Measuring RAG groundedness: a complete evaluation guide for February 2026

Groundedness in Retrieval-Augmented Generation (RAG) systems is a critical evaluation metric that ensures each factual claim in an output traces back to the retrieved source documents, preventing the generation of authoritative-sounding but fabricated details. This evaluation involves decomposing responses into testable claims and using a language model as a judge to score groundedness, achieving up to 80% agreement with human evaluators. Groundedness is part of the RAG triad, alongside context relevance and answer relevance, each addressing distinct failure modes. For critical domains, setting groundedness thresholds above 0.85 is essential, with continuous monitoring to catch regressions in quality. Optimizing retrieval by focusing on document relevance and context utilization can reduce the opportunities for hallucination, and automated testing in production environments helps maintain high groundedness scores, blocking deployments when scores fall below acceptable levels. Integrating these evaluations into CI/CD processes is vital for ensuring that RAG systems generate trustworthy and reliable outputs that adhere strictly to the retrieved evidence.

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