The RAG-enhanced assistant achieves context recall above 0.93 and answer relevance of 0.87 on an expert-built 52-question evaluation set¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? Evaluated with the RAGAS framework on 52 expert-developed question-answer pairs, the system retrieved sufficient clinical content (context recall above 0.93) and generated responses closely aligned with query intent (answer relevance 0.87). → Ronghua Xu 2026

Evidence¶

Ronghua Xu 2026¶

Ronghua Xu, Kepha Barasa, Manoj Kumal, Xinyun Liu, Weihua Zhou, Xin Qian. (2026). Agentic AI-driven Immersive Simulation: A Knowledge-Aware Virtual Training Platform for High Dose Rate (HDR) Brachytherapy. arXiv. https://arxiv.org/abs/2608.08163 doi:10.48550/arxiv.2608.08163

q2 · i? · design · r2

RAGAS evaluation of the prototype's RAG assistant on 52 question-answer pairs developed by medical professionals, categorized as basic, medium, and advanced. The article reports context recall "above 0.93" and separately an answer relevance score of 0.87; answer completeness was consistently high across configurations.

"We found that recall successfully captured the required information to answer the question, with a score above 0.93 showing a strong retrieval capacity for retrieving sufficient clinical content."

Discussion¶

Related Claims¶

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• Research-question responses score higher Answer Relevancy (0.87) than follow-ups (0.72), while Faithfulness, Context Relevance and Response Groundedness are lower — related