RAGAS

RAGAS, short for Retrieval-Augmented Generation Assessment, is an evaluation framework specifically designed for RAG pipelines, released as an open-source library in 2023 and adopted by many enterprise RAG deployments. RAGAS computes metrics that decompose RAG quality into specific failure modes: faithfulness (does the answer match the retrieved context), answer relevancy (does the answer address the question), context precision (are retrieved chunks relevant), context recall (did retrieval find the needed information), and answer correctness (does the answer match the ground truth when available). The framework uses LLMs as judges to compute most metrics, which makes evaluation cheap and automatable but introduces LLM-judge biases. RAGAS pairs naturally with RAG frameworks like LangChain, LlamaIndex, and Haystack through built-in integrations. AI governance teams adopt RAGAS for continuous evaluation of production RAG systems, particularly for monitoring hallucination rates (low faithfulness) and retrieval quality. The framework is hosted at github.com/explodinggradients/ragas under Apache 2.0 license. Newer alternatives include TruLens, DeepEval, and Phoenix from Arize.

RAGAS-evaluated pipelines with Centralpoint: Centralpoint integrates with RAGAS and similar frameworks to validate RAG quality across whichever LLM and embedding model you use. Tokens are metered per skill, prompts stay local, and validated chatbots deploy through one line of JavaScript with audit-ready governance.


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