ai-evaluation

Automate LLM evaluation workflows for CI/CD by applying layered architectures with deterministic checks, LLM-based judging, and human escalation to ensure reproducible benchmarking and safety assessment.

Set up regression testing for AI agents by applying cross-reference rules and rubric-based scoring templates, which enable repeatable calibration and benchmarking as models evolve.

Rubric-based scoring for benchmarking AI models works by applying standardized cross-reference rules and calibration patterns to evaluate agent performance consistently across established references.

You can run adversarial red-team testing using standardized evaluation harnesses that encode cross-framework rules for safety assessment and benchmarking across AI agents.

No specific dependencies are required to reproduce A/B testing results for LLMs; the evaluation harness supplies reusable templates and references to standardize calibration tasks.