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.
AI Tools in LLM Evaluations (6)
LM Arena
just nowWeb platform for comparing, running, and deploying large language models with hosted inference and API access.
Scale AI
11dScale AI provides enterprise-grade data labeling, model evaluation, RLHF, and a GenAI Data Engine with API and SDKs to build, fine-tune, and deploy production AI systems.
Confident AI
14dEnd-to-end platform for LLM evaluation and observability that benchmarks, tests, monitors, and traces LLM applications to prevent regressions and optimize performance.
Galileo
14dEnd-to-end platform for generative AI evaluation, observability, and real-time protection that helps teams test, monitor, and guard production AI applications.
Patronus AI
14dAutomated evaluation and monitoring platform that scores, detects failures, and optimizes LLMs and AI agents using evaluation models, experiments, traces, and an API/SDK ecosystem.
Mastra
19dA TypeScript-first AI agent framework and cloud platform for building, orchestrating, and observing production AI agents and workflows.
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