arXiv:2605.26476v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become critical for knowledge-intensive applications, yet evaluating its performance in vertical domains remains difficult due to domain complexity, diverse context scales, and heavy reliance on expert assessments that are costly, inconsistent, and non-scalable. We introduce FAB-Bench, an end-to-end framework for adaptive benchmarking of RAG systems in semiconductor manufacturing. FAB-Bench defines six diagnostic metrics measuring factual accuracy, contextual utilization, completeness, retrieval relevance,
The proliferation of RAG systems in critical industrial applications makes robust and standardized evaluation frameworks essential to ensure reliability and adoption.
A standardized benchmarking framework for RAG in semiconductor manufacturing addresses a key hurdle for AI adoption in a globally critical industry, promising more reliable and efficient integration of AI.
The introduction of FAB-Bench provides a structured and scalable method for evaluating RAG systems, potentially accelerating their deployment in complex industrial settings and reducing reliance on costly manual expert assessments.
- · Semiconductor manufacturers
- · AI developers
- · Semiconductor industry
- · Companies relying on ad-hoc RAG evaluation
- · Expert consultants for RAG validation
FAB-Bench will enable more efficient development and deployment of RAG systems tailored for semiconductor manufacturing.
Increased adoption of RAG could lead to optimized production processes and faster innovation cycles within the semiconductor industry.
Improved efficiency in semiconductor manufacturing could contribute to broader advancements in AI and compute supply chains by reducing costs and accelerating chip development.
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Read at arXiv cs.CL