AI Topic: LLM Evaluations

Platforms and frameworks for evaluating, testing, and benchmarking LLM systems and AI applications. These tools provide evaluators and evaluation models to score AI outputs, measure hallucinations, assess RAG quality, detect failures, and optimize model performance. Features include automated testing with LLM-as-a-judge metrics, component-level evaluation with tracing, regression testing in CI/CD pipelines, custom evaluator creation, dataset curation, and real-time monitoring of production systems. Teams use these solutions to validate prompt effectiveness, compare models side-by-side, ensure answer correctness and relevance, identify bias and toxicity, prevent PII leakage, and continuously improve AI product quality through experiments, benchmarks, and performance analytics.