LLM Evaluation Framework

LLM Evaluation Framework You can't improve what you can't measure. This framework gives you automated, repeatable evaluation harnesses for LLM outputs — with built-in metrics for accuracy, relevance, coherence, and safety, plus custom metric support. Run evaluations in CI/CD, track quality over time, compare models head-to-head, and catch regressions before they reach production. Key Features Automated Eval Harnesses — Define test suites as YAML, run them against any model, and get structured scores with statistical significance testing Built-In Metrics — Accuracy, relevance, coherence, faithfulness, toxicity, and latency measured out of the box Custom Metrics — Define your own scoring functions (Python callables) and plug them into the evaluation pipeline Human Feedback Collection — Web-based annotation interface for side-by-side comparisons, Likert scales, and free-text feedback Regression Testing — Compare current model outputs against a golden baseline and flag any score drops exce