evaluation-harness

CI-driven LLM evaluation detects prompt regressions by running a test harness against golden datasets on every commit. It executes scoring rubrics and compares current results against saved baselines to automatically flag quality drops.

A golden dataset for AI model benchmarking is a standardized JSON structure containing test inputs, expected outputs, scoring rubrics, and metadata. It provides the reproducible ground truth needed to systematically evaluate model quality and track regressions.

Automate LLM scoring by applying exact-match, semantic similarity, or rubric-based scoring algorithms to expected outputs. An automated test runner executes these scoring methods across your golden datasets to generate quantitative evaluation metrics.

You can compare baseline vs current results for AI regression testing by generating regression reports. The evaluation harness executes your datasets, scores the outputs, and produces a comparative report highlighting performance shifts between model versions.

The evaluation harness requires no external dependencies or components to function. It operates as a standalone framework, allowing you to directly implement golden datasets, scoring rubrics, and regression reporting within your existing environment.

The best way to build a repeatable AI model evaluation pipeline is to standardize golden datasets, define scoring rubrics, establish pass/fail thresholds, and automate the test runner. This ensures reproducible benchmarking and systematic quality assurance.