, creating golden datasets, running replay analysis, and applying rubric scoring to ensure baseline correctness and reliability.

Set up automated testing for LLM applications by establishing an end-to-end evals stack that uses labeled scenarios and experiment comparisons to continuously check accuracy and relevance against benchmarks.

Rubric scoring for LLM benchmarking works by applying predefined criteria to model outputs, enabling multi-dimensional quality assessment and comparative performance reporting across different experiment configurations.

Yes, you can use replay analysis for continuous LLM quality assurance by capturing production traces and re-evaluating them against golden sets to map scenario coverage and detect regressions.

Yes, a golden dataset is required to evaluate LLM agents as it provides the baseline correctness needed to measure system performance, map scenario coverage, and validate trace-driven analysis.

An end-to-end LLM evaluation framework includes system definition, golden datasets, scenario coverage mapping, replay harnesses, rubric scoring, and experiment configuration to establish comprehensive operating cadences.