Data as of Sep 26, 2026 · Based on 4,029,442 AI responses across 13,338 prompts · See how Parse measures this
Amazon Sciencegithub.comRAGChecker is an advanced automatic evaluation framework designed to assess and diagnose Retrieval-Augmented Generation (RAG) systems. It provides a holistic set of metrics, including diagnostic retriever and generator metrics and fine-grained claim-level entailment evaluation to pinpoint specific weaknesses. It includes a benchmark dataset of 4k questions across 10 domains (upcoming) and a meta-evaluation dataset to correlate results with human judgments, enabling researchers to thoroughly evaluate and improve RAG systems.
The market map · 5 of 100 labelled
LLM Observability and Evaluation Platforms →Where Amazon Science ranks in AI
BraintrustPicked instead4 questions
DeepEvalPicked instead3 questions
Arize PhoenixPicked instead3 questions
RagasPicked instead3 questions
LangSmithPicked instead2 questionsNo contexts measured yet.
